Street computing : towards an integrated open data Application Programming Interface (API) for cities

This special issue of the Journal of Urban Technology brings together five articles that are based on presentations given at the Street Computing workshop held on 24 November 2009 in Melbourne in conjunction with the Australian Computer-Human Interaction conference (OZCHI 2009). Our own article introduces the Street Computing vision and explores the potential, challenges and foundations of this research vision. In order to do so, we first look at the currently available sources of information and discuss their link to existing research efforts. Section 2 then introduces the notion of Street Computing and our research approach in more detail. Section 3 looks beyond the core concept itself and summarises related work in this field of interest.

Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data

In this paper we present a sequential Monte Carlo algorithm for Bayesian sequential experimental design applied to generalised non-linear models for discrete data. The approach is computationally convenient in that the information of newly observed data can be incorporated through a simple re-weighting step. We also consider a flexible parametric model for the stimulus-response relationship together with a newly developed hybrid design utility that can produce more robust estimates of the target stimulus in the presence of substantial model and parameter uncertainty. The algorithm is applied to hypothetical clinical trial or bioassay scenarios. In the discussion, potential generalisations of the algorithm are suggested to possibly extend its applicability to a wide variety of scenarios

Perceptions of data quality dimensions and data roles

Several authors stress the importance of data’s crucial foundation for operational, tactical and strategic decisions (e.g., Redman 1998, Tee et al. 2007). Data provides the basis for decision making as data collection and processing is typically associated with reducing uncertainty in order to make more effective decisions (Daft and Lengel 1986). While the first series of investments of Information Systems/Information Technology (IS/IT) into organizations improved data collection, restricted computational capacity and limited processing power created challenges (Simon 1960). Fifty years on, capacity and processing problems are increasingly less relevant; in fact, the opposite exists. Determining data relevance and usefulness is complicated by increased data capture and storage capacity, as well as continual improvements in information processing capability. As the IT landscape changes, businesses are inundated with ever-increasing volumes of data from both internal and external sources available on both an ad-hoc and real-time basis. More data, however, does not necessarily translate into more effective and efficient organizations, nor does it increase the likelihood of better or timelier decisions. This raises questions about what data managers require to assist their decision making processes.

The social orders of family mealtime

This study examined the everyday practices of families within the context of family mealtime to investigate how members accomplished mealtime interactions. Using an ethnomethodological approach, conversation analysis and membership categorization analysis, the study investigated the interactional resources that family members used to assemble their social orders moment by moment during family mealtimes. While there is interest in mealtimes within educational policy, health research and the media, there remain few studies that provide fine-grained detail about how members produce the social activity of having a family meal. Findings from this study contribute empirical understandings about families and family mealtime. Two families with children aged 2 to 10 years were observed as they accomplished their everyday mealtime activities. Data collection took place in the family homes where family members video recorded their naturally occurring mealtimes. Each family was provided with a video camera for a one-month period and they decided which mealtimes they recorded, a method that afforded participants greater agency in the data collection process and made available to the analyst a window into the unfolding of the everyday lives of the families. A total of 14 mealtimes across the two families were recorded, capturing 347 minutes of mealtime interactions. Selected episodes from the data corpus, which includes centralised breakfast and dinnertime episodes, were transcribed using the Jeffersonian system. Three data chapters examine extended sequences of family talk at mealtimes, to show the interactional resources used by members during mealtime interactions. The first data chapter explores multiparty talk to show how the uniqueness of the occasion of having a meal influences turn design. It investigates the ways in which members accomplish two-party talk within a multiparty setting, showing how one child "tells" a funny story to accomplish the drawing together of his brothers as an audience. As well, this chapter identifies the interactional resources used by the mother to cohort her children to accomplish the choralling of grace. The second data chapter draws on sequential and categorical analysis to show how members are mapped to a locally produced membership category. The chapter shows how the mapping of members into particular categories is consequential for social order; for example, aligning members who belong to the membership category "had haircuts" and keeping out those who "did not have haircuts". Additional interactional resources such as echoing, used here to refer to the use of exactly the same words, similar prosody and physical action, and increasing physical closeness, are identified as important to the unfolding talk particularly as a way of accomplishing alignment between the grandmother and grand-daughter. The third and final data analysis chapter examines topical talk during family mealtimes. It explicates how members introduce topics of talk with an orientation to their co-participant and the way in which the take up of a topic is influenced both by the sequential environment in which it is introduced and the sensitivity of the topic. Together, these three data chapters show aspects of how family members participated in family mealtimes. The study contributes four substantive themes that emerged during the analytic process and, as such, the themes reflect what the members were observed to be doing. The first theme identified how family knowledge was relevant and consequential for initiating and sustaining interaction during mealtime with, for example, members buying into the talk of other members or being requested to help out with knowledge about a shared experience. Knowledge about members and their activities was evident with the design of questions evidencing an orientation to coparticipant’s knowledge. The second theme found how members used topic as a resource for social interaction. The third theme concerned the way in which members utilised membership categories for producing and making sense of social action. The fourth theme, evident across all episodes selected for analysis, showed how children’s competence is an ongoing interactional accomplishment as they manipulated interactional resources to manage their participation in family mealtime. The way in which children initiated interactions challenges previous understandings about children’s restricted rights as conversationalists. As well as making a theoretical contribution, the study offers methodological insight by working with families as research participants. The study shows the procedures involved as the study moved from one where the researcher undertook the decisions about what to videorecord to offering this decision making to the families, who chose when and what to videorecord of their mealtime practices. Evident also are the ways in which participants orient both to the video-camera and to the absent researcher. For the duration of the mealtime the video-camera was positioned by the adults as out of bounds to the children; however, it was offered as a "treat" to view after the mealtime was recorded. While situated within family mealtimes and reporting on the experiences of two families, this study illuminates how mealtimes are not just about food and eating; they are social. The study showed the constant and complex work of establishing and maintaining social orders and the rich array of interactional resources that members draw on during family mealtimes. The family’s interactions involved members contributing to building the social orders of family mealtime. With mealtimes occurring in institutional settings involving young children, such as long day care centres and kindergartens, the findings of this study may help educators working with young children to see the rich interactional opportunities mealtimes afford children, the interactional competence that children demonstrate during mealtimes, and the important role/s that adults may assume as co-participants in interactions with children within institutional settings.

Analysing mentoring practices to understand how a preservice teacher reflects on practice to advance teaching

Mentors (cooperating classroom teachers) have a shared responsibility with universities for developing preservice teachers’ pedagogical practices, particularly towards becoming reflective practitioners. Preservice teachers need to participate actively in their own learning, by reflecting and acting on the mentor’s constructive feedback provided during planning and feedback dialogue sessions. This case study uses feedback practices outlined within a five-factor mentoring model to analyse dialogue between a mentor and her respective mentee during different stages in their school-based programs (first practicum). This investigation uses multiple data sources such as video and audio-recorded interviews, archival documents from participants such as lesson plans, reflections and reports to examine preservice teacher’s reflections and implementations of practice as a result of her mentor’s feedback (e.g., establish expectations, review lesson plans, observe teaching then provide oral and written feedback, and evaluate progress). Findings indicated that reflective thinking was more apparent when the mentor did not dominate conversations but instead asked astute pedagogical knowledge questions to facilitate the mentee’s reflections on practice.

Positive emotional responses to hybridised writing about a socio-scientific issue

In order to understand better the role of affect in learning about socio-scientificissues (SSI), this study investigated Year 12 students’ emotional arousal as they participated in an online writing-to-learn science project about the socio-scientific issue of biosecurity. Students wrote a series of hybridised scientific narratives, or BioStories, that integrate scientific information about biosecurity with narrative storylines, and uploaded these to a dedicated website. Throughout their participation in the project, students recorded their emotional responses to the various activities (N=50). Four case students were also video recorded during selected science lessons as they researched, composed and uploaded their BioStories for peer review. Analysis of these data, as well as interview data obtained from the case students, revealed that pride, strength, determination, interest and alertness were among the positive emotions most strongly elicited by the project. These emotions reflected students’ interest in learning about a new socio-scientific issue, and their enhanced feelings of self-efficacy in successfully writing hybridised scientific narratives in science. The results of this study suggest that the elicitation of positive emotional responses as students engage in hybridised writing about SSI with strong links to environmental education, such as biosecurity, can be valuable in engaging students in education for sustainability.

Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease

Mixture models are a flexible tool for unsupervised clustering that have found popularity in a vast array of research areas. In studies of medicine, the use of mixtures holds the potential to greatly enhance our understanding of patient responses through the identification of clinically meaningful clusters that, given the complexity of many data sources, may otherwise by intangible. Furthermore, when developed in the Bayesian framework, mixture models provide a natural means for capturing and propagating uncertainty in different aspects of a clustering solution, arguably resulting in richer analyses of the population under study. This thesis aims to investigate the use of Bayesian mixture models in analysing varied and detailed sources of patient information collected in the study of complex disease. The first aim of this thesis is to showcase the flexibility of mixture models in modelling markedly different types of data. In particular, we examine three common variants on the mixture model, namely, finite mixtures, Dirichlet Process mixtures and hidden Markov models. Beyond the development and application of these models to different sources of data, this thesis also focuses on modelling different aspects relating to uncertainty in clustering. Examples of clustering uncertainty considered are uncertainty in a patient’s true cluster membership and accounting for uncertainty in the true number of clusters present. Finally, this thesis aims to address and propose solutions to the task of comparing clustering solutions, whether this be comparing patients or observations assigned to different subgroups or comparing clustering solutions over multiple datasets. To address these aims, we consider a case study in Parkinson’s disease (PD), a complex and commonly diagnosed neurodegenerative disorder. In particular, two commonly collected sources of patient information are considered. The first source of data are on symptoms associated with PD, recorded using the Unified Parkinson’s Disease Rating Scale (UPDRS) and constitutes the first half of this thesis. The second half of this thesis is dedicated to the analysis of microelectrode recordings collected during Deep Brain Stimulation (DBS), a popular palliative treatment for advanced PD. Analysis of this second source of data centers on the problems of unsupervised detection and sorting of action potentials or "spikes" in recordings of multiple cell activity, providing valuable information on real time neural activity in the brain.

Top Ten Box Office Blockbusters of All Time

‘Top Ten Box Office Blockbusters in Dollars’, is an ongoing series of works that represent the production budgets and worldwide gross profits of the top ten grossing films of all time. By displaying this data on top of the full running time of each blockbuster, the viewer’s attention is drawn back and forth between the amassing dollar figures, and the original film’s highly polished presentation. In doing so, the work aims to provide a new opportunity to enjoy these immensely popular films with a new sense of value. The exhibition was selected for the Artistic Program at MetroArts, Brisbane in 2010

XML data clustering: An overview

In the last few years we have observed a proliferation of approaches for clustering XML docu- ments and schemas based on their structure and content. The presence of such a huge amount of approaches is due to the different applications requiring the XML data to be clustered. These applications need data in the form of similar contents, tags, paths, structures and semantics. In this paper, we first outline the application contexts in which clustering is useful, then we survey approaches so far proposed relying on the abstract representation of data (instances or schema), on the identified similarity measure, and on the clustering algorithm. This presentation leads to draw a taxonomy in which the current approaches can be classified and compared. We aim at introducing an integrated view that is useful when comparing XML data clustering approaches, when developing a new clustering algorithm, and when implementing an XML clustering compo- nent. Finally, the paper moves into the description of future trends and research issues that still need to be faced.

Modeling player-like behaviour for game Al design

The design of artificial intelligence in computer games is an important component of a player's game play experience. As games are becoming more life-like and interactive, the need for more realistic game AI will increase. This is particularly the case with respect to AI that simulates how human players act, behave and make decisions. The purpose of this research is to establish a model of player-like behavior that may be effectively used to inform the design of artificial intelligence to more accurately mimic a player's decision making process. The research uses a qualitative analysis of player opinions and reactions while playing a first person shooter video game, with recordings of their in game actions, speech and facial characteristics. The initial studies provide player data that has been used to design a model of how a player behaves.

Engaging parents in STEM: Coteaching and cogenerative dialoguing in a Queensland high school

Diminished student interest in science, technology, engineering and mathematics (STEM) is recognised by educators, researchers and public policy makers as a concerning global trend. Inviting stakeholders like scientists and industry specialists to discuss their work is one means schools use to facilitate student engagement in the sciences. However, these visits generally comprise one-off sessions with minimal relevance to students’ particular and ongoing learning needs. This case study investigated coteaching and cogenerative dialoguing with parents in teaching a Year-8 multidisciplinary unit with science and technology foci. Two parents cotaught alongside the resident teacher and researcher over eight months. This paper concentrates on one parent, a medical scientist by profession. Data sources included video and audio recordings of cogenerative dialogues and classroom interactions, student work samples and journal entries. Data were interrogated using the sociological constructs of fields and capitals and the dialectic of structure|agency. The findings reveal how (a) the parent’s science and technology knowledge was tailored to the students’ needs initially and continually and (b) student-generated data indicated enhanced engagement in science and technology. The research speaks to schools and governments about enhancing STEM education by furthering collaborative relationships with relevant stakeholders.

Data modelling with first-grade students

This paper argues for a renewed focus on statistical reasoning in the beginning school years, with opportunities for children to engage in data modelling. Results are reported from the first year of a 3-year longitudinal study in which three classes of first-grade children (6-year-olds) and their teachers engaged in data modelling activities. The theme of Looking after our Environment, part of the children’s science curriculum, provided the task context. The goals for the two activities addressed here included engaging children in core components of data modelling, namely, selecting attributes, structuring and representing data, identifying variation in data, and making predictions from given data. Results include the various ways in which children represented and re represented collected data, including attribute selection, and the metarepresentational competence they displayed in doing so. The “data lenses” through which the children dealt with informal inference (variation and prediction) are also reported.

A first stage test of Dunning's framework on multinational contracting into Australia : reviewing secondary data.

In response to the need to leverage private finance and the lack of competition in some parts of the Australian public sector infrastructure market, especially in the very large economic infrastructure sector procured using Pubic Private Partnerships, the Australian Federal government has demonstrated its desire to attract new sources of in-bound foreign direct investment (FDI). This paper aims to report on progress towards an investigation into the determinants of multinational contractors’ willingness to bid for Australian public sector major infrastructure projects. This research deploys Dunning’s eclectic theory for the first time in terms of in-bound FDI by multinational contractors into Australia. Elsewhere, the authors have developed Dunning’s principal hypothesis to suit the context of this research and to address a weakness arising in this hypothesis that is based on a nominal approach to the factors in Dunning's eclectic framework and which fails to speak to the relative explanatory power of these factors. In this paper, a first stage test of the authors' development of Dunning's hypothesis is presented by way of an initial review of secondary data vis-à-vis the selected sector (roads and bridges) in Australia (as the host location) and with respect to four selected home countries (China; Japan; Spain; and US). In doing so, the next stage in the research method concerning sampling and case studies is also further developed and described in this paper. In conclusion, the extent to which the initial review of secondary data suggests the relative importance of the factors in the eclectic framework is considered. It is noted that more robust conclusions are expected following the future planned stages of the research including primary data from the case studies and a global survey of the world’s largest contractors and which is briefly previewed. Finally, and beyond theoretical contributions expected from the overall approach taken to developing and testing Dunning’s framework, other expected contributions concerning research method and practical implications are mentioned.

Understanding user experience of mobile video : framework, measurement, and optimization

Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the user’s interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining users’ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account user’s needs and desires when using the service, emphasizing the user’s overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study.

Rule-based approach for identifying assertions in clinical free-text data

A rule-based approach for classifying previously identified medical concepts in the clinical free text into an assertion category is presented. There are six different categories of assertions for the task: Present, Absent, Possible, Conditional, Hypothetical and Not associated with the patient. The assertion classification algorithms were largely based on extending the popular NegEx and Context algorithms. In addition, a health based clinical terminology called SNOMED CT and other publicly available dictionaries were used to classify assertions, which did not fit the NegEx/Context model. The data for this task includes discharge summaries from Partners HealthCare and from Beth Israel Deaconess Medical Centre, as well as discharge summaries and progress notes from University of Pittsburgh Medical Centre. The set consists of 349 discharge reports, each with pairs of ground truth concept and assertion files for system development, and 477 reports for evaluation. The system’s performance on the evaluation data set was 0.83, 0.83 and 0.83 for recall, precision and F1-measure, respectively. Although the rule-based system shows promise, further improvements can be made by incorporating machine learning approaches.