Food and interaction design [Extended Abstract]

Food and interaction design presents an interesting challenge to the HCI community in attending to the pervasive nature of food, the socio-cultural differences in food practices and a changing global foodscape. To design for meaningful and positive interactions it is essential to identify daily food practices and the opportunities for the design of technology to support such practices. This workshop brings together a community of researchers and practitioners in human-food interaction to attend to the practical and theoretical difficulties in designing for human-food interactions in everyday life. Through a practical field study and workshop we explore themes of food experiences, health and wellbeing, sustainability and alternative food cultures.

Assessment of flood-related recharge to alluvial aquifers of an irrigated catchment following extended drought conditions using 3D visualisation and environmental tracers, Lockyer Valley, southeast Queensland, Australia.

The Lockyer Valley in southeast Queensland, Australia, hosts an economically significant alluvial aquifer system which has been impacted by prolonged drought conditions (~1997 to ~ 2009). Throughout this time, the system was under continued groundwater extraction, resulting in severe aquifer depletion. By 2008, much of the aquifer was at <30% of storage but some relief occurred with rains in early 2009. However, between December 2010 and January 2011, most of southeast Queensland experienced unprecedented flooding, which generated significant aquifer recharge. In order to understand the spatial and temporal controls of groundwater recharge in the alluvium, a detailed 3D lithological property model of gravels, sands and clays was developed using GOCAD software. The spatial distribution of recharge throughout the catchment was assessed using hydrograph data from about 400 groundwater observation wells screened at the base of the alluvium. Water levels from these bores were integrated into a catchment-wide 3D geological model using the 3D geological modelling software GOCAD; the model highlights the complexity of recharge mechanisms. To support this analysis, groundwater tracers (e.g. major and minor ions, stable isotopes, 3H and 14C) were used as independent verification. The use of these complementary methods has allowed the identification of zones where alluvial recharge primarily occurs from stream water during episodic flood events. However, the study also demonstrates that in some sections of the alluvium, rainfall recharge and discharge from the underlying basement into the alluvium are the primary recharge mechanisms of the alluvium. This is indicated by the absence of any response to the flood, as well as the observed old radiocarbon ages and distinct basement water chemistry signatures at these locations. Within the 3D geological model, integration of water chemistry and time-series displays of water level surfaces before and after the flood suggests that the spatial variations of the flood response in the alluvium are primarily controlled by the valley morphology and lithological variations within the alluvium. The integration of time-series of groundwater level surfaces in the 3D geological model also enables the quantification of the volumetric change of groundwater stored in the unconfined sections of this alluvial aquifer during drought and following flood events. The 3D representation and analysis of hydraulic and recharge information has considerable advantages over the traditional 2D approach. For example, while many studies focus on singular aspects of catchment dynamics and groundwater-surface water interactions, the 3D approach is capable of integrating multiple types of information (topography, geological, hydraulic, water chemistry and spatial) into a single representation which provides valuable insights into the major factors controlling aquifer processes.

Bending and blurring blended learning boundaries [Extended Abstract]

This presentation presents a blended learning model that provides greater opportunity for learning to be self-managed and personalized.

Probability analysis of machine angle stability with non-gaussian wind power input

The security of power transfer across a given transmission link is typically a steady state assessment. This paper develops tools to assess machine angle stability as affected by a combination of faults and uncertainty of wind power using probability analysis. The paper elaborates on the development of the theoretical assessment tool and demonstrates its efficacy using single machine infinite bus system.

An extended theory of planned behavior intervention for older adults With Type 2 diabetes and cardiovascular disease

A randomized controlled trial evaluated the effectiveness of a 4-wk extended theory of planned behavior (TPB) intervention to promote regular physical activity and healthy eating among older adults diagnosed with Type 2 diabetes or cardiovascular disease (N = 183). Participants completed TPB measures of attitude, subjective norm, perceived behavioral control, and intention, as well as planning and behavior, at preintervention and 1 wk and 6 wk postintervention for each behavior. No significant time-by-condition effects emerged for healthy eating. For physical activity, significant time-by-condition effects were found for behavior, intention, planning, perceived behavioral control, and subjective norm. In particular, compared with control participants, the intervention group showed short-term improvements in physical activity and planning, with further analyses indicating that the effect of the intervention on behavior was mediated by planning. The results indicate that TPB-based interventions including planning strategies may encourage physical activity among older people with diabetes and cardiovascular disease.

The role of mindfulness in an extended TPB framework to understand drivers’ speeding intentions in school zones.

Many researchers have demonstrated the applicability of the Theory of Planned Behaviour (TPB) in predicting both intention to speed and actual speeding behaviour. However, there remain shortcomings in the explanatory power of the TPB, with research suggesting that even when drivers had reported an intention to not speed approximately 25% of drivers report behaviour that does not align with their intentions (i.e., they engaged in speeding, Elliott & Armitage, 2006). This research explores the role of a novel and promising construct, mindfulness, in enhancing the explanatory utility of the TPB for the understanding of drivers’ speeding behaviour in school zones. Mindfulness is a concept which has been widely used in studies of consciousness, but has recently been applied to the understanding of behaviour in other areas, including clinical psychology, physical activity, education and business. It has been suggested that mindfulness can also be applied to road safety, though its application within this context currently remains limited. This study was based on an e-survey of the general driving public (N=240). Overall, the results identified mindfulness as a construct which may aid understanding of the relationship between drivers’ intentions and behaviour. Theoretically, the findings may have implications in terms of identifying mindfulness as an additional explanatory construct within a TPB framework. In road safety practice, the findings suggest that efficacious countermeasures around school zones may be those that function to heighten drivers’ mindfulness, such as flashing lights and physical speed reduction measures.

Emotional arousal of beginning physics teachers during extended experimental investigations

Teachers often have difficulty implementing inquiry-based activities, leading to the arousal of negative emotions. In this multicase study of beginning physics teachers in Australia, we were interested in the extent to which their expectations were realized and how their classroom experiences while implementing extended experimental investigations (EEIs) produced emotional states that mediated their teaching practices. Against rhetoric of fear expressed by their senior colleagues, three of the four teachers were surprised by the positive outcomes from their supervision of EEIs for the first time. Two of these teachers experienced high intensity positive emotions in response to their students’ success. When student actions / outcomes did not meet their teachers’ expectations, frustration, anger, and disappointment were experienced by the teachers, as predicted by a sociological theory of human emotions (Turner, 2007). Over the course of the EEI projects, the teachers’ practices changed along with their emotional states and their students’ achievements. We account for similarities and differences in the teachers’ emotional experiences in terms of context, prior experience, and expectations. The findings from this study provide insights into effective supervision practices that can be used to inform new and experienced teachers alike.

How to design extended finite state machine test models in Java

This chapter is a tutorial that teaches you how to design extended finite state machine (EFSM) test models for a system that you want to test. EFSM models are more powerful and expressive than simple finite state machine (FSM) models, and are one of the most commonly used styles of models for model-based testing, especially for embedded systems. There are many languages and notations in use for writing EFSM models, but in this tutorial we write our EFSM models in the familiar Java programming language. To generate tests from these EFSM models we use ModelJUnit, which is an open-source tool that supports several stochastic test generation algorithms, and we also show how to write your own model-based testing tool. We show how EFSM models can be used for unit testing and system testing of embedded systems, and for offline testing as well as online testing.

High strong order methods for non-commutative stochastic ordinary differential equation systems and the Magnus formula

In recent years considerable attention has been paid to the numerical solution of stochastic ordinary differential equations (SODEs), as SODEs are often more appropriate than their deterministic counterparts in many modelling situations. However, unlike the deterministic case numerical methods for SODEs are considerably less sophisticated due to the difficulty in representing the (possibly large number of) random variable approximations to the stochastic integrals. Although Burrage and Burrage [High strong order explicit Runge-Kutta methods for stochastic ordinary differential equations, Applied Numerical Mathematics 22 (1996) 81-101] were able to construct strong local order 1.5 stochastic Runge-Kutta methods for certain cases, it is known that all extant stochastic Runge-Kutta methods suffer an order reduction down to strong order 0.5 if there is non-commutativity between the functions associated with the multiple Wiener processes. This order reduction down to that of the Euler-Maruyama method imposes severe difficulties in obtaining meaningful solutions in a reasonable time frame and this paper attempts to circumvent these difficulties by some new techniques. An additional difficulty in solving SODEs arises even in the Linear case since it is not possible to write the solution analytically in terms of matrix exponentials unless there is a commutativity property between the functions associated with the multiple Wiener processes. Thus in this present paper first the work of Magnus [On the exponential solution of differential equations for a linear operator, Communications on Pure and Applied Mathematics 7 (1954) 649-673] (applied to deterministic non-commutative Linear problems) will be applied to non-commutative linear SODEs and methods of strong order 1.5 for arbitrary, linear, non-commutative SODE systems will be constructed - hence giving an accurate approximation to the general linear problem. Secondly, for general nonlinear non-commutative systems with an arbitrary number (d) of Wiener processes it is shown that strong local order I Runge-Kutta methods with d + 1 stages can be constructed by evaluated a set of Lie brackets as well as the standard function evaluations. A method is then constructed which can be efficiently implemented in a parallel environment for this arbitrary number of Wiener processes. Finally some numerical results are presented which illustrate the efficacy of these approaches. (C) 1999 Elsevier Science B.V. All rights reserved.

Early adolescents’ helping behaviours when a friend is injured in a fight : the role of an extended Theory of Planned Behavior

This paper examines the role of first aid training in increasing adolescent helping behaviours when taught in a school-based injury prevention program, Skills for Preventing Injury in Youth (SPIY). The research involved the development and application of an extended Theory of Planned Behaviour (TPB), including “behavioural willingness in a fight situation,” “first aid knowledge” and “perceptions of injury seriousness”, to predict the relationship between participation in SPIY and helping behaviours when a friend is injured in a fight. From 35 Queensland high schools, 2500 Year 9 students (mean age = 13.5, 40% male) completed surveys measuring their attitudes, perceived behavioural control, subjective norms and behavioural intention, from the TPB, and added measures of behavioural willingness in a fight situation, perceptions of injury seriousness and first aid knowledge, to predict helping behaviours when a friend is injured in a fight. It is expected that the TPB will significantly contribute to understanding the relationship between participation in SPIY and helping behaviours when a friend is injured in a fight. Further analyses will determine whether the extension of the model significantly increases the variance explained in helping behaviours. The findings of this research will provide insight into the critical factors that may increase adolescent bystanders’ actions in injury situations.

Exploring the validity and predictive power of an extended volunteer functions inventory within the context of episodic skilled volunteering by retirees

The current study examined the structure of the volunteer functions inventory within a sample of older individuals (N = 187). The career items were replaced with items examining the concept of continuity of work, a potentially more useful and relevant concept for this population. Factor analysis supported a four factor solution, with values, social and continuity emerging as single factors and enhancement and protective items loading together on a single factor. Understanding items did not load highly on any factor. The values and continuity functions were the only dimensions to emerge as predictors of intention to volunteer. This research has important implications for understanding the motivation of older adults to engage in contemporary volunteering settings.

Speaker diarization : "who spoke when"

Speaker diarization is the process of annotating an input audio with information that attributes temporal regions of the audio signal to their respective sources, which may include both speech and non-speech events. For speech regions, the diarization system also specifies the locations of speaker boundaries and assign relative speaker labels to each homogeneous segment of speech. In short, speaker diarization systems effectively answer the question of ‘who spoke when’. There are several important applications for speaker diarization technology, such as facilitating speaker indexing systems to allow users to directly access the relevant segments of interest within a given audio, and assisting with other downstream processes such as summarizing and parsing. When combined with automatic speech recognition (ASR) systems, the metadata extracted from a speaker diarization system can provide complementary information for ASR transcripts including the location of speaker turns and relative speaker segment labels, making the transcripts more readable. Speaker diarization output can also be used to localize the instances of specific speakers to pool data for model adaptation, which in turn boosts transcription accuracies. Speaker diarization therefore plays an important role as a preliminary step in automatic transcription of audio data. The aim of this work is to improve the usefulness and practicality of speaker diarization technology, through the reduction of diarization error rates. In particular, this research is focused on the segmentation and clustering stages within a diarization system. Although particular emphasis is placed on the broadcast news audio domain and systems developed throughout this work are also trained and tested on broadcast news data, the techniques proposed in this dissertation are also applicable to other domains including telephone conversations and meetings audio. Three main research themes were pursued: heuristic rules for speaker segmentation, modelling uncertainty in speaker model estimates, and modelling uncertainty in eigenvoice speaker modelling. The use of heuristic approaches for the speaker segmentation task was first investigated, with emphasis placed on minimizing missed boundary detections. A set of heuristic rules was proposed, to govern the detection and heuristic selection of candidate speaker segment boundaries. A second pass, using the same heuristic algorithm with a smaller window, was also proposed with the aim of improving detection of boundaries around short speaker segments. Compared to single threshold based methods, the proposed heuristic approach was shown to provide improved segmentation performance, leading to a reduction in the overall diarization error rate. Methods to model the uncertainty in speaker model estimates were developed, to address the difficulties associated with making segmentation and clustering decisions with limited data in the speaker segments. The Bayes factor, derived specifically for multivariate Gaussian speaker modelling, was introduced to account for the uncertainty of the speaker model estimates. The use of the Bayes factor also enabled the incorporation of prior information regarding the audio to aid segmentation and clustering decisions. The idea of modelling uncertainty in speaker model estimates was also extended to the eigenvoice speaker modelling framework for the speaker clustering task. Building on the application of Bayesian approaches to the speaker diarization problem, the proposed approach takes into account the uncertainty associated with the explicit estimation of the speaker factors. The proposed decision criteria, based on Bayesian theory, was shown to generally outperform their non- Bayesian counterparts.