131,660 research outputs found
Mcinerney, J D (John Darcy), NX44785
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/403502Surname: MCINERNEY. Given Name(s) or Initials: J D (JOHN DARCY). Military Service Number or Last Known Location: NX44785. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 36073.224328
Item: [2016.0049.35795] "Mcinerney, J D (John Darcy), NX44785
Learning periodic human behaviour models from sparse data for crowdsourcing aid delivery in developing countries
No full textIn many developing countries, half the population lives in rural locations, where access to essentials such as school materials, mosquito nets, and medical supplies is restricted. We propose an alternative method of distribution (to standard road delivery) in which the existing mobility habits of a local population are leveraged to deliver aid, which raises two technical challenges in the areas optimisation and learning. For optimisation, a standard Markov decision process applied to this problem is intractable, so we provide an exact formulation that takes advantage of the periodicities in human location behaviour. To learn such behaviour models from sparse data (i.e., cell tower observations), we develop a Bayesian model of human mobility. Using real cell tower data of the mobility behaviour of 50,000 individuals in Ivory Coast, we find that our model outperforms the state of the art approaches in mobility prediction by at least 25% (in held-out data likelihood). Furthermore, when incorporating mobility prediction with our MDP approach, we find a 81.3% reduction in total delivery time versus routine planning that minimises just the number of participants in the solution path
Me, my classroom, my school : a mixed methods approach to the meE Framework of motivation, engagement, and academic development
No full textThis chapter picks up on some of the critical findings and recommendations around the significance of mastery goal orientation and a sense of ‘purpose in schooling’ for student motivation and engagement in the work of McInerney. In particular, the chapter explores classroom approaches that are strongly aligned with McInerney’s extensive application of Personal Investment Theory, especially as it applies to Indigenous students in Australia (McInerney & Liem, 2009). With its emphasis on mixed methods, the chapter also offers a methodological extension on McInerney’s quantitative and qualitative research across both Personal Investment and Facilitating Conditions (McInerney, Dowson & Yeung, 2005)
Intelligent agents for mobile location services
No full textUnderstanding human mobility patterns is a significant research endeavour that has recently received considerable attention. Developing the science to describe and predict how people move from one place to another during their daily lives promises to address a wide range of societal challenges: from predicting the spread of infectious diseases, improving urban planning, to devising effective emergency response strategies. Individuals are also set to benefit from this area of research, as mobile devices will be able to analyse their mobility pattern and offer context-aware assistance and information. For example, a service could warn about travel disruptions before the user is likely to encounter them, or provide recommendations and mobile vouchers for local services that promise to be of high value to the user, based on their predicted future plans. More ambitiously, control systems for home heating and electric vehicle charging could be enhanced with knowledge of when the user will be home. In this thesis, we focus on such anticipatory computing. Some aspects of the vision of context-awareness have been pursued for many years, resulting in mature research in the area of ubiquitous systems. However, the combination of surprisingly rapid adoption of advanced mobile devices by consumers and the broad acceptance of location-based apps has surfaced not only new opportunities, but also a number of pressing challenges.In more detail, these challenges are the (i) prediction of future mobility, (ii) inference of features of human location behaviour, and (iii) use of prediction and inference to make decisions about timely information or control actions. Our research brings together, for the first time, the entire workflow that a mobile location service needs to follow, in order to achieve an understanding of mobile user needs and to act on such understanding effectively. This framing of the problem highlights the shortcomings of existing approaches which we seek to address. In the current literature, prediction is only considered for established users, which implicitly assumes that new users will continue to use an initially inaccurate prediction system long enough for it to improve and increase in accuracy over time. Additionally, inference of user behaviour is mostly concerned with interruptibility, which does not take into account the constructive role of intelligent location services that goes beyond simply avoiding interrupting the user at inopportune times (e.g., in a meeting, or while driving). Finally, no principled decision framework for intelligent location services has been provided that takes into account the results of prediction and inference.To address these shortcomings, we make three main contributions to the state of the art. Firstly, we provide a novel Bayesian model that relates the location behaviour of new and established users, allowing the reuse of structure learnt from rich mobility data. This model shows a factor of 2.4 improvement over the state-of-the-art baseline in heldout data likelihood in experiments using the Nokia Lausanne dataset. Secondly, we give new tools for the analysis and prediction of routine in mobility, which is a latent feature of human behaviour, that informs the service about the user’s availability to follow up on any information provided. And thirdly, we provide a fully worked example of an intelligent mobile location service (a crowdsourced package delivery service) that performs decision-making using predictive densities of current and future user mobility. Simulations using real mobility data from the Orange Ivory Coast dataset indicate a 81.3% improvement in service efficiency when compared with the next best (non-anticipatory) approach
Statistical emulation of climate model projections based on precomputed GCM runs
No full textAmerican Meteorological SocietyStefano Castruccio, David J. McInerney, Michael L. Stein, Feifei Liu Crouch, Robert L. Jacob, Elisabeth J. Moye
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Author Co-Citation Analysis (ACA): a powerful tool for representing implicit knowledge of scholar knowledge workers
No full textIn the last decade, knowledge has emerged as one of the most important and valuable organizational assets. Gradually this importance caused to emergence of new discipline entitled ―knowledge management‖. However one of the major challenges of knowledge management is conversion implicit or tacit knowledge to explicit knowledge. Thus Making knowledge visible so that it can be better accessed, discussed, valued or generally managed is a long-standing objective in knowledge management. Accordingly in this paper author co- citation analysis (ACA) will be proposed as an efficient technique of knowledge visualization in academia (Scholar knowledge workers)
Cell anatomy and leaf delta(13)C as proxies for shading and canopy structure in a Miocene forest from Ethiopia
No full textAbstract not availableRosemary T. Bush, Jon Wallace, Ellen D. Currano, Bonnie F. Jacobs, Francesca A. McInerney, Regan E. Dunn, Neil J. Tabo
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