Using job strain and organizational justice models to predict multiple forms of employee performance behaviours among Australian policing personnel

The overall purpose of this investigation was to examine the relationship between stress-related working conditions and three forms of employee performance behaviours: in-role behaviours, citizenship behaviours directed at other individuals and citizenship behaviours directed at the organization. The potentially stressful working conditions were based on the job strain model (incorporating job demands, job control and social support) as well as organizational justice theory. A sample of Australian-based police officers (n = 640) took part in this study and the data were collected via a mail-out survey. Multiple regression analyses were undertaken to assess both the strength and the nature of the relationships between the working conditions and employee performance and these analyses included tests for additive, interactional and curvilinear effects. The overall results indicated that a significant proportion of the explained variance in all three outcome measures was attributed to the additive effects of demand, control and support. The level of variance associated with the organizational justice dimensions was relatively small, although there were signs that specific dimensions of justice may provide unique insights into the relationship between job stressors and employee performance. The implications of these and other notable findings are discussed.

Policy recognition in the Abstract Hidden Markov Model

In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

Tracking and surveillance in wide-area spatial environments using the abstract hidden markov model.

In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Abstract Hidden Markov Models (AHMM), an extension of the well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.

Hierarchical monitoring of people's behaviors in complex environments using multiple cameras

We present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people, we propose the use of the Abstract Hidden Markov Model (AHMM), which can be considered as an extension of the Hidden Markov Model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackwellised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. The results of the system demonstrate the ability to answer queries about people's behaviors at different levels of details using multiple cameras in a large and complex indoor environment.

A Bayesian framework for learning shared and individual subspaces from multiple data sources

This paper presents a novel Bayesian formulation to exploit shared structures across multiple data sources, constructing foundations for effective mining and retrieval across disparate domains. We jointly analyze diverse data sources using a unifying piece of metadata (textual tags). We propose a method based on Bayesian Probabilistic Matrix Factorization (BPMF) which is able to explicitly model the partial knowledge common to the datasets using shared subspaces and the knowledge specific to each dataset using individual subspaces. For the proposed model, we derive an efficient algorithm for learning the joint factorization based on Gibbs sampling. The effectiveness of the model is demonstrated by social media retrieval tasks across single and multiple media. The proposed solution is applicable to a wider context, providing a formal framework suitable for exploiting individual as well as mutual knowledge present across heterogeneous data sources of many kinds.

Investigation of a prefetch model for low bandwidth networks

We investigate speculative prefetching under a model in which prefetching is neither aborted nor preempted by demand fetch but instead gets equal priority in network bandwidth utilisation. We argue that the non-abortive assumption is appropriate for wireless networks where bandwidth is low and latency is high, and the non-preemptive assumption is appropriate for Internet where prioritization is not always possible. This paper assumes the existence of an access model to provide some knowledge about future accesses and investigates analytically the performance of a prefetcher that utilises this knowledge. In mobile computing, because resources are severely constrained, performance prediction is as important as access prediction. For uniform retrieval time, we derive a theoretical limit of improvement in access time due to prefetching. This leads to the formulation of an optimal algorithrn for prefetching one access ahead. For non-uniform retrieval time, two different types of prefetching of multiple documents, namely mainline and branch prefetch, are evaluated against prefetch of single document. In mainline prefetch, the most probable sequence of future accesses is prefetched. In branch prefetch, a set of different alternatives for future accesses is prefetched. Under some conditions, mainline prefetch may give slight improvement in user-perceived access time over single prefetch with nominal extra retrieval cost, where retrieval cost is defined as the expected network time wasted in non-useful prefetch. Branch prefetch performs better than mainline prefetch but incurs more retrieval cost.

Presenting evidence-based health information for people with multiple sclerosis : the IN-DEEP project protocol

Background - Increasingly, evidence-based health information, in particular evidence from systematic reviews, is being made available to lay audiences, in addition to health professionals. Research efforts have focused on different formats for the lay presentation of health information. However, there is a paucity of data on how patients integrate evidence-based health information with other factors such as their preferences for information and experiences with information-seeking. The aim of this project is to explore how people with multiple sclerosis (MS) integrate health information with their needs, experiences, preferences and values and how these factors can be incorporated into an online resource of evidence-based health information provision for people with MS and their families.

Methods - This project is an Australian-Italian collaboration between researchers, MS societies and people with MS. Using a four-stage mixed methods design, a model will be developed for presenting evidence-based health information on the Internet for people with MS and their families. This evidence-based health information will draw upon systematic reviews of MS interventions from The Cochrane Library. Each stage of the project will build on the last. After conducting focus groups with people with MS and their family members (Stage 1), we will develop a model for summarising and presenting Cochrane MS reviews that is integrated with supporting information to aid understanding and decision making. This will be reviewed and finalised with people with MS, family members, health professionals and MS Society staff (Stage 2), before being uploaded to the Internet and evaluated (Stages 3 and 4).

Discussion - This project aims to produce accessible and meaningful evidence-based health information about MS for use in the varied decision making and management situations people encounter in everyday life. It is expected that the findings will be relevant to broader efforts to provide evidence-based health information for patients and the general public. The international collaboration also permits exploration of cultural differences that could inform international practice.

Predicting landscape-genetic consequences of habitat loss, fragmentation and mobility for multiple species of woodland birds

Inference concerning the impact of habitat fragmentation on dispersal and gene flow is a key theme in landscape genetics. Recently, the ability of established approaches to identify reliably the differential effects of landscape structure (e.g. land-cover composition, remnant vegetation configuration and extent) on the mobility of organisms has been questioned. More explicit methods of predicting and testing for such effects must move beyond post hoc explanations for single landscapes and species. Here, we document a process for making a priori predictions, using existing spatial and ecological data and expert opinion, of the effects of landscape structure on genetic structure of multiple species across replicated landscape blocks. We compare the results of two common methods for estimating the influence of landscape structure on effective distance: least-cost path analysis and isolation-by-resistance. We present a series of alternative models of genetic connectivity in the study area, represented by different landscape resistance surfaces for calculating effective distance, and identify appropriate null models. The process is applied to ten species of sympatric woodland-dependant birds. For each species, we rank a priori the expectation of fit of genetic response to the models according to the expected response of birds to loss of structural connectivity and landscape-scale tree-cover. These rankings (our hypotheses) are presented for testing with empirical genetic data in a subsequent contribution. We propose that this replicated landscape, multi-species approach offers a robust method for identifying the likely effects of landscape fragmentation on dispersal.

Preliminary insights from a multiple case study on process modelling impact

Process modelling – the design and use of graphical documentations of an organisation’s business processes – is a key method to document and use information about business processes in organisational projects. Still, despite current interest in process modelling, this area of study still faces essential challenges. One of the key unanswered questions concerns the impact of process modelling in organisational practice. Process modelling initiatives call for tangible results in the form of returns on the substantial investments that organisations undertake to achieve improved processes. This study explores the impact of process model use on end-users and its contribution to organisational success. We posit that the use of conceptual models creates impact in organisational process teams. We also report on a set of case studies in which we explore tentative evidence for the development of impact of process model use. The results of this work provide a better understanding of process modelling impact from information practices and also lead to insights into how organisations should conduct process modelling initiatives in order to achieve an optimum return on their investment.

Entrepreneurship as a process : toward harmonizing multiple perspectives

Are there any common denominators within the diversity of entrepreneurship literature that may serve as foundations for understanding the entrepreneurial process in a systematic and comprehensive way that is useful to both scholars and practitioners? The objective of this paper was to discover about the entrepreneurial process what, if anything, is both generic (all processes that are “entrepreneurial” do this) and distinct (only entrepreneurial processes do this). Our approach was to evaluate published models of entrepreneurial process to discover what scholars have argued about what entrepreneurs do and how they do it (the processes they use) and to seek out any key commonalities that scholars claim are associated with the phenomenon. Unfortunately for the field, the investigation demonstrates that, as at the time of our investigation, the 32 extant models of entrepreneurial process are highly fragmented in their claims and emphases and are insufficient for establishing an infrastructure upon which to synthesize an understanding of entrepreneurial process that is both generic and distinct. Insights gained in the study lead to suggestions for future research and theory development of which the most urgent is the need to develop a single harmonized model of entrepreneurial process capable of embracing the best of what is on offer and adding new theoretical arguments in areas where practice shows that they are lacking.

The new higher education reality: what is an appropriate model to address the widening participation agenda?

Along with the massification of higher education comes a need for new models to support the success of greater numbers of diverse students. A greater proportion of these students are ‘non-traditional’ in terms of preparedness, socioeconomic status  and geography. This paper introduces an Associate Degree model designed to support this new higher education reality of broader student cohorts, thin regional markets and cross-sectoral collaboration. Background literature on challenges facing the higher education sector and its prospective students is presented, with a particular focus on regionality. An argument is made for the role of curriculum and pedagogy as enablers of non-traditional student success. This is supported by the results of a mixed-methods exploratory study. This Associate Degree model was attractive to students and institutes. Students experienced similar levels of challenge, workload and progress to their traditional peers. While technology was essential for the success of the model, it played a supporting role to the relationships and multiple modes of learning it facilitated. This article provides insights for institutions seeking to address the broadening participation agenda.

Multiple-object tracking in cluttered and crowded public spaces

This paper addresses the problem of tracking moving objects of variable appearance in challenging scenes rich with features and texture. Reliable tracking is of pivotal importance in surveillance applications. It is made particularly difficult by the nature of objects encountered in such scenes: these too change in appearance and scale, and are often articulated (e.g. humans). We propose a method which uses fast motion detection and segmentation as a constraint for both building appearance models and their robust propagation (matching) in time. The appearance model is based on sets of local appearances automatically clustered using spatio-kinetic similarity, and is updated with each new appearance seen. This integration of all seen appearances of a tracked object makes it extremely resilient to errors caused by occlusion and the lack of permanence of due to low data quality, appearance change or background clutter. These theoretical strengths of our algorithm are empirically demonstrated on two hour long video footage of a busy city marketplace.

The correlates of preschoolers' compliance with screen recommendations exist across multiple domains

Objective:
To investigate the individual, social and physical environment correlates of preschool children's compliance with Australian/Canadian and American Academy of Pediatrics (AAP) screen recommendations.

Method:
An Ecological Model (EM) was used to identify constructs potentially associated with children's screen time. In 2008–2009, parents in Melbourne, Australia, reported their child's screen time and on a range of potential correlates. Children (n = 935; 54% boys, mean age 4.54 ± 0.70 years) were assessed as meeting or not meeting each of the screen recommendations. Logistic regression assessed bivariable and multivariable associations.

Results:
In total, 15 explanatory variables, across the three domains of the EM were associated with boys' and/or girls' compliance with either Australian/Canadian or AAP recommendations. Correlates varied by sex and recommendation. Maternal television viewing time was the only consistent correlate for both boys' and girls' compliance with both recommendations. No demographic groups were identified as being less likely to comply with screen recommendations.

Conclusion:
Public health programs should take account of the sex-specific nature of correlates of preschool children's screen time. Preschool children across all demographic groups need support to engage in less screen use. Parents may benefit from education and parenting skills to minimize potentially harmful effects of excessive screen time for their child.

Multitasking: multiple, domain-specific cognitive functions in a virtual environment

Multitasking among three or more different tasks is a ubiquitous requirement of everyday cognition, yet rarely is it addressed in research on healthy adults who have had no specific training in multitasking skills. Participants completed a set of diverse subtasks within a simulated shopping mall and office environment, the Edinburgh Virtual Errands Test (EVET). The aim was to investigate how different cognitive functions, such as planning, retrospective and prospective memory, and visuospatial and verbal working memory, contribute to everyday multitasking. Subtasks were chosen to be diverse, and predictions were derived from a statistical model of everyday multitasking impairments associated with frontal-lobe lesions (Burgess, Veitch, de Lacy Costello, & Shallice, 2000b). Multiple regression indicated significant independent contributions from measures of retrospective memory, visuospatial working memory, and online planning, but not from independent measures of prospective memory or verbal working memory. Structural equation modelling showed that the best fit to the data arose from three underlying constructs, with Memory and Planning having a weak link, but with both having a strong directional pathway to an Intent construct that reflected implementation of intentions. Participants who followed their preprepared plan achieved higher scores than those who altered their plan during multitask performance. This was true regardless of whether the plan was efficient or poor. These results substantially develop and extend the Burgess et al. (2000b) model to healthy adults and yield new insight into the poorly understood area of everyday multitasking. The findings also point to the utility of using virtual environments for investigating this form of complex human cognition.