Nursing and justice as a basic human need

This paper explores the idea that justice is a basic human need akin to those famously depicted in Maslow’s hierarchy of human needs and, as such, warrants recognition as a core element in representative ideas about nursing. Early nurse theorists positioned the principles and practice of nursing as having their origins in ‘universal human needs’. The principle of deriving nursing care from human needs was thought to provide a guide not only for promoting health, but for preventing disease and illness. The nursing profession has had a longstanding commitment to social justice as a core professional value and ideal, obligating nurses to address the social conditions that undermine people’s health.The idea of justice as a universal human need per se and its possible relationship to people’s health outcomes has, however, not been considered. One reason for this is that justice in nursing discourse has more commonly been associated with law and ethics, and the legal and ethical responsibilities of nurses in relation to individualized patient care and, more recently, changing systems of care to improve health and health outcomes. Although this association is not incorrect, it is incomplete.A key aim of this paper is to redress this oversight and to encourage a broader conceptualization of justice as necessary for human survival, health and development, not merely as a professional value, or legal or ethical principle for guiding human conduct.

A decision-making model to choose business intelligence platforms for organizations

In recent time, technology applications in different fields, especially Business Intelligence (BI) have been developed rapidly and considered to be one of the most significant uses of information technology with special position reserved. The application of BI systems provides organizations with a sense of superiority in the competitive environment. Despite many advantages, the companies applying such systems may also encounter problems in decision-making process because of the highly diversified interactions within the systems. Hence, the choice of a suitable BI platform is important to take the great advantage of using information technology in all organizational fields. The current research aims at addressing the problems existed in the organizational decision-making process, proposing and implementing a suitable BI platform using Iranian companies as case study. The paper attempts to present a solitary model based on studying different methods in BI platform choice and applying the chosen BI platform for different decisionmaking processes. The results from evaluating the effectiveness of subsequently implementing the model for Iranian Industrial companies are discussed.

Trade-off manipulations in the development of negotiation decision support systems

Our model of negotiation for constructing Negotiation Decision Support Systems is based upon Principled Negotiation and uses trade-off manipulations in order to provide decision support. A resulting system, Family_Winner, was constructed using several information systems techniques. Trade-off Maps (a variant of Constraint Diagrams) are used to represent trade-off opportunities, while an empirically derived formula calculates the amount of compensation given to the ratings of issues remaining in dispute. The Issue Decomposition Hierarchy embedded in the system allows for the incorporation of sub-issues. Family_Winner was originally built for use in Australian Family Law. We believe our model can be extended for use in various other domains.

Family_Winner has been evaluated in the areas of industrial relations, international disputation and company disputes. Results from our evaluation suggest the system works satisfactorily in these domains. We conclude this paper by describing future projects that will develop and extend Family_Winner's functions and applicability.

Creating an Innovation Exception? Copyright Law as the Infrastructure for Innovation

Innovation is clearly essential for economic growth, cultural development and personal autonomy. Yet the relationship between innovation and copyright law in Australia is uncertain and perhaps overly restrictive. After the Australia-United States Free Trade Agreement Australia now has a copyright regime that can broadly be
described as a lock up and lock out scheme. Whilst the Australian Government has paid lip service to innovation the Australian Copyright Act, which provides the essential legal infrastructure for innovation, now privileges the rights of owners over the interests of the public. In particular, the Copyright Act neglects to create a specific exception for technology innovation. If there is to be some coherence in Australia
thinking with regards to innovation and copyright policy it is crucial that such an exception be created. Arguably, it is possible that such an exception can withstand the scrutiny of the three step test. At present the only ‘exception’ that can be said to exist is in the form of the limits of the authorisation liability provisions or the ISP safe harbour scheme. Australian copyright law needs something more substantial than that
and needs for there to be a clear hierarchy between the exceptions and the liability provisions.

Export quality : representing Fijian bodies and the economy of war

Fijian bodies have become a valuable commodity in the economy of war. Remittances from workers overseas are Fiji’s largest income – exceeding that of tourism and sugar export. This essay examines historical and contemporary representations of the black male body that perpetuate the exploitation of Fijians by inscribing the Fijian male body as warrior, criminal and protector. Taking a multidisciplinary approach informed by sociology, cultural theory, Pacific studies, visual culture, feminist and post-colonial theory, my practice is the vehicle through which I address issues of neocolonial commodification of Fijian bodies. Through an analysis of my own staged photographs and vernacular images taken by Fijians working for private security military companies and British and US armies, I hope to challenge audiences to consider their own perceptions of Fijian agency and subjectivity. By theorising the politicisation of the black body and interrogating colonial representations of blackness, I argue that we can begin to create links between the historical and contemporary exploitation of Fijians and that at the essence of both is an underlying racial hierarchy and economic requirement for cheap and, arguably, expendable labour.

'Becoming a supervisor' : the impact of doctoral supervision on supervisors' learning

The doctorate is an educative process for students but what is its impact on supervisors' learning about the practice of doctoral supervision? Internationally, there is an increased emphasis on formal training, monitoring and accountability of doctoral supervisors. Yet there is a striking silence about what doctoral supervisors learn through supervising doctoral students, and how the impacts on supervisors might be theorised. The aim of this article is to begin to address this gap in the doctoral education literature, based on a thematic analysis of two complementary interview studies of a cross-disciplinary sample of experienced doctoral supervisors. The analysis illustrates the significant impact of doctoral supervision on the learning and knowledge of doctoral supervisors, particularly in relation to how supervisors engage with/in the social and political context of their university, understand themselves and their students, and how the contemporary context of supervision affects the sort of pedagogical relationships supervisors establish with their doctoral students. Regardless of supervisors' discipline, position in the academic hierarchy or supervisory experience, the analysis indicates that supervisors' learning experiences shape their subjectivities and identities, and that supervision is an ongoing ontological process of ‘becoming a supervisor’. The importance of integrating a theory of ‘becoming a supervisor’ into supervisor professional development is proposed.

Graph matching with subdivision surfaces for texture synthesis on surfaces

Existing texture synthesis-from-example strategies for polygon meshes typically make use of three components: a multi-resolution mesh hierarchy that allows the overall nature of the pattern to be reproduced before filling in detail; a matching strategy that extends the synthesized texture using the best fit from a texture sample; and a transfer mechanism that copies the selected portion of the texture sample to the target surface. We introduce novel alternatives for each of these components. Use of p2-subdivision surfaces provides the mesh hierarchy and allows fine control over the surface complexity. Adaptive subdivision is used to create an even vertex distribution over the surface. Use of the graph defined by a surface region for matching, rather than a regular texture neighbourhood, provides for flexible control over the scale of the texture and allows simultaneous matching against multiple levels of an image pyramid created from the texture sample. We use graph cuts for texture transfer, adapting this scheme to the context of surface synthesis. The resulting surface textures are realistic, tolerant of local mesh detail and are comparable to results produced by texture neighbourhood sampling approaches.

Knowledge sharing through virtual layers in regional sustainable development networks

Our research examines how the organisational structure facilitates knowledge sharing within the group. This case study examines a Victorian regional sustainable group using interviews and social network analysis to identify the group’s organisational structure and its effect on knowledge sharing between the members. Our findings indicate that while the mixed membership, lack of hierarchy and layered structure are complex, these elements work together to provide members with a rich body of knowledge. The diversity and differences in membership are complimentary and combined can provide a more in-depth understanding of the regional sustainable development issues.

Layered dynamic probabilistic networks for spatio-temporal modelling

In applications such as tracking and surveillance in large spatial environments, there is a need for representing dynamic and noisy data and at the same time dealing with them at different levels of detail. In the spatial domain, there has been work dealing with these two issues separately, however, there is no existing common framework for dealing with both of them. In this paper, we propose a new representation framework called the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN), capable of handling uncertainty and representing spatial data at various levels of detail. The framework is thus particularly suited to applications in wide-area environments which are characterised by large region size, complex spatial layout and multiple sensors/cameras. For example, a building has three levels: entry/exit to the building, entry/exit between rooms and moving within rooms. To avoid the problem of a relatively large state space associated with a large spatial environment, the LDPN explicitly encodes the hierarchy of connected spatial locations, making it scalable to the size of the environment being modelled. There are three main advantages of the LDPN. First, the reduction in state space makes it suitable for dealing with wide area surveillance involving multiple sensors. Second, it offers a hierarchy of intervals for indexing temporal data. Lastly, the explicit representation of intermediate sub-goals allows for the extension of the framework to easily represent group interactions by allowing coupling between sub-goal layers of different individuals or objects. We describe an adaptation of the likelihood sampling inference scheme for the LDPN, and illustrate its use in a hypothetical surveillance scenario.

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.

Semantic data modelling and visualization using Noetica

Noetica is a tool for structuring knowledge about concepts and the reIationships between them. It differs from typical information systems in that the knowledge it represents is abstract, highly connected, and includes meta-knowledge (knowledge about knowledge). Noetica represents knowledge using a strongly typed graph data model. By providing a rich type system it is possible to represent conceptual information using formalized structures. A class hierarchy provides a basic classification for all objects. This allows for a consistency of representation that is not often found in `free' semantic networks, and gives the ability to easily extend a knowledge model while retaining its semantics. Visualization and query tools are provided for this data model. Visualization can be used to explore complete sets of link-classes, show paths while navigating through the database, or visualize the results of queries. Noetica supports goal-directed queries (a series of user-supplied goals that the system attempts to satisfy in sequence) and pathfinding queries (where the system finds relationships between objects in the database by following links).

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.

On the automated interpretation and indexing of American football

This work combines natural language understanding and image processing with incremental learning to develop a system that can automatically interpret and index American Football. We have developed a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain. Our representation combines expert knowledge, domain knowledge, spatial knowledge and temporal knowledge. We also present an incremental learning algorithm to improve the knowledge base as well as to keep previously developed concepts consistent with new data. The advantages of the incremental learning algorithm are that is that it does not split concepts and it generates a compact conceptual hierarchy which does not store instances.

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model's parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.

Automatically learning structural units in educational videos with the hierarchical hidden Markov models

In this paper we present a coherent approach using the hierarchical HMM with shared structures to extract the structural units that form the building blocks of an education/training video. Rather than using hand-crafted approaches to define the structural units, we use the data from nine training videos to learn the parameters of the HHMM, and thus naturally extract the hierarchy. We then study this hierarchy and examine the nature of the structure at different levels of abstraction. Since the observable is continuous, we also show how to extend the parameter learning in the HHMM to deal with continuous observations.