Pondering policy and parental perspectives : how parents view their role in shaping early childhood public policy

The focus of this paper is the role of Australian parents in early childhood education and care (ECEC), in particular, their role in shaping ECEC public policy. The paper reports the findings of a study investigating the different ways in which a group of parents viewed and experienced this role. Set against a policy backdrop where parents are positioned as 'consumers' and 'participants' in ECEC, the study employed a phenomenographic research approach to describe this role as viewed and experienced by parents. The study identified four logically related, qualitatively different ways of constituting this role among this group of parents, ranging from 'no role in shaping public policy' (the no role conception) to 'participating in policy decision-making, particularly where policy was likely to affect their child and family (the participating in policy decision-making conception). The study provides an insider-perspective on the role of parents in shaping policy and highlights variation in how this role is constituted by parents. The study also identifies factors perceived by parents as influencing their participation and discusses their implications for both policy and practice.

Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.

A synthesizable hardware evolutionary algorithm design for unmanned aerial system real-time path planning

The main objective of this paper is to detail the development of a feasible hardware design based on Evolutionary Algorithms (EAs) to determine flight path planning for Unmanned Aerial Vehicles (UAVs) navigating terrain with obstacle boundaries. The design architecture includes the hardware implementation of Light Detection And Ranging (LiDAR) terrain and EA population memories within the hardware, as well as the EA search and evaluation algorithms used in the optimizing stage of path planning. A synthesisable Very-high-speed integrated circuit Hardware Description Language (VHDL) implementation of the design was developed, for realisation on a Field Programmable Gate Array (FPGA) platform. Simulation results show significant speedup compared with an equivalent software implementation written in C++, suggesting that the present approach is well suited for UAV real-time path planning applications.

Integrating the resource-based view and transaction cost economics in immigrant business performance

This paper presents a new integrated framework that integrates the resource-based view and transaction cost economics to explain the phenomenon of immigrant entrepreneurship. We extend the existing literature on immigrant entrepreneurship by identifying different types of ethnic network resources and demonstrating how these resources interact with transaction costs in the context of Chinese immigrants. Thus, our study contributes to the literature by providing a theoretical framework which identifies mechanisms immigrant entrepreneurs use to strategically deploy resources to minimize costs and maximize performance outcomes.

Theoretical model of user acceptance : in the view of measuring success in web personalization

This paper attempts to develop a theoretical acceptance model for measuring Web personalization success. Key factors impacting Web personalization acceptance are identified from a detailed literature review. The final model is then cast in a structural equation modeling (SEM) framework comprising nineteen manifest variables, which are grouped into three focal behaviors of Web users. These variables could provide a framework for better understanding of numerous factors that contribute to the success measures of Web personalization technology. Especially, those concerning the quality of personalized features and how personalized information through personalized Website can be delivered to the user. The interrelationship between success constructs is also explained. Empirical validations of this theoretical model are expected on future research.

Development of an autonomous helicopter for aerial powerline inspections

Autonomous mini-helicopters have been seen as a viable option for aerial-based powerline inspections, however there are numerous research and engineering challenges in developing a system capable of achieving this task in a dependable manner. We have developed an autonomous helicopter as a research platform which will allow us to demonstrate proof-of-concept capabilities for powerline inspections. Through numerous development cycles and from flight test experience we have gained insights into the key challenges in this area. We discuss these insights, describe the helicopter platform and present our research progress in the area of obstacle avoidance for mini-helicopters.

Lolipop: A downtown Auckland view on Japanese street fashion

Loli-Pop brings together the relationships between the Loli-Goth and popular culture, and the strong association of the Loli-Goth with the doll, including a selection from Hardy Bernal’s personal collection of Japanese Lolita dolls. This display is supported by the highlight of the show, five full-sized garments created and constructed by AUT University Fashion staff members, Angie Finn, Yvonne Stewart, Lize Niemczyk, Gabriella Trussardi, Carmel Donnelly and Kathryn Hardy Bernal, which demonstrate the designers’ own interpretations of Gothic & Lolita, inspired by Japanese street style. The exhibit is complimented by a backdrop of photographs that illustrate the impact of the outfits when worn, modelled by AUT University Bachelor of Fashion Design students, Emily Huang, Shangshang Cookie Wang, Emily Wang, Shiahug-Wen Sean Kuo and Yanling Wang.

Similarity metrics within a point of view

In vector space based approaches to natural language processing, similarity is commonly measured by taking the angle between two vectors representing words or documents in a semantic space. This is natural from a mathematical point of view, as the angle between unit vectors is, up to constant scaling, the only unitarily invariant metric on the unit sphere. However, similarity judgement tasks reveal that human subjects fail to produce data which satisfies the symmetry and triangle inequality requirements for a metric space. A possible conclusion, reached in particular by Tversky et al., is that some of the most basic assumptions of geometric models are unwarranted in the case of psychological similarity, a result which would impose strong limits on the validity and applicability vector space based (and hence also quantum inspired) approaches to the modelling of cognitive processes. This paper proposes a resolution to this fundamental criticism of of the applicability of vector space models of cognition. We argue that pairs of words imply a context which in turn induces a point of view, allowing a subject to estimate semantic similarity. Context is here introduced as a point of view vector (POVV) and the expected similarity is derived as a measure over the POVV's. Different pairs of words will invoke different contexts and different POVV's. Hence the triangle inequality ceases to be a valid constraint on the angles. We test the proposal on a few triples of words and outline further research.

A Stochastic View of Optimal Regret through Minimax Duality

We study the regret of optimal strategies for online convex optimization games. Using von Neumann's minimax theorem, we show that the optimal regret in this adversarial setting is closely related to the behavior of the empirical minimization algorithm in a stochastic process setting: it is equal to the maximum, over joint distributions of the adversary's action sequence, of the difference between a sum of minimal expected losses and the minimal empirical loss. We show that the optimal regret has a natural geometric interpretation, since it can be viewed as the gap in Jensen's inequality for a concave functional--the minimizer over the player's actions of expected loss--defined on a set of probability distributions. We use this expression to obtain upper and lower bounds on the regret of an optimal strategy for a variety of online learning problems. Our method provides upper bounds without the need to construct a learning algorithm; the lower bounds provide explicit optimal strategies for the adversary. Peter L. Bartlett, Alexander Rakhlin

Forced landing technologies for unmanned aerial vehicles : towards safer operations

While using unmanned systems in combat is not new, what will be new in the foreseeable future is how such systems are used and integrated in the civilian space. The potential use of Unmanned Aerial Vehicles in civil and commercial applications is becoming a fact, and is receiving considerable attention by industry and the research community. The majority of Unmanned Aerial Vehicles performing civilian tasks are restricted to flying only in segregated space, and not within the National Airspace. The areas that UAVs are restricted to flying in are typically not above populated areas, which in turn are the areas most useful for civilian applications. The reasoning behind the current restrictions is mainly due to the fact that current UAV technologies are not able to demonstrate an Equivalent Level of Safety to manned aircraft, particularly in the case of an engine failure which would require an emergency or forced landing. This chapter will preset and guide the reader through a number of developments that would facilitate the integration of UAVs into the National Airspace. Algorithms for UAV Sense-and-Avoid and Force Landings are recognized as two major enabling technologies that will allow the integration of UAVs in the civilian airspace. The following sections will describe some of the techniques that are currently being tested at the Australian Research Centre for Aerospace Automation (ARCAA), which places emphasis on the detection of candidate landing sites using computer vision, the planning of the descent path trajectory for the UAV, and the decision making process behind the selection of the final landing site.

The benefits of not managing change and not communicating : towards a complex systems view of communication in evolving organisations

The relationship between change in organisations and communication about change in organisations can be analysed as a particular case of a general debate in social theory about the extent to which reality is socially constructed. Social constructivists emphasise the role of language in the construction of social realities, enacted through controlling the message agenda; material determinists assert that economic and social structural factors are more constitutive of reality as seen in strategies emphasising structural and resource interventions. Here we define a third view of language and materiality - one that leads to the potential for a reflexive, experimental approach to change based on the view that organisations are complex evolving systems.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion’s dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying general optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion's dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.