Ghostly sisters: feminist collaborative performance in Australia

This article examines how feminist performance has been, and continues to be, a key vehicle for the collaborative exploration of sexual difference and female subjectivity in Australia. It focuses specifically on the Lean Sisters and Generic Ghosts, whose collaborative performances occurred during the seventies and eighties, and their impact on subsequent feminist collaborative performance groups. As the article demonstrates, this counter-cultural tradition of performance typically deploys tactics of intertextuality, cross-media experimentation, humour, and détournement to critique gender oppression and its recurrence, while staging new possibilities of an embodied feminist politics.

Collaborative dance between robot and human

Dance is an inherently embodied activity. The dancer is attuned to the effects of the physical world on her own physicality and the relationship of her presence to other dancers. This research is an investigation into artificially intelligent performing agents and robots and how a human dancer can guide the learning and performance of a robot performer. Using Artificial Neural Networks as the bases for the agent’s computational intelligence, performing agents were created that can perform by collaborating with human dancers through robots.

Pavements and pathways

A one-day interdisciplinary collaborative performance event exploring Mobility and the city. A Deakin-Monash collaboration in which project participants will engage in the interactive remapping of the City in a creative place-making event.

Nurse practitioner competency standards: findings from collaborative Australian and New Zealand research

Background: The title, Nurse Practitioner, is protected in most jurisdictions in Australia and New Zealand and the number of nurse practitioners is increasing in health services in both countries. Despite this expansion of the role, there is scant national or international research to inform development of nurse practitioner competency standards.

Objectives: The aim of this study was to research nurse practitioner practice to inform development of generic standards that could be applied for the education, authorisation and practice of nurse practitioners in both countries.

Design: The research used a multi-methods approach to capture a range of data sources including research of policies and curricula, and interviews with clinicians. Data were collected from relevant sources in Australia and New Zealand.

Settings: The research was conducted in New Zealand and the five states and territories in Australia where, at the time of the research, the title of nurse practitioner was legally protected.

Participants: The research was conducted with a purposeful sample of nurse practitioners from diverse clinical settings in both countries. Interviews and material data were collected from a range of sources and data were analysed within and across these data modalities.

Results: Findings included identification of three generic standards for nurse practitioner practice: namely, Dynamic Practice, Professional Efficacy and Clinical Leadership. Each of these standards has a number of practice competencies, each of these competencies with its own performance indicators.

Conclusions: Generic standards for nurse practitioner practice will support a standardised approach and mutual recognition of nurse practitioner authorisation across the two countries. Additionally, these research outcomes can more generally inform education providers, authorising bodies and clinicians on the standards of practice for the nurse practitioner whilst also contributing to the current international debate on nurse practitioner standards and scope of practice.

Investigating the shared visual context in distributed collaborative work

Distributed collaborators require computermediated communication (CMC) technologies in order to work together. Various systems have been developed for this express purpose with varying degrees of success. A basic method of evaluating the usability of a system is to compare it with face-to-face interaction. To replicate the face-to-face context, it is necessary to investigate how visual information plays a role in supporting collaborators performing tasks.
This research examines the effects of visual information and its role in both face-to-face and video generated visual contexts. The results were generated by asking participants to collaboratively solve visual tasks in either of the two contexts. The results show that both the face-to-face and the video conferencing contexts have similar effects on subjects’ ability to perform tasks. Task outcomes exhibited no significant difference between these two contexts. Awareness and conversational grounding had positive effects on the subject’s task performance and communication. On the other hand, presence had mixed effects on a subject’s task performance and communication behaviors.

Collaborative improvement : interplay but not a game

Collaboration literally means working together. Collaborative improvement is an extension of continuous improvement and can be defined as a purposeful inter-company interactive process that focuses on continuous incremental innovation aimed at enhancing the collaboration’s overall performance. Developing collaborative improvement is a protracted and difficult process. Previous research has identified a number of factors affecting that process and suggested that it is not so much the individual factors, but rather their interplay that determines the successful development of collaborative improvement. This article reports research aimed at developing a deeper understanding of that interplay. Ten relationships between ten factors are presented and discussed. It appears that vision, approach, trust and commercial reality are the strongest factors. These factors are, however, influenced by, or affect the other factors, notably national culture, partner characteristics and competences, the use of power, individual behaviour and commitment. The way this interplay develops varies from case to case and has great influence on the development of collaborative improvement.

Can eLearning provide tools for capacity-building to improve performance in the nonprofit sector?

Can E-learning 2.0 provide a platform for improving performance in nonprofit organizations? It is argued that Web 2.0 technologies provide the tools that today’s students rely on in the learning environment. As such they may be the means to attract and retain learners in training and education programs designed to improve performance. E-learning delivered through a blended learning model has proven to be effective in corporate training and higher education. Can the interactive, collaborative model offered in E-learning 2 prove to be as effective? This paper reviews the literature on blended learning and capacity-building as background for a discussion of the potential that e-learning models enhanced by web 2.0 technologies have for expanding access to education and training and facilitating implementation of skills and knowledge gained in the workplace. We suggest that increasing training opportunities for staff and making nonprofit management education more accessible through online programs is not enough to meet the challenge. What is needed at this point is practicable education and training delivered in today’s user’s environment, that is online and “on the go.”

Differential privacy for neighborhood-based collaborative filtering

As a popular technique in recommender systems, Collaborative Filtering (CF) has received extensive attention in recent years. However, its privacy-related issues, especially for neighborhood-based CF methods, can not be overlooked. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. The algorithm includes two privacy-preserving operations: Private Neighbor Selection and Recommendation-Aware Sensitivity. Private Neighbor Selection is constructed on the basis of the notion of differential privacy to privately choose neighbors. Recommendation-Aware Sensitivity is introduced to enhance the performance of recommendations. Theoretical and experimental analysis are provided to show the proposed algorithm can preserve differential privacy while retaining the accuracy of recommendations.

Dancing in suits: a performer's perspective on the collaborative exchange between self, body, motion capture, animation and audience

The motion capture process places unique demands on performers. The impact of this process on the simultaneously artistic/somatic nature of dance practice is profound. This paper explores, from a performer’s perspective, how the process of performing in an optical motion capture system can impact and limit, but also expand and reconfigure a dancer’s somatic practice. This paper argues that working within motion capture processes affects not only the immediate contexts of capture and interactive performance, but also sets up a dialogue between dance practices within and beyond the motion capture studio.

Influence of high performance work systems on employee subjective well-being and job burnout: empirical evidence from the Chinese healthcare sector

Over the last two decades, high performance work systems (HPWSs) research has been dominated by examining the effects of these systems on firm performance. Research on the impact of HPWSs on employees has been marginalised. This study examines the impact of HPWSs on two psychological outcomes for employees, namely, subjective well-being (SWB) and workplace burnout, by utilising data collected from 1488 physicians and nurses in 25 Chinese hospitals. It also examines the moderating effects of employees' organisational based self-esteem (OBSE), as an individual intervention and physician–nurse relationships, as an organisational intervention, on the relationship between HPWSs and employee outcomes. HPWS is found to increase employees' SWB and decrease burnout. Such well-being-enhancing and burnout-relieving effects are stronger when employees have high OBSE. The positive effect of HPWS on SWB is also stronger when there is a collaborative relationship among employees in an organisation. The major contribution of this study is to unpack the ‘black box’ of how HPWS influences employee well-being in the Chinese healthcare sector context.

Achieving eating independence in an acute stroke ward : Developing a collaborative care plan

Aim: This article outlines the development and implementation of a collaborative feeding care plan (FCP) for stroke patients in an acute stroke ward. The aim of this pilot study was to evaluate the impact of an ecological intervention to improve eating independence in an acute stroke ward environment. Methods: An action research approach comprising seven stages—determine the initial problem, develop the care plan, act, reflect and monitor progress, evaluate, reflect, and refine plan—was used to track environmental changes during the development and implementation of the FCP in an acute stroke ward in an Australian regional hospital. During the evaluation phase, six allied health staff completed a survey on the FCP. The staff also completed an observation assessment integrating the Eating Disability Scale, Functional Independence Measure and Canadian Occupational Performance Measure with 12 participants with acute stoke (participants with FCP=6; participants without FCP=6). Results: The FCP group showed significant improvements in upper limb independence (p=0.046), when comparing mean admission scores (3.5±0.97) with discharge scores (4.17±2.14). Clinically significant improvements in levels of collaboration between health professionals were also demonstrated. Conclusions: The changes in team collaboration and the patient’s upper limb independence indicate how environmental change can influence acute stroke patient outcomes. It is recommended that this study be expanded to further explore the effect of ecological interventions and change.

An effective privacy preserving algorithm for neighborhood-based collaborative filtering

As a popular technique in recommender systems, Collaborative Filtering (CF) has been the focus of significant attention in recent years, however, its privacy-related issues, especially for the neighborhood-based CF methods, cannot be overlooked. The aim of this study is to address these privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. This algorithm includes two privacy preserving operations: Private Neighbor Selection and Perturbation. Using the item-based method as an example, Private Neighbor Selection is constructed on the basis of the notion of differential privacy, meaning that neighbors are privately selected for the target item according to its similarities with others. Recommendation-Aware Sensitivity and a re-designed differential privacy mechanism are introduced in this operation to enhance the performance of recommendations. A Perturbation operation then hides the true ratings of selected neighbors by adding Laplace noise. The PNCF algorithm reduces the magnitude of the noise introduced from the traditional differential privacy mechanism. Moreover, a theoretical analysis is provided to show that the proposed algorithm can resist a KNN attack while retaining the accuracy of recommendations. The results from experiments on two real datasets show that the proposed PNCF algorithm can obtain a rigid privacy guarantee without high accuracy loss. © 2013 Published by Elsevier B.V. All rights reserved.

Using technology to improve peer review and collaborative conversations to benchmark academic standards

In 2010 the Australian government commissioned the Australian Learning and Teaching Council (ALTC) to undertake a national project to facilitate disciplinary development of threshold learning standards. The aim was to lay the foundation for all higher education providers to demonstrate to the new national higher education regulator, the Tertiary Education Quality and Standards Agency (TEQSA), that graduates achieved or exceeded minimum academic standards. Through a yearlong consultative process, representatives of employers, professional bodies, academics and students, developed learning standards applying to any Australian higher education provider. Willey and Gardner reported using a software tool, SPARKPLUS, in calibrating academic standards amongst teaching staff in large classes. In this paper, we investigate the effectiveness of this technology to promote calibrated understandings with the national accounting learning standards. We found that integrating the software with a purposely designed activity provided significant efficiencies in calibrating understandings about learning standards, developed expertise and a better understanding of what is required to meet these standards and how best to demonstrate them. The software and supporting calibration and assessment process can be adopted by other disciplines, including engineering, seeking to provide direct evidence about performance against learning standards. © 2012 IEEE.

Developing a collaborative model of industry feedback for work placement of business students

Work-integrated learning (WIL) is a signature feature of study in many higher education institutions. In business degrees, industry feedback is recognized as an integral part of the assessment of WIL, yet the role played by industry in appraising student performance in the workplace has not been clearly defined. Based on interviews with industry supervisors and academic mentors, this paper addresses the integration of academic and industry supervisor assessment practices designed to maximize student learning outcomes and capture the depth of the learning experiences during a work placement. A model of industry feedback was developed to incorporate planned assessment practices that achieve the learning outcomes agreed to at the start of the placement by all stakeholders: the student, the academic mentor and the industry supervisor.

Privacy preserving collaborative filtering for KNN attack resisting

Privacy preserving is an essential aspect of modern recommender systems. However, the traditional approaches can hardly provide a rigid and provable privacy guarantee for recommender systems, especially for those systems based on collaborative filtering (CF) methods. Recent research revealed that by observing the public output of the CF, the adversary could infer the historical ratings of the particular user, which is known as the KNN attack and is considered a serious privacy violation for recommender systems. This paper addresses the privacy issue in CF by proposing a Private Neighbor Collaborative Filtering (PriCF) algorithm, which is constructed on the basis of the notion of differential privacy. PriCF contains an essential privacy operation, Private Neighbor Selection, in which the Laplace noise is added to hide the identity of neighbors and the ratings of each neighbor. To retain the utility, the Recommendation-Aware Sensitivity and a re-designed truncated similarity are introduced to enhance the performance of recommendations. A theoretical analysis shows that the proposed algorithm can resist the KNN attack while retaining the accuracy of recommendations. The experimental results on two real datasets show that the proposed PriCF algorithm retains most of the utility with a fixed privacy budget.