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.

Possibilidades e categorias de exploração tímbrica: considerações sobre as relações intérprete-instrumento na performance

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Performance measurement and indicators for water supply management: review and international cases

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Physics teachers initial education and professional performance: what do future teachers have to say?

This research deals with the discussion about Physics teachers’ undergraduate education and professional performance related to the knowledge acquired during this initial education. More specifically, we try to answer questions like: How do future teachers evaluate the knowledge acquired during their initial education as in terms of specific knowledge as pedagogical knowledge? What are their formative needs and future expectatives about professional performance and the school teaching environment? Data was constituted from a sample of 26 future high school physics teachers, one semester long, that were taking the supervised curricular training in a undergraduate Physics education program (called Licenciatura in Brazil), in São Paulo State public university. Besides the final report of this training, future teachers were asked to answer a questionnaire aiming to take their conceptions about their initial education program, their formative needs, future professional expectatives and high school teaching environment. According to the future teachers, the program they were about to finish was satisfactory in terms of Physics specific contents; however, about the pedagogical content knowledge and the pedagogical practice, they showed to be unsatisfied and insecure. The majority of the questionnaire responses demonstrated that they feel lack of teaching experience. Moreover, teachers emphasize other factors related to the future professional performance: possible difficulties to deal with students’ indiscipline, schools’ bad physical structure, limited number of Physics classes in high school level, lack of didactical laboratories and also they seem to be frightened that the expertise teachers do not be collaborative with the new ones. In this sense, the research outcomes shows the necessity of discussions about questions involving teachers knowledge, related to either, the Physics conceptual domain and the pedagogical one, since it matters directly to future teachers professional performance. Discussions in this sense can also help evaluation and restructuration of programs designed to initial and continuous teachers’ education.

Analysis of the interplay between knowledge and performance management in industrial clusters

The concept of industrial clustering has been studied in-depth by policy makers and researchers from many fields, mainly due to the competitive advantages it may bring to regional economies. Companies often take part in collaborative initiatives with local partners while also taking advantage of knowledge spillovers to benefit from locating in a cluster. Thus, Knowledge Management (KM) and Performance Management (PM) have become relevant topics for policy makers and cluster associations when undertaking collaborative initiatives. Taking this into account, this paper aims to explore the interplay between both topics using a case study conducted in a collaborative network formed within a cluster. The results show that KM should be acknowledged as a formal area of cluster management so that PM practices can support knowledge-oriented initiatives and therefore make better use of the new knowledge created. Furthermore, tacit and explicit knowledge resulting from PM practices needs to be stored and disseminated throughout the cluster as a way of improving managerial practices and regional strategic direction. Knowledge Management Research & Practice (2012) 10, 368-379. doi:10.1057/kmrp.2012.23

Structured P2P Video Streaming and Collaborative Failure Detection

Il video streaming in peer-to-peer sta diventando sempre più popolare e utiliz- zato. Per tali applicazioni i criteri di misurazione delle performance sono: - startup delay: il tempo che intercorre tra la connessione e l’inizio della ripro- duzione dello stream (chiamato anche switching delay), - playback delay: il tempo che intercorre tra l’invio da parte della sorgente e la riproduzione dello stream da parte di un peer, - time lag: la differenza tra i playback delay di due diversi peer. Tuttavia, al giorno d’oggi i sistemi P2P per il video streaming sono interessati da considerevoli ritardi, sia nella fase di startup che in quella di riproduzione. Un recente studio su un famoso sistema P2P per lo streaming, ha mostrato che solitamente i ritardi variano tra i 10 e i 60 secondi. Gli autori hanno osservato anche che in alcuni casi i ritardi superano i 4 minuti! Si tratta quindi di gravi inconvenienti se si vuole assistere a eventi in diretta o se si vuole fruire di applicazioni interattive. Alcuni studi hanno mostrato che questi ritardi sono la conseguenza della natura non strutturata di molti sistemi P2P. Ogni stream viene suddiviso in blocchi che vengono scambiati tra i peer. A causa della diffusione non strutturata del contenuto, i peer devono continuamente scambiare informazioni con i loro vicini prima di poter inoltrare i blocchi ricevuti. Queste soluzioni sono estremamente re- sistenti ai cambiamenti della rete, ma comportano una perdita notevole in termini di prestazioni, rendendo complicato raggiungere l’obiettivo di un broadcast in realtime. In questo progetto abbiamo lavorato su un sistema P2P strutturato per il video streaming che ha mostrato di poter offrire ottimi risultati con ritardi molto vicini a quelli ottimali. In un sistema P2P strutturato ogni peer conosce esattamente quale blocchi inviare e a quali peer. Siccome il numero di peer che compongono il sistema potrebbe essere elevato, ogni peer dovrebbe operare possedendo solo una conoscenza limitata dello stato del sistema. Inoltre il sistema è in grado di gestire arrivi e partenze, anche raggruppati, richiedendo una riorganizzazione limitata della struttura. Infine, in questo progetto abbiamo progettato e implementato una soluzione personalizzata per rilevare e sostituire i peer non più in grado di cooperare. Anche per questo aspetto, l’obiettivo è stato quello di minimizzare il numero di informazioni scambiate tra peer.

Method of collaborative detection of autonomous transport vehicles based on laser rangefinder data

To master changing performance demands, autonomous transport vehicles are deployed to make inhouse material flow applications more flexible. The socalled cellular transport system consists of a multitude of small scale transport vehicles which shall be able to form a swarm. Therefore the vehicles need to detect each other, exchange information amongst each other and sense their environment. By provision of peripherally acquired information of other transport entities, more convenient decisions can be made in terms of navigation and collision avoidance. This paper is a contribution to collective utilization of sensor data in the swarm of cellular transport vehicles.

Social skills as predictors of communication, performance and quality of collaboration in project-based learning

The assumption that social skills are necessary ingredients of collaborative learning is well established but rarely empirically tested. In addition, most theories on collaborative learning focus on social skills only at the personal level, while the social skill configurations within a learning group might be of equal importance. Using the integrative framework, this study investigates which social skills at the personal level and at the group level are predictive of task-related e-mail communication, satisfaction with performance and perceived quality of collaboration. Data collection took place in a technology-enhanced long-term project-based learning setting for pre-service teachers. For data collection, two questionnaires were used, one at the beginning and one at the end of the learning cycle which lasted 3 months. During the project phase, the e-mail communication between group members was captured as well. The investigation of 60 project groups (N = 155 for the questionnaires; group size: two or three students) and 33 groups for the e-mail communication (N = 83) revealed that personal social skills played only a minor role compared to group level configurations of social skills in predicting satisfaction with performance, perceived quality of collaboration and communication behaviour. Members from groups that showed a high and/or homogeneous configuration of specific social skills (e.g., cooperation/compromising, leadership) usually were more satisfied and saw their group as more efficient than members from groups with a low and/or heterogeneous configuration of skills.

Cloud Computing Service for Managing Large Medical Image Data-Sets Using Balanced Collaborative Agents

Managing large medical image collections is an increasingly demanding important issue in many hospitals and other medical settings. A huge amount of this information is daily generated, which requires robust and agile systems. In this paper we present a distributed multi-agent system capable of managing very large medical image datasets. In this approach, agents extract low-level information from images and store them in a data structure implemented in a relational database. The data structure can also store semantic information related to images and particular regions. A distinctive aspect of our work is that a single image can be divided so that the resultant sub-images can be stored and managed separately by different agents to improve performance in data accessing and processing. The system also offers the possibility of applying some region-based operations and filters on images, facilitating image classification. These operations can be performed directly on data structures in the database.

Heterogeneous Collaborative Sensor Network for Electrical Management of an Automated House with PV Energy

In this paper we present a heterogeneous collaborative sensor network for electrical management in the residential sector. Improving demand-side management is very important in distributed energy generation applications. Sensing and control are the foundations of the “Smart Grid” which is the future of large-scale energy management. The system presented in this paper has been developed on a self-sufficient solar house called “MagicBox” equipped with grid connection, PV generation, lead-acid batteries, controllable appliances and smart metering. Therefore, there is a large number of energy variables to be monitored that allow us to precisely manage the energy performance of the house by means of collaborative sensors. The experimental results, performed on a real house, demonstrate the feasibility of the proposed collaborative system to reduce the consumption of electrical power and to increase energy efficiency.

A Balanced Memory-Based Collaborative Filtering Similarity Measure.

Collaborative filtering recommender systems contribute to alleviating the problem of information overload that exists on the Internet as a result of the mass use of Web 2.0 applications. The use of an adequate similarity measure becomes a determining factor in the quality of the prediction and recommendation results of the recommender system, as well as in its performance. In this paper, we present a memory-based collaborative filtering similarity measure that provides extremely high-quality and balanced results; these results are complemented with a low processing time (high performance), similar to the one required to execute traditional similarity metrics. The experiments have been carried out on the MovieLens and Netflix databases, using a representative set of information retrieval quality measures.