A national English curriculum far all Australian youth: Making it work for teacher and students everywhere

The authors draw on some powerful practitioner research they have been associated with recently to nvision ways in which a national curriculum might redress the inequities experienced by Australia's most disadvantaged young people.

A grouping model for distributed pipeline assets maintenance decision

Distributed pipeline assets systems are crucial to society. The deterioration of these assets and the optimal allocation of limited budget for their maintenance correspond to crucial challenges for water utility managers. Decision makers should be assisted with optimal solutions to select the best maintenance plan concerning available resources and management strategies. Much research effort has been dedicated to the development of optimal strategies for maintenance of water pipes. Most of the maintenance strategies are intended for scheduling individual water pipe. Consideration of optimal group scheduling replacement jobs for groups of pipes or other linear assets has so far not received much attention in literature. It is a common practice that replacement planners select two or three pipes manually with ambiguous criteria to group into one replacement job. This is obviously not the best solution for job grouping and may not be cost effective, especially when total cost can be up to multiple million dollars. In this paper, an optimal group scheduling scheme with three decision criteria for distributed pipeline assets maintenance decision is proposed. A Maintenance Grouping Optimization (MGO) model with multiple criteria is developed. An immediate challenge of such modeling is to deal with scalability of vast combinatorial solution space. To address this issue, a modified genetic algorithm is developed together with a Judgment Matrix. This Judgment Matrix is corresponding to various combinations of pipe replacement schedules. An industrial case study based on a section of a real water distribution network was conducted to test the new model. The results of the case study show that new schedule generated a significant cost reduction compared with the schedule without grouping pipes.

Role demands and work-family balance experience in Malaysia : the different moderating effects of collectivism and gender role identity among diverse ethnic groups

The purpose of this thesis is to examine the influence of ethnic cultural values on the relationship of role demands and the work-family balance (WFB) experience. Past studies have found that the demands from work and family roles have a different impact on the work-family experience in people of different ethnicity. Researchers attribute these results to the cultural differences across the groups. However, there has been no empirical support for these assumptions because most past studies did not explicitly measure the cultural dimension in their design. Therefore, although studies have found ethnic differences in work-family experience, as cultural variables were not measured, it cannot be determined whether these differences were due to the differing ethnic groups’ cultural styles. The present thesis is set up to address this limitation in the literature, employing the Malay and Chinese ethnic groups in Malaysia as the study samples. The investigation consisted of pilot interviews and two survey studies. The interviews were carried out to establish the perception of WFB by target participants of a non-western nation. The first survey served to identify whether the Malay and Chinese ethnic groups residing under the same economic and social systems vary in their perceptions of work and family roles. The second survey tests the research model empirically, that is, whether cultural values moderate the relationship between role demands and WFB and if these moderation effects vary across ethnic groups. From the interviews, the results indicated that work-family experience is not a universal experience, but is partly culture-specific. Specifically, in the case of Malaysia, WFB is very much observed from the role obligation perspective. In particular, balance is perceived when work duties and household affairs are both adequately fulfilled. On the other hand, the conceptualisation of WFB in terms of role satisfaction and role interference also emerged in the interviews, suggesting the universality of these constructs across cultures. The findings from Survey One indicated that participants of different ethnicities in this study do not differ greatly in their perceptions regarding their participation in work and family roles. Generally, these participants revealed the less traditional attitudes towards women’s participation in work and family roles. However, variations were observed between the two groups in terms of reasons for working, spouses’ preferences towards their employment, and the extent to which their work role is perceived to impede their normative role performance in the household. Despite these differences, the Malay and Chinese ethnic groups showed more similarities than differences in their perceptions of work and family. The findings from Survey Two, which tested the research model, produced mixed results. On the whole, the results showed that the cultural dimensions examined in this study (i.e. collectivism, work identity and family identity) did influence the relationship between role demands and WFB experience, thus providing empirical evidence for the assumption in the literature that the relationship between role demand and work-family experience is moderated by cultural values. Most importantly, support was found for the proposition that these moderation effects vary between the Malay and Chinese ethnic groups. Moreover, this study also found evidence that Malays and Chinese differ significantly on collectivism and work identity cultural dimensions where Malays are found to be more collectivist than the Chinese, while work identity is stronger in the Chinese than in the Malays. There is no difference in the levels of family identity between the two groups. Of all the three moderators, work identity was deemed the most important because many of the supported hypotheses pertained to the work identity moderating effects. In contrast, family identity does not seem to have much moderating influence on role demand-WFB relationships, while the results for the collectivism moderator are mixed. As such, although not conclusive, it can be deduced that variations in the effects of role demand on work-family experience across ethnicity are a result of the groups’ cultural differences, thereby supporting the assumption in the literature.

What you need to know to work in television

During a three-month stint at a production company, Alan McKee discovered that some of the knowledge required to work in television can only be acquired through practical experience. Here he offers some tips to help students successfully transition into the industry

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Parallel user profiling based on folksonomy for Large Scaled Recommender Systems : an implimentation of Cascading MapReduce

The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users’ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Del.icio.us website.

Shrinking neighborhood evolution : a novel stochastic algorithm for numerical optimization

Focusing on the conditions that an optimization problem may comply with, the so-called convergence conditions have been proposed and sequentially a stochastic optimization algorithm named as DSZ algorithm is presented in order to deal with both unconstrained and constrained optimizations. The principle is discussed in the theoretical model of DSZ algorithm, from which we present the practical model of DSZ algorithm. Practical model efficiency is demonstrated by the comparison with the similar algorithms such as Enhanced simulated annealing (ESA), Monte Carlo simulated annealing (MCS), Sniffer Global Optimization (SGO), Directed Tabu Search (DTS), and Genetic Algorithm (GA), using a set of well-known unconstrained and constrained optimization test cases. Meanwhile, further attention goes to the strategies how to optimize the high-dimensional unconstrained problem using DSZ algorithm.

Calibration and operational analysis of variable speed limits for high flow condition

Variable Speed Limits (VSL) is a control tool of Intelligent Transportation Systems (ITS) which can enhance traffic safety and which has the potential to contribute to traffic efficiency. This study presents the results of a calibration and operational analysis of a candidate VSL algorithm for high flow conditions on an urban motorway of Queensland, Australia. The analysis was done using a framework consisting of a microscopic simulation model combined with runtime API and a proposed efficiency index. The operational analysis includes impacts on speed-flow curve, travel time, speed deviation, fuel consumption and emission.

Scaling acoustic data analysis through collaboration and automation

Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; collaboration, manual, automatic and human-in-the loop analysis.

Deterministic diffraction loss modelling for novel broadband communication in rural environments

This paper presents a deterministic modelling approach to predict diffraction loss for an innovative Multi-User-Single-Antenna (MUSA) MIMO technology, proposed for rural Australian environments. In order to calculate diffraction loss, six receivers have been considered around an access point in a selected rural environment. Generated terrain profiles for six receivers are presented in this paper. Simulation results using classical diffraction models and diffraction theory are also presented by accounting the rural Australian terrain data. Results show that in an area of 900 m by 900 m surrounding the receivers, path loss due to diffraction can range between 5 dB and 35 dB. Diffraction loss maps can contribute to determine the optimal location for receivers of MUSA-MIMO systems in rural areas.

Effective pattern discovery for text mining

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase) based approaches should perform better than the term-based ones, but many experiments did not support this hypothesis. This paper presents an innovative technique, effective pattern discovery which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.

Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents

Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment.

Comparison of standard image segmentation methods for segmentation of brain tumors from 2D MR images

In the analysis of medical images for computer-aided diagnosis and therapy, segmentation is often required as a preliminary step. Medical image segmentation is a complex and challenging task due to the complex nature of the images. The brain has a particularly complicated structure and its precise segmentation is very important for detecting tumors, edema, and necrotic tissues in order to prescribe appropriate therapy. Magnetic Resonance Imaging is an important diagnostic imaging technique utilized for early detection of abnormal changes in tissues and organs. It possesses good contrast resolution for different tissues and is, thus, preferred over Computerized Tomography for brain study. Therefore, the majority of research in medical image segmentation concerns MR images. As the core juncture of this research a set of MR images have been segmented using standard image segmentation techniques to isolate a brain tumor from the other regions of the brain. Subsequently the resultant images from the different segmentation techniques were compared with each other and analyzed by professional radiologists to find the segmentation technique which is the most accurate. Experimental results show that the Otsu’s thresholding method is the most suitable image segmentation method to segment a brain tumor from a Magnetic Resonance Image.

Sharing with care : an information accountability perspective

Health information sharing has become a vital part of modern healthcare delivery. E-health technologies provide efficient and effective ways of sharing medical information, but give rise to issues that neither the medical professional nor the consumers have control over. Information security and patient privacy are key impediments that hinder sharing information as sensitive as health information. Health information interoperability is another issue which hinders the adoption of available e health technologies. In this paper we propose a solution for these problems in terms of information accountability, the HL7 interoperability standard and social networks for manipulating personal health records.