Power electronics education electronic book (PEEEB)

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Social clubs and social capital : the effect of electronic gaming machines in disadvantaged regions on the creation or destruction of community resilience

The role of particular third sector organisations, Social Clubs, in supporting gambling through the use of EGMs in venues presents as a difficult social issue. Social Clubs gain revenue from gambling activities; but also contribute to social well-being through the provision of services to communities. The revenues derived from gambling in specific geographic locales has been seen by government as a way to increase economic development particularly in deprived areas. However there are also concerns about accessibility of low-income citizens to Electronic Gaming Machines (EGMS) and the high level of gambling overall in these deprived areas. We argue that social capital can be viewed as a guard against deleterious effects of unconstrained use of EGM gambling in communities. However, it is contended that social capital may also be destroyed by gambling activity if commercial business actors are able to use EGMs without community obligations to service provision. This paper examines access to gambling through EGMs and its relationship to social capital and the consequent effect on community resilience, via an Australian case study. The results highlight the potential two-way relationship between gambling and volunteering, such that volunteering (and social capital more generally) may help protect against problems of gambling, but also that volunteering as an activity may be damaged by increased gambling activity. This suggests that, regardless of the direction of causation, it is necessary to build up social capital via volunteering and other social capital activities in areas where EGMS are concentrated. The study concludes that Social Clubs using EGMs to derive funds are uniquely positioned within the community to develop programs that foster social capital creation and build community resilience in deprived areas.

Catch me if you can : the effective service of bankruptcy documents in a changing world

This article discusses some recent judicial decisions to assist legal practitioners to overcome some of the problems encountered when serving Bankruptcy Notices and Creditor’s Petitions. Some of the issues covered in the discussion are: What the valid last-known address of the debtor can be, whether a Bankruptcy Notice can be validly served by email on a debtor who is located outside Australia, whether service of a Bankruptcy Notice is valid when the debtor is outside Australia when service on the debtor occurs in Australia, whether the creditor’s failure to obtain leave for service of a Bankruptcy Notice can be excused, what can be done regarding personal service of a Creditor’s Petition when a debtor is outside Australia and whether the Court can set aside a sequestration order. The article goes on to place the issues in the context of broader bankruptcy policies noting that effective service of bankruptcy documents is challenging in a world where mobility of debtors is global and new modes of communication ever changing.

Creative industries after the first decade of debate

It has now been over a decade since the concept of creative industries was first put into the public domain through the Creative Industries Mapping Documents developed by the Blair Labour government in Britain. The concept has developed traction globally, but it has also been understood and developed in different ways in Europe, Asia, Australia, New Zealand and North America, as well as through international bodies such as UNCTAD and UNESCO. A review of the policy literature reveals that while questions and issues remain around definitional coherence, there is some degree of consensus emerging about the size, scope and significance of the sectors in question in both advanced and developing economies. At the same time, debate about the concept remains highly animated in media, communication and cultural studies, with its critics dismissing the concept outright as a harbinger of neo-liberal ideology in the cultural sphere. This paper couches such critiques in light of recent debates surrounding the intellectual coherence of the concept of neo-liberalism, arguing that this term itself possesses problems when taken outside of the Anglo-American context in which it originated. It is argued that issues surrounding the nature of participatory media culture, the relationship between cultural production and economic innovation, and the future role of public cultural institutions can be developed from within a creative industries framework, and that writing off such arguments as a priori ideological and flawed does little to advance debates about 21st century information and media culture.

Export decisions within Taiwanese electrical and electronic SMEs : the role of management characteristics and attitudes

In this globalized environment, Taiwanese firms have been very successful in achieving growth via international market expansion. In particular, the Taiwanese electronics industry has shown a dynamism lacking in comparable industries around the world. However, in recent years there has been a move by many of the larger Taiwanese manufacturing firms to outsource their manufacturing to low-cost producers such as China in order to remain competitive. Conversely, most Taiwanese small- to medium-sized enterprises (SMEs) have retained their production facilities in Taiwan. These SMEs seek to expand their sales beyond the domestic market by employing an export strategy, making a significant socioeconomic contribution to the domestic and regional economies. This paper highlights the key dimensions such as enhancing factors (benefits/advantages), inhibiting factors (barriers/costs), and managerial factors (characteristics/commitment) that play an important role in the internationalization of SMEs located within the Taiwanese electronics industry. A logistic regression model is used to predict the probability of a firm being an exporter.

Electronic games: A legitimate diversional therapy intervention for health care consumers?

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Multi-version documents : A digitisation solution for textual cultural artefacts

Textual cultural heritage artefacts present two serious problems for the encoder: how to record different or revised versions of the same work, and how to encode conflicting perspectives of the text using markup. Both are forms of textual variation, and can be accurately recorded using a multi-version document, based on a minimally redundant directed graph that cleanly separates variation from content.

XML documents clustering using tensor space model - A preliminary study

A hierarchical structure is used to represent the content of the semi-structured documents such as XML and XHTML. The traditional Vector Space Model (VSM) is not sufficient to represent both the structure and the content of such web documents. Hence in this paper, we introduce a novel method of representing the XML documents in Tensor Space Model (TSM) and then utilize it for clustering. Empirical analysis shows that the proposed method is scalable for a real-life dataset as well as the factorized matrices produced from the proposed method helps to improve the quality of clusters due to the enriched document representation with both the structure and the content information.

Overview of the INEX 2010 XML mining track : clustering and classification of XML documents

The XML Document Mining track was launched for exploring two main ideas: (1) identifying key problems and new challenges of the emerging field of mining semi-structured documents, and (2) studying and assessing the potential of Machine Learning (ML) techniques for dealing with generic ML tasks in the structured domain, i.e., classification and clustering of semi-structured documents. This track has run for six editions during INEX 2005, 2006, 2007, 2008, 2009 and 2010. The first five editions have been summarized in previous editions and we focus here on the 2010 edition. INEX 2010 included two tasks in the XML Mining track: (1) unsupervised clustering task and (2) semi-supervised classification task where documents are organized in a graph. The clustering task requires the participants to group the documents into clusters without any knowledge of category labels using an unsupervised learning algorithm. On the other hand, the classification task requires the participants to label the documents in the dataset into known categories using a supervised learning algorithm and a training set. This report gives the details of clustering and classification tasks.

Note sequence morphing algorithms for performance of electronic dance music

This paper describes algorithms that can musically augment the realtime performance of electronic dance music by generating new musical material by morphing. Note sequence morphing involves the algorithmic generation of music that smoothly transitions between two existing musical segments. The potential of musical morphing in electronic dance music is outlined and previous research is summarised; including discussions of relevant music theoretic and algorithmic concepts. An outline and explanation is provided of a novel Markov morphing process that uses similarity measures to construct transition matrices. The paper reports on a ‘focus-concert’ study used to evaluate this morphing algorithm and to compare its output with performances from a professional DJ. Discussions of this trial include reflections on some of the aesthetic characteristics of note sequence morphing. The research suggests that the proposed morphing technique could be effectively used in some electronic dance music contexts.

XML documents clustering using Tensor Space Model

The traditional Vector Space Model (VSM) is not able to represent both the structure and the content of XML documents. This paper introduces a novel method of representing XML documents in a Tensor Space Model (TSM) and then utilizing it for clustering. Empirical analysis shows that the proposed method is scalable for large-sized datasets; as well, the factorized matrices produced from the proposed method help to improve the quality of clusters through the enriched document representation of both structure and content information.

Resourcing resilience through recreational music programs

Many music programs in Australia deliver a United States (US) package created by the Recreational Music-Making Movement, founded by Karl Bruhn and Barry Bittman. This quasi-formal group of music makers, academics and practitioners uses the logic of decentralised global networks to connect with local musicians, offering them benefits associated with their ‘Recreational Music Program’ (RMP). These RMPs encapsulate the broad goals of the movement, developed in the US during the 1980s, and now available as a package, endorsed by the National Association of Music Merchants (NAMM), for music retailers and community organisations to deliver locally (Bittman et al., 2003). High participation rates in RMPs have been historically documented amongst baby boomers with disposable income. Yet the Australian programs increasingly target marginalised groups and associated funding sources, which in turn has lowered the costs of participation. This chapter documents how Australian manifestations of RMPs presently report on the benefits of participation to attract cross-sector funding. It seeks to show the diversity of participants who claim to have developed and accessed resources that improve their capacity for resilience through recreational music performance events. We identify funding issues pertaining to partnerships between local agencies and state governments that have begun to commission such music programs. Our assessment of eight Australian RMPs includes all additional music groups implemented since the first program, their purposes and costs, the skills and coping strategies that participants developed, how organisers have reported on resources, outcomes and attracted funding. We represent these features through a summary table, standard descriptive statistics and commentaries from participants and organisers.

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.

Selected new training documents to update user profile

Relevance Feedback (RF) has been proven very effective for improving retrieval accuracy. Adaptive information filtering (AIF) technology has benefited from the improvements achieved in all the tasks involved over the last decades. A difficult problem in AIF has been how to update the system with new feedback efficiently and effectively. In current feedback methods, the updating processes focus on updating system parameters. In this paper, we developed a new approach, the Adaptive Relevance Features Discovery (ARFD). It automatically updates the system's knowledge based on a sliding window over positive and negative feedback to solve a nonmonotonic problem efficiently. Some of the new training documents will be selected using the knowledge that the system currently obtained. Then, specific features will be extracted from selected training documents. Different methods have been used to merge and revise the weights of features in a vector space. The new model is designed for Relevance Features Discovery (RFD), a pattern mining based approach, which uses negative relevance feedback to improve the quality of extracted features from positive feedback. Learning algorithms are also proposed to implement this approach on Reuters Corpus Volume 1 and TREC topics. Experiments show that the proposed approach can work efficiently and achieves the encouragement performance.