Forecasting outcomes in tennis matches using within-match betting markets

Klaassen and Magnus (2003) provide a model of the probability of a given player winning a tennis match, with the prediction updated on a point-by-point basis. This paper provides a point-by-point comparison of that model with the probability of a given player winning the match, as implied by betting odds. The predictions implied by the betting odds match the model predictions closely, with an extremely high correlation being found between the model and the betting market. The results for both men’s and women’s matches also suggest that there is a high level of efficiency in the betting market, demonstrating that betting markets are a good predictor of the outcomes of tennis matches. The significance of service breaks and service being held is anticipated up to four points prior to the end of the game. However, the tendency of players to lose more points than would be expected after conceding a break of service is not captured instantaneously in betting odds. In contrast, there is no evidence of a biased reaction to a player winning a game on service.

Batch Verification of Validity of Bids in Homomorphic E-auction

Bid opening in e-auction is efficient when a homomorphic secret sharing function is employed to seal the bids and homomorphic secret reconstruction is employed to open the bids. However, this high efficiency is based on an assumption: the bids are valid (e.g., within a special range). An undetected invalid bid can compromise correctness and fairness of the auction. Unfortunately, validity verification of the bids is ignored in the auction schemes employing homomorphic secret sharing (called homomorphic auction in this paper). In this paper, an attack against the homomorphic auction in the absence of bid validity check is presented and a necessary bid validity check mechanism is proposed. Then a batch cryptographic technique is introduced and applied to improve the efficiency of bid validity check.

Selecting an IMC Career: Influences, Choices and Destinations

Integrated marketing communication incorporates both customer and non-customer stakeholder groups. While the literature commonly refers to this distinction as marketing communication and corporate communication, respectively, and practitioners accept the need for these roles, this study aims to explore the student perspective. US-based research suggests that students are more interested in marketing communication activities such as promotion that target customer stakeholders, and less interested in corporate communication activities that target non-customer stakeholders including employees, investors, and government (Bowen, 2003). The findings of this study match its US counterpart, and present implications for both the education and practice of marketing communication

The generation of virtual prototypes for performance optimization

I am suspicious of tools without a purpose - tools that are not developed in response to a clearly defined problem. Of course tools without a purpose can still be useful. However the development of first generation CAD was seriously impeded because the solution came before the problem. We are in danger of repeating this mistake if we do not clarify the nature of the problem that we are trying to solve with the next generation of tools. Back in the 1980s I used to add a postscript slide at the end of CAD conference presentations and the applause would invariably turn to concern. The slide simple asked: can anyone remember what it was about design that needed aiding before we had computer aided design?

Adolescence: A useful concept for this millennium

‘Adolescence’ has become increasingly recognised as a nebulous concept. Previous conceptualisations of adolescence have adopted a ‘deficit’ view, regarding teenagers as ‘unfinished’ adults. The deficit view of adolescence is highly problematic in an era where adulthood itself is difficult to define. The terms ‘kidult’ or ‘adultescent’ have emerged to describe adult-age people whose interests and priorities match those of their teenage counterparts. Rather than relying on ‘lock-step’ models of physical, cognitive and social growth put forward by developmental psychology, adolescence can be more usefully defined by looking at the common experiences of people in their teenage years. Common experiences arise at an institutional level; for example, all adolescents are treated as the same by legal and education systems. The transition from primary to secondary schooling is a milestone for all children, exposing them to a new type of educational environment. Shared experiences also arise from generational factors. Today’s adolescents belong to the millennial generation, characterised by technological competence, global perspectives, high susceptibility to media influence, individualisation and rapid interactions. This generation focuses on teamwork, achievement, modesty and good conduct, and has great potential for significant collective accomplishments. These generational factors challenge educators to provide relevant learning experiences for today’s students. Many classrooms still utilise textbook-based pedagogy more suited to previous generations, resulting in disengagement among millennial students. Curriculum content must also be tailored to generational needs. The rapid pace of change, as well as the fluidity of identity created by dissolving geographical and vocational boundaries, mean that the millennial generation will need more than a fixed set of skills and knowledge to enter adulthood. Teachers must enable their students to think like ‘expert novices’, adept at assimilating new concepts in depth and prepared to engage in lifelong learning.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

Improvements in sustainable energy and water practice in the food processing industry : an in depth analysis of the manufacture of Ghee at the Butter Producers' Cooperative Federation Limited, Brisbane

This thesis is a documented energy audit and long term study of energy and water reduction in a ghee factory. Global production of ghee exceeds 4 million tonnes annually. The factory in this study refines dairy products by non-traditional centrifugal separation and produces 99.9% pure, canned, crystallised Anhydrous Milk Fat (Ghee). Ghee is traditionally made by batch processing methods. The traditional method is less efficient, than centrifugal separation. An in depth systematic investigation was conducted of each item of major equipment including; ammonia refrigeration, a steam boiler, canning equipment, pumps, heat exchangers and compressed air were all fine-tuned. Continuous monitoring of electrical usage showed that not every initiative worked, others had pay back periods of less than a year. In 1994-95 energy consumption was 6,582GJ and in 2003-04 it was 5,552GJ down 16% for a similar output. A significant reduction in water usage was achieved by reducing the airflow in the refrigeration evaporative condensers to match the refrigeration load. Water usage has fallen 68% from18ML in 1994-95 to 5.78ML in 2003-04. The methods reported in this thesis could be applied to other industries, which have similar equipment, and other ghee manufacturers.

Examining the antecedents and consequences of ritualized fan behavior [Abstract]

One of the features of the sporting industry is the ritualized way in which it is consumed across the world. Fans of every sport have rituals and superstitions to help them enjoy the spectacle, socialize with other like-minded fans, and reduce some of the anxiety of watching their team play. These rituals include dress, barracking styles and pre and post match behaviors. What is not known are the factors that lead fans to engage in ritual behaviors and what relationship rituals have with desirable outcomes such as increased attendance, attitudinal loyalty or satisfaction. Given that some ritual behaviors are clearly undesirable, (e.g., hooliganism), understanding these relationships is important to managers who may be questioning whether rituals should be encouraged. Although ritualized behavior amongst fans is clearly visible, the symbolic and emotional nature of ritual poses challenges to researchers. Most previous ritual research is exploratory and qualitative in nature. This study, however, uses a behavior-based scale to measure fan ritual and relates it to desirable outcomes such as commitment and attendance. Over 2,000 season ticket holders of a football (soccer) team in Australia’s professional A-League competition were surveyed to investigate the antecedents and consequences of fan ritual behavior. Cluster analysis was used to explore the characteristics of respondents, and it revealed that those fans that engage in ritual behavior also differed on many other demographic and attitudinal dimensions. The associations between ritual and psychological commitment, and ritual and attendance are positive and significant. When used in conjunction with other constructs, fan ritual also improves the explanation of attendance behavior. The findings support previous research that found a significant and positive relationship between team identification, involvement and attendance, and extend previous research by finding a significant and positive relationship between rituals and attendance. For sports marketing practitioners, the results indicate the importance of developing and managing consumption rituals tied to game day attendance, with a view to generating uncommon loyalty.

Adaptive load shedding and regional protection

The requirement for improved efficiency whilst maintaining system security necessitates the development of improved system analysis approaches and the development of advanced emergency control technologies. Load shedding is a type of emergency control that is designed to ensure system stability by curtailing system load to match generation supply. This paper presents a new adaptive load shedding scheme that provides emergency protection against excess frequency decline, whilst minimizing the risk of line overloading. The proposed load shedding scheme uses the local frequency rate information to adapt the load shedding behaviour to suit the size and location of the experienced disturbance. The proposed scheme is tested in simulation on a 3-region, 10-generator sample system and shows good performance.

Supply chain sustainability: a relationship management approach

Purpose: In this research we examined, by means of case studies, the mechanisms by which relationships can be managed and by which communication and cooperation can be enhanced in sustainable supply chains. The research was predicated on the contention that the development of a sustainable supply chain depends, in part, on the transfer of knowledge and capabilities from the larger players in the supply chain. Design/Methodology/Approach: The research adopted a triangulated approach in which quantitative data were collected by questionnaire, interviews were conducted to explore and enrich the quantitative data and case studies were undertaken in order to illustrate and validate the findings. Handy‟s (1985) view of organisational culture, Allen & Meyer‟s (1990) concepts of organisational commitment and Van de Ven & Ferry‟s (1980) measures of organisational structuring have been combined into a model to test and explain how collaborative mechanisms can affect supply chain sustainability. Findings: It has been shown that the degree of match and mismatch between organisational culture and structure has an impact on staff‟s commitment level. A sustainable supply chain depends on convergence – that is the match between organisational structuring, organisation culture and organisation commitment. Research Limitations/implications: The study is a proof of concept and three case studies have been used to illustrate the nature of the model developed. Further testing and refinement of the model in practice should be the next step in this research. Practical implications: The concept of relationship management needs to filter down to all levels in the supply chain if participants are to retain commitment and buy-in to the relationship. A sustainable supply chain requires proactive relationship management and the development of an appropriate organisational culture, and trust. By legitimising individuals‟ expectations of the type of culture which is appropriate to their company and empowering employees to address mismatches that may occur a situation can be created whereby the collaborating organisations develop their competences symbiotically and so facilitate a sustainable supply chain. Originality/value: The culture/commitment/structure model developed from three separate strands of management thought has proved to be a powerful tool for analysing collaboration in supply chains and explaining how and why some supply chains are sustainable, and others are not.

Attitudes and interests towards biotechnology: The mismatch between students and teachers

Increasing the scientific literacy of Australians has become an educational priority in recent times. The ‘Science State – Smart State’ initiative of the Queensland Government involves an action plan for improving science education that includes a Science for Life action. A desired outcome is for an increased understanding of the natural world so that responsible decisions concerning our future wellbeing can be made in an age of science and technology. Biotechnology is a technology that is having profound impact on our lives. This paper describes how 15-16 year old students and biology teachers revealed a mismatch in both attitudes and interests towards biotechnology between the students and teachers. The findings are of interest as the teachers are writing biotechnology into their work programs in response to new syllabus documents. The teacher’s areas of interest did not match those of the students, possibly resulting in a curriculum the teachers want to teach, but the students do not want to learn.

Information enrichment for quality recommender systems

The explosive growth of the World-Wide-Web and the emergence of ecommerce are the major two factors that have led to the development of recommender systems (Resnick and Varian, 1997). The main task of recommender systems is to learn from users and recommend items (e.g. information, products or books) that match the users’ personal preferences. Recommender systems have been an active research area for more than a decade. Many different techniques and systems with distinct strengths have been developed to generate better quality recommendations. One of the main factors that affect recommenders’ recommendation quality is the amount of information resources that are available to the recommenders. The main feature of the recommender systems is their ability to make personalised recommendations for different individuals. However, for many ecommerce sites, it is difficult for them to obtain sufficient knowledge about their users. Hence, the recommendations they provided to their users are often poor and not personalised. This information insufficiency problem is commonly referred to as the cold-start problem. Most existing research on recommender systems focus on developing techniques to better utilise the available information resources to achieve better recommendation quality. However, while the amount of available data and information remains insufficient, these techniques can only provide limited improvements to the overall recommendation quality. In this thesis, a novel and intuitive approach towards improving recommendation quality and alleviating the cold-start problem is attempted. This approach is enriching the information resources. It can be easily observed that when there is sufficient information and knowledge base to support recommendation making, even the simplest recommender systems can outperform the sophisticated ones with limited information resources. Two possible strategies are suggested in this thesis to achieve the proposed information enrichment for recommenders: • The first strategy suggests that information resources can be enriched by considering other information or data facets. Specifically, a taxonomy-based recommender, Hybrid Taxonomy Recommender (HTR), is presented in this thesis. HTR exploits the relationship between users’ taxonomic preferences and item preferences from the combination of the widely available product taxonomic information and the existing user rating data, and it then utilises this taxonomic preference to item preference relation to generate high quality recommendations. • The second strategy suggests that information resources can be enriched simply by obtaining information resources from other parties. In this thesis, a distributed recommender framework, Ecommerce-oriented Distributed Recommender System (EDRS), is proposed. The proposed EDRS allows multiple recommenders from different parties (i.e. organisations or ecommerce sites) to share recommendations and information resources with each other in order to improve their recommendation quality. Based on the results obtained from the experiments conducted in this thesis, the proposed systems and techniques have achieved great improvement in both making quality recommendations and alleviating the cold-start problem.

Human cortical processing of colour and pattern

Investigated human visual processing of simple two-colour patterns using a delayed match to sample paradigm with positron emission tomography (PET). This study is unique in that the authors specifically designed the visual stimuli to be the same for both pattern and colour recognition with all patterns being abstract shapes not easily verbally coded composed of two-colour combinations. The authors did this to explore those brain regions required for both colour and pattern processing and to separate those areas of activation required for one or the other. 10 right-handed male volunteers aged 18–35 yrs were recruited. The authors found that both tasks activated similar occipital regions, the major difference being more extensive activation in pattern recognition. A right-sided network that involved the inferior parietal lobule, the head of the caudate nucleus, and the pulvinar nucleus of the thalamus was common to both paradigms. Pattern recognition also activated the left temporal pole and right lateral orbital gyrus, whereas colour recognition activated the left fusiform gyrus and several right frontal regions.

The self-Googling phenomenon : investigating the performance of personalized information resources

This paper investigates self–Googling through the monitoring of search engine activities of users and adds to the few quantitative studies on this topic already in existence. We explore this phenomenon by answering the following questions: To what extent is the self–Googling visible in the usage of search engines; is any significant difference measurable between queries related to self–Googling and generic search queries; to what extent do self–Googling search requests match the selected personalised Web pages? To address these questions we explore the theory of narcissism in order to help define self–Googling and present the results from a 14–month online experiment using Google search engine usage data.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).