The performance in context model: A 21st century tertiary dance teaching pedagogy

Despite tertiary institutions acknowledging that reflective practice is an essential component of undergraduate dance teacher training, there is often a disparity between the tertiary students’ reflective skills and the more sophisticated reflective ability needed to navigate the 21st century workforce (Silva 2008). This paper charts the evolution of a dance teaching reflective pedagogy within a suite of three units across a three-year undergraduate dance teacher-training course for school, community and studio dance teachers. This reflective pedagogy based on exploration, collaboration, critical questioning and connections with community forms the basis of a model of tertiary dance teacher- training; the Performance in Context Model (PCM). Over the past four years, through four cycles of action research, the PCM pedagogy, context and engagement with community has developed into a successful model integrating practical dance teaching skills, artistry and community engagement. The PCM represents a holistic collaborative approach to dance teacher education: the marrying of ‘teacher-as-artist’, ‘teacher-as-performer’ and ‘teacher-as-researcher’. More specifically, it emphasises the need for mature, reflective, receptive and flexible approaches in response to dance teaching and learning. These are enacted in a variety of contexts, with tertiary dance teaching students identifying as teaching artists, as well as researchers of their own practice.

Efficient safety message dissemination for cooperative collision warning via context modelling

A Cooperative Collision Warning System (CCWS) is an active safety techno- logy for road vehicles that can potentially reduce traffic accidents. It provides a driver with situational awareness and early warnings of any possible colli- sions through an on-board unit. CCWS is still under active research, and one of the important technical problems is safety message dissemination. Safety messages are disseminated in a high-speed mobile environment using wireless communication technology such as Dedicated Short Range Communication (DSRC). The wireless communication in CCWS has a limited bandwidth and can become unreliable when used inefficiently, particularly given the dynamic nature of road traffic conditions. Unreliable communication may significantly reduce the performance of CCWS in preventing collisions. There are two types of safety messages: Routine Safety Messages (RSMs) and Event Safety Messages (ESMs). An RSM contains the up-to-date state of a vehicle, and it must be disseminated repeatedly to its neighbouring vehicles. An ESM is a warning message that must be sent to all the endangered vehi- cles. Existing RSM and ESM dissemination schemes are inefficient, unscalable, and unable to give priority to vehicles in the most danger. Thus, this study investigates more efficient and scalable RSM and ESM dissemination schemes that can make use of the context information generated from a particular traffic scenario. Therefore, this study tackles three technical research prob- lems, vehicular traffic scenario modelling and context information generation, context-aware RSM dissemination, and context-aware ESM dissemination. The most relevant context information in CCWS is the information about possible collisions among vehicles given a current vehicular traffic situation. To generate the context information, this study investigates techniques to model interactions among multiple vehicles based on their up-to-date motion state obtained via RSM. To date, there is no existing model that can represent interactions among multiple vehicles in a speciffic region and at a particular time. The major outcome from the first problem is a new interaction graph model that can be used to easily identify the endangered vehicles and their danger severity. By identifying the endangered vehicles, RSM and ESM dis- semination can be optimised while improving safety at the same time. The new model enables the development of context-aware RSM and ESM dissemination schemes. To disseminate RSM efficiently, this study investigates a context-aware dis- semination scheme that can optimise the RSM dissemination rate to improve safety in various vehicle densities. The major outcome from the second problem is a context-aware RSM dissemination protocol. The context-aware protocol can adaptively adjust the dissemination rate based on an estimated channel load and danger severity of vehicle interactions given by the interaction graph model. Unlike existing RSM dissemination schemes, the proposed adaptive scheme can reduce channel congestion and improve safety by prioritising ve- hicles that are most likely to crash with other vehicles. The proposed RSM protocol has been implemented and evaluated by simulation. The simulation results have shown that the proposed RSM protocol outperforms existing pro- tocols in terms of efficiency, scalability and safety. To disseminate ESM efficiently, this study investigates a context-aware ESM dissemination scheme that can reduce unnecessary transmissions and deliver ESMs to endangered vehicles as fast as possible. The major outcome from the third problem is a context-aware ESM dissemination protocol that uses a multicast routing strategy. Existing ESM protocols use broadcast rout- ing, which is not efficient because ESMs may be sent to a large number of ve- hicles in the area. Using multicast routing improves efficiency because ESMs are sent only to the endangered vehicles. The endangered vehicles can be identified using the interaction graph model. The proposed ESM protocol has been implemented and evaluated by simulation. The simulation results have shown that the proposed ESM protocol can prevent potential accidents from occurring better than existing ESM protocols. The context model and the RSM and ESM dissemination protocols can be implemented in any CCWS development to improve the communication and safety performance of CCWS. In effect, the outcomes contribute to the realisation of CCWS that will ultimately improve road safety and save lives.

Robust suspicious behaviour detection for smart surveillance systems

Video surveillance technology, based on Closed Circuit Television (CCTV) cameras, is one of the fastest growing markets in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. To overcome this limitation, it is necessary to have “intelligent” processes which are able to highlight the salient data and filter out normal conditions that do not pose a threat to security. In order to create such intelligent systems, an understanding of human behaviour, specifically, suspicious behaviour is required. One of the challenges in achieving this is that human behaviour can only be understood correctly in the context in which it appears. Although context has been exploited in the general computer vision domain, it has not been widely used in the automatic suspicious behaviour detection domain. So, it is essential that context has to be formulated, stored and used by the system in order to understand human behaviour. Finally, since surveillance systems could be modeled as largescale data stream systems, it is difficult to have a complete knowledge base. In this case, the systems need to not only continuously update their knowledge but also be able to retrieve the extracted information which is related to the given context. To address these issues, a context-based approach for detecting suspicious behaviour is proposed. In this approach, contextual information is exploited in order to make a better detection. The proposed approach utilises a data stream clustering algorithm in order to discover the behaviour classes and their frequency of occurrences from the incoming behaviour instances. Contextual information is then used in addition to the above information to detect suspicious behaviour. The proposed approach is able to detect observed, unobserved and contextual suspicious behaviour. Two case studies using video feeds taken from CAVIAR dataset and Z-block building, Queensland University of Technology are presented in order to test the proposed approach. From these experiments, it is shown that by using information about context, the proposed system is able to make a more accurate detection, especially those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information give critical feedback to the system designers to refine the system. Finally, the proposed modified Clustream algorithm enables the system to both continuously update the system’s knowledge and to effectively retrieve the information learned in a given context. The outcomes from this research are: (a) A context-based framework for automatic detecting suspicious behaviour which can be used by an intelligent video surveillance in making decisions; (b) A modified Clustream data stream clustering algorithm which continuously updates the system knowledge and is able to retrieve contextually related information effectively; and (c) An update-describe approach which extends the capability of the existing human local motion features called interest points based features to the data stream environment.

The voices of the exegesis

In a recent journal article, Luke Jaaniste and I identified an emergent model of exegesis. From a content analysis of submitted exegeses within a local archive, we identified an approach that is quite different from the traditional thesis, but is also distinct from previously identified forms of exegesis, which Milech and Schilo have described as a ‘context model’ (which assumes the voice of academic objectivity and provides an historical or theoretical context for the creative practice) and a ‘commentary’ model’ (which takes the form of a first person reflection on the challenges, insights and achievements of the practice). The model we identified combines these dichotomous forms and assumes a dual orientation–looking outwards to the established field of research, exemplars and theories, and inwards to the methodologies, processes and outcomes of the practice. We went on to argue that this ‘connective’ exegesis offers clear benefits to the researcher in connecting the practice to an established field while allowing the researcher to demonstrate how the methods have led to outcomes that advance the field in some way. And, while it helps the candidate to articulate objective claims for research innovation, it enables them to retain a voiced, personal relationship with their practice. However, it also poses considerable complexities and challenges in the writing. It requires a reconciliation of multi-perspectival subject positions: the disinterested perspective and academic objectivity of an observer/ethnographer/analyst/theorist at times and the invested perspective of the practitioner/ producer at others. The author must also contend with a range of writing styles, speech genres and voices: from the formal, polemical voice of the theorist to the personal, questioning and sometimes emotive voice of reflexivity. Moreover, the connective exegesis requires the researcher to synthesize various perspectives, subject positions, writing styles, and voices into a unified and coherent text. In this paper I consider strategies for writing a hybrid, connective exegesis. I first ground the discussion on polyvocality and alternate textual structures through reference to recent discussions in philosophy and critical theory, and point to examples of emergent approaches to texts and practices in related fields. I then return to the collection of archived exegeses to investigate the strategies that postgraduate candidates have adopted to resolve the problems that arise from a polyvocal, connective exegesis.

Rule-based approach for identifying assertions in clinical free-text data

A rule-based approach for classifying previously identified medical concepts in the clinical free text into an assertion category is presented. There are six different categories of assertions for the task: Present, Absent, Possible, Conditional, Hypothetical and Not associated with the patient. The assertion classification algorithms were largely based on extending the popular NegEx and Context algorithms. In addition, a health based clinical terminology called SNOMED CT and other publicly available dictionaries were used to classify assertions, which did not fit the NegEx/Context model. The data for this task includes discharge summaries from Partners HealthCare and from Beth Israel Deaconess Medical Centre, as well as discharge summaries and progress notes from University of Pittsburgh Medical Centre. The set consists of 349 discharge reports, each with pairs of ground truth concept and assertion files for system development, and 477 reports for evaluation. The system’s performance on the evaluation data set was 0.83, 0.83 and 0.83 for recall, precision and F1-measure, respectively. Although the rule-based system shows promise, further improvements can be made by incorporating machine learning approaches.

A typology of music distribution models

A typology of music distribution models is proposed consisting of the ownership model, the access model, and the context model. These models are not substitutes for each other and may co‐exist serving different market niches. The paper argues that increasingly the economic value created from recorded music is based on con‐text rather than on ownership. During this process, access‐based services temporarily generate economic value, but such services are destined to eventually become commoditised.

Dance teacher education in the 21st century : Linking cultural and aesthetic practice

This paper describes the implementation of the recommendations of a series of research projects, within an undergraduate dance teacher-training course, into the training of collaborative, empathetic, ethical and creative dance teachers. Banks’s Dimensions for Multicultural Education (Banks, 1993) was used as a lens to analyze the design and delivery of cultural dance activities within a university dance-teaching unit, implemented in Australia and Timor Leste, and to reflect on the adaptability of the Performance in Context Model (Stevens & Huddy, in press) across different cultural contexts. Content and contextual knowledge, transformational learning pedagogy, teaching for equity and empathy development were explored through a culturally responsive teaching and learning unit, supported by critical analysis and reflection. This analysis identified a number of key understandings in relation to the design and delivery of cultural dance activities.

Applying Keller’s brand equity model in a B2B context : limitations and an empirical test

It has been recognised that brands play a role in industrial markets, but to date a comprehensive model of business-to-business (B2B) branding does not exist, nor has there been an empirical study of the applicability of a full brand equity model in a B2B context. This paper is the first to begin to address these issues. The paper introduces the Customer- Based Brand Equity (CBBE) model by Kevin Keller (1993; 2001; 2003), and empirically tests its applicability in the market of electronic tracking systems for waste management. While Keller claims that the CBBE pyramid can be applied in a B2B context, this research highlights challenges of such an application, and suggests changes to the model are required. Assessing the equity of manufacturers’ brand names is more appropriate than measuring the equity of individual product brands as suggested by Keller. Secondly, the building blocks of Keller’s model appear useful in an organisational context, although differences in the subdimensions are required. Brand feelings appear to lack relevance in the industrial market investigated, and the pinnacle of Keller’s pyramid, resonance, needs serious modifications. Finally, company representatives play a role in building brand equity, indicating a need for this human element to be recognised in a B2B model.

An application of Keller’s brand equity model in a B2B context

Purpose – The importance of branding in industrial contexts has increased, yet a comprehensive model of business-to-business (B2B) branding does not exist, nor has there been a thoroughempirical study of the applicability of a full brand equitymodel in a B2B context. This paper aims to discuss the suitability and limitations of Keller’s customer-based brand equity model and tests its applicability in a B2B market. Design/methodology/approach – The study involved the use of semi-structured interviews with senior buyers of technology for electronic tracking of waste management. Findings – Findings suggest that amongst organisational buyers there is a much greater emphasis on the selling organisation, including its corporate brand, credibility and staff, than on individual brands and their associated dimensions. Research limitations/implications – The study investigates real brands with real potential buyers, so there is a risk that the results may represent industry-specific factors that are not representative of all B2B markets. Future research that validates the importance of the Keller elements in other industrial marketing contexts would be beneficial. Practical implications – The findings are relevant for marketing practitioners, researchers and managers as a starting-point for their B2B brand equity research. Originality/value – Detailed insights and key lessons from the field with regard to how B2B brand equity should be conceptualised and measured are offered. A revised brand equity model for B2B application is also presented.

Airports in their urban settings : towards a conceptual model of interfaces in the Australian context

In Australia, airports have emerged as important sub-regional activity centres and now pose challenges for both airport operation and planning in the surrounding urban and regional environment. The changing nature of airports in their metropolitan context and the emergence of new pressures and problems require the introduction of a fresh conceptual framework to assist the better understanding of these complex roles and spatial interactions. The approach draws upon the meta-concept of interfaces of an ‘airport metropolis’ as an organising device consisting of four main domains: economic development, land use,infrastructure, and governance. The paper uses the framework to further discuss airport and regional interactions and highlights the use of sustainability criteria to operationalise the model. The approach aims to move research and practice beyond the traditionally compartmentalised analysis of airport issues and policy-making by highlighting interdependencies between airports and regions.

An application of Keller’s Brand Equity Model in a B2B context

Powerful brands create meaningful images in the minds of customers (Keller, 1993). A strong brand image and reputation enhances differentiation and has a positive influence on buying behaviour (Gordon et al., 1993; McEnally and de Chernatony, 1999). While the power of branding is widely acknowledged in consumer markets, the nature and importance of branding in industrial markets remains under-researched. Many business-to-business (B2B) strategists have claimed brand-building belongs in the consumer realm. They argue that industrial products do not need branding as it is confusing and adds little value to functional products (Collins, 1977; Lorge, 1998; Saunders and Watt, 1979). Others argue that branding and the concept of brand equity however are increasingly important in industrial markets, because it has been shown that what a brand means to a buyer can be a determining factor in deciding between industrial purchase alternatives (Aaker, 1991). In this context, it is critical for suppliers to initiate and sustain relationships due to the small number of potential customers (Ambler, 1995; Webster and Keller, 2004). To date however, there is no model available to assist B2B marketers in identifying and measuring brand equity. In this paper, we take a step in that direction by operationalising and empirically testing a prominent brand equity model in a B2B context. This makes not only a theoretical contribution by advancing branding research, but also addresses a managerial need for information that will assist in the assessment of industrial branding efforts.

A suspicious behaviour detection using a context space model for smart surveillance systems

Video surveillance systems using Closed Circuit Television (CCTV) cameras, is one of the fastest growing areas in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. This work attempts to address these problems by proposing an automatic suspicious behaviour detection which utilises contextual information. The utilisation of contextual information is done via three main components: a context space model, a data stream clustering algorithm, and an inference algorithm. The utilisation of contextual information is still limited in the domain of suspicious behaviour detection. Furthermore, it is nearly impossible to correctly understand human behaviour without considering the context where it is observed. This work presents experiments using video feeds taken from CAVIAR dataset and a camera mounted on one of the buildings Z-Block) at the Queensland University of Technology, Australia. From these experiments, it is shown that by exploiting contextual information, the proposed system is able to make more accurate detections, especially of those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information gives critical feedback to the system designers to refine the system.

A context sensitive model for querying linked scientific data

This thesis provides a query model suitable for context sensitive access to a wide range of distributed linked datasets which are available to scientists using the Internet. The model is designed based on scientific research standards which require scientists to provide replicable methods in their publications. Although there are query models available that provide limited replicability, they do not contextualise the process whereby different scientists select dataset locations based on their trust and physical location. In different contexts, scientists need to perform different data cleaning actions, independent of the overall query, and the model was designed to accommodate this function. The query model was implemented as a prototype web application and its features were verified through its use as the engine behind a major scientific data access site, Bio2RDF.org. The prototype showed that it was possible to have context sensitive behaviour for each of the three mirrors of Bio2RDF.org using a single set of configuration settings. The prototype provided executable query provenance that could be attached to scientific publications to fulfil replicability requirements. The model was designed to make it simple to independently interpret and execute the query provenance documents using context specific profiles, without modifying the original provenance documents. Experiments using the prototype as the data access tool in workflow management systems confirmed that the design of the model made it possible to replicate results in different contexts with minimal additions, and no deletions, to query provenance documents.

A Context Space Model for Detecting Anomalous Behaviour in Video Surveillance

Having a good automatic anomalous human behaviour detection is one of the goals of smart surveillance systems’ domain of research. The automatic detection addresses several human factor issues underlying the existing surveillance systems. To create such a detection system, contextual information needs to be considered. This is because context is required in order to correctly understand human behaviour. Unfortunately, the use of contextual information is still limited in the automatic anomalous human behaviour detection approaches. This paper proposes a context space model which has two benefits: (a) It provides guidelines for the system designers to select information which can be used to describe context; (b)It enables a system to distinguish between different contexts. A comparative analysis is conducted between a context-based system which employs the proposed context space model and a system which is implemented based on one of the existing approaches. The comparison is applied on a scenario constructed using video clips from CAVIAR dataset. The results show that the context-based system outperforms the other system. This is because the context space model allows the system to considering knowledge learned from the relevant context only.