Determining operational measures from multi-camera surveillance systems using soft biometrics

CCTV and surveillance networks are increasingly being used for operational as well as security tasks. One emerging area of technology that lends itself to operational analytics is soft biometrics. Soft biometrics can be used to describe a person and detect them throughout a sparse multi-camera network. This enables them to be used to perform tasks such as determining the time taken to get from point to point, and the paths taken through an environment by detecting and matching people across disjoint views. However, in a busy environment where there are 100's if not 1000's of people such as an airport, attempting to monitor everyone is highly unrealistic. In this paper we propose an average soft biometric, that can be used to identity people who look distinct, and are thus suitable for monitoring through a large, sparse camera network. We demonstrate how an average soft biometric can be used to identify unique people to calculate operational measures such as the time taken to travel from point to point.

Towards a service framework for remote sales support via augmented reality

Real-time sales assistant service is a problematic component of remote delivery of sales support for customers. Solutions involving web pages, telephony and video support prove problematic when seeking to remotely guide customers in their sales processes, especially with transactions revolving around physically complex artefacts. This process involves a number of services that are often complex in nature, ranging from physical compatibility and configuration factors, to availability and credit services. We propose the application of a combination of virtual worlds and augmented reality to create synthetic environments suitable for remote sales of physical artefacts, right in the home of the purchaser. A high level description of the service structure involved is shown, along with a use case involving the sale of electronic goods and services within an example augmented reality application. We expect this work to have application in many sales domains involving physical objects needing to be sold over the Internet.

New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry

This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell tower location as well as the activity type and usage quantity (e.g., voice call with duration in seconds); and the research focuses are on customers’ spatial and temporal usage behaviour. The main methodological emphasis is on the development of clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational Bayesian (VB) method. VB is an efficient deterministic alternative to the popular but computationally demandingMarkov chainMonte Carlo (MCMC) methods. The standard VBGMMalgorithm is extended by allowing component splitting such that it is robust to initial parameter choices and can automatically and efficiently determine the number of components. The new algorithm we propose allows more effective modelling of individuals’ highly heterogeneous and spiky spatial usage behaviour, or more generally human mobility patterns; the term spiky describes data patterns with large areas of low probability mixed with small areas of high probability. Customers are then characterised and segmented based on the fitted GMM which corresponds to how each of them uses the products/services spatially in their daily lives; this is essentially their likely lifestyle and occupational traits. Other significant research contributions include fitting GMMs using VB to circular data i.e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of VB-GMM.

Train service timetabling in railway open markets by particle swarm optimisation

Railway timetabling is an important process in train service provision as it matches the transportation demand with the infrastructure capacity while customer satisfaction is also considered. It is a multi-objective optimisation problem, in which a feasible solution, rather than the optimal one, is usually taken in practice because of the time constraint. The quality of services may suffer as a result. In a railway open market, timetabling usually involves rounds of negotiations among a number of self-interested and independent stakeholders and hence additional objectives and constraints are imposed on the timetabling problem. While the requirements of all stakeholders are taken into consideration simultaneously, the computation demand is inevitably immense. Intelligent solution-searching techniques provide a possible solution. This paper attempts to employ a particle swarm optimisation (PSO) approach to devise a railway timetable in an open market. The suitability and performance of PSO are studied on a multi-agent-based railway open-market negotiation simulation platform.

The emergent male shopper : an identification of male supermarket shopper types

Grocery shopping is a routine activity widely considered the responsibility of the female spouse, yet modern social and demographic shifts are causing men to engage in this task. This study develops a retail shopping typology of male grocery shoppers, employing a cluster analysis technique. Five distinct cohorts emerge from the data of eight constructs, measured by seventy one items. One new shopper type emerges from this research. This shopper presented as a younger man, at the commencement of their family lifecycle, attracted by a strong value offer, focusing on price and promotional discounts. Our research offers a contribution to the marketing, consumer behaviour and supermarket retailing disciplines in three ways. By examining and identifying male shopping behaviour in the context of grocery shopping, the development of a retail shopping typology of male grocery shoppers and the extension and employment of a cluster analysis in identifying distinct groups. This research has implications for gender, segmentation studies and consumer behaviour disciplines in regard to grocery shopping. The identification of specific groups of male grocery shoppers will enable grocery retailers to effectively implement important, targeted marketing strategies.

QoS-aware web service composition using genetic algorithms

Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition — by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers’ expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services.

An innovative approach to reducing risks associated with infant feeding : the use of technology

Infant feeding is a complex behavior enacted in a risk adverse society. Despite ongoing communication and education strategies, breastfeeding rates in countries like Australia, the US, and the UK remain static, thus increasing the risk of short and long-term health problems. Health professionals and non-profit organizations recognize social marketing as an appropriate strategy for increasing breastfeeding duration since it addresses the shortfalls of education-only campaigns. Technology as an innovative alternative to mass media and education has the potential to reduce the social price of breastfeeding by assisting women to manage the identity and health risks associated with infant feeding. This paper reports findings from six focus groups that explored the risks associated with breastfeeding and the potential role of technology in ameliorating these risks. A key finding of this research was that technology has the potential to negate the impact of perceived uncertainty and lack of control associated with breastfeeding. The results indicated that future breastfeeding campaigns that are innovative in their approach and use technology may be more effective in changing breastfeeding behavior.

Scene invariant crowd counting and crowd occupancy analysis

In public places, crowd size may be an indicator of congestion, delay, instability, or of abnormal events, such as a fight, riot or emergency. Crowd related information can also provide important business intelligence such as the distribution of people throughout spaces, throughput rates, and local densities. A major drawback of many crowd counting approaches is their reliance on large numbers of holistic features, training data requirements of hundreds or thousands of frames per camera, and that each camera must be trained separately. This makes deployment in large multi-camera environments such as shopping centres very costly and difficult. In this chapter, we present a novel scene-invariant crowd counting algorithm that uses local features to monitor crowd size. The use of local features allows the proposed algorithm to calculate local occupancy statistics, scale to conditions which are unseen in the training data, and be trained on significantly less data. Scene invariance is achieved through the use of camera calibration, allowing the system to be trained on one or more viewpoints and then deployed on any number of new cameras for testing without further training. A pre-trained system could then be used as a ‘turn-key’ solution for crowd counting across a wide range of environments, eliminating many of the costly barriers to deployment which currently exist.

Feedback information and consumer motivation : the moderation role of positive and negative reference values in self-regulation

Marketers spend considerable resources to motivate people to consume their products and services as a means of goal attainment (Bagozzi and Dholakia, 1999). Why people increase, decrease, or stop consuming some products is based largely on how well they perceive they are doing in pursuit of their goals (Carver and Scheier, 1992). Yet despite the importance for marketers in understanding how current performance influences a consumer’s future efforts, this topic has received little attention in marketing research. Goal researchers generally agree that feedback about how well or how poorly people are doing in achieving their goals affects their motivation (Bandura and Cervone, 1986; Locke and Latham, 1990). Yet there is less agreement about whether positive and negative performance feedback increases or decreases future effort (Locke and Latham, 1990). For instance, while a customer of a gym might cancel his membership after receiving negative feedback about his fitness, the same negative feedback might cause another customer to visit the gym more often to achieve better results. A similar logic can apply to many products and services from the use of cosmetics to investing in mutual funds. The present research offers managers key insights into how to engage customers and keep them motivated. Given that connecting customers with the company is a top research priority for managers (Marketing Science Institute, 2006), this article provides suggestions for performance metrics including four questions that managers can use to apply the findings.

Antecedents of Destination Brand Loyalty for a Long-Haul Market: Australia's Destination Loyalty Among Chilean Travelers

Limited extant research examines Latin American consumers' perceptions of holiday destinations. This article measures destination brand equity for Australia as a long-haul destination in the emerging Chilean market. Specifically, it develops a model of consumer-based brand equity (CBBE) to explain attitudinal destination loyalty. The proposed model is tested using data from a sample of Chilean travelers. The findings suggest that brand salience, brand image, and brand value are positively related to brand loyalty for Australia. Further, while brand salience for Australia is strong, as a long-haul destination the country faces significant challenges in converting awareness into intent to visit. Australia is a more compelling destination brand for previous visitors than non-visitors. This implies that a word-of-mouth recommendation from previous visitors, a key component of attitudinal loyalty, is a positive indicator of future growth opportunities for Australia's destination marketers to capitalize on.

The morphology of service bundling settings

The purpose of this paper is to advance our understanding of what contextual factors influence the service bundling process in an organizational setting. Although previous literature contains insights into the mechanisms underlying bundling and the artefacts for performing the bundling task itself, the body of knowledge seems to lack a comprehensive framework for analysing the actual scenario in which the bundling process is performed. This is required as the scenario will influence the bundling method and the IT support. We address this need by designing a morphological box for analysing bundling scenarios in different organizational settings. The factors featured in the box are systematised into a set of four categories of bundling layers which we identify from reviewing literature. The two core layers in the framework are the service bundling on a type level and on an instance level (i.e. configuration). To demonstrate the applicability and utility of the proposed morphological box, we apply it to assess the underlying differences and commonalities of two different bundling scenarios from the B2B and G2C sectors which stress the differences between bundling on a type and instance level. In addition, we identify several prospects for future research that can benefit from the proposed morphological box.

Ensuring Correctness During Process Configuration via Partner Synthesis

Variants of the same process can be encountered within one organization or across different organizations. For example, different municipalities, courts, and rental agencies all need to support highly similar processes. In fact, procurement and sales processes can be found in almost any organization. However, despite these similarities, there is also the need to allow for local variations in a controlled manner. Therefore, many academics and practitioners have advocated the use of configurable process models (sometimes referred to as reference models). A configurable process model describes a family of similar process models in a given domain. Such a model can be configured to obtain a specific process model that is subsequently used to handle individual cases, for instance, to process customer orders. Process configuration is notoriously difficult as there may be all kinds of interdependencies between configuration decisions. In fact, an incorrect configuration may lead to behavioral issues such as deadlocks and livelocks. To address this problem, we present a novel verification approach inspired by the “operating guidelines” used for partner synthesis. We view the configuration process as an external service, and compute a characterization of all such services which meet particular requirements via the notion of configuration guideline. As a result, we can characterize all feasible configurations (i. e., configurations without behavioral problems) at design time, instead of repeatedly checking each individual configuration while configuring a process model.

Drivers of satisfaction and dissatisfaction for overseas service consumers : a critical incident technique approach

This is one of the few studies in the academic literature that directly addresses inward exporting of customer services, which is a topic that has gained less attention from an international services marketing point of view. The objective of this study is to explore the drivers of satisfaction and dissatisfaction for overseas service customers of higher education in Australia. Critical incident technique (CIT) method was used to collect and analyse the data and a total of 107 critical incidents were collected. Findings from this study show that service satisfaction and dissatisfaction for international students derive from: elements of the core service (educational service performance), personal sources (international student performance), and the external environment (socialization and host environment performance). Additionally, results show that the drivers of satisfaction and dissatisfaction for international students are not necessarily the same. Limitations relating to the specific sector of higher education and the cross sectional natures of the data are addressed.

Inward internationalization of services : Exploring satisfaction and dissatisfaction of overseas students in Australia

Most research has assessed the outward internationalization process of service firms and less is known in the literature about the inward internationalization of services, or companies that provide the service to overseas customers in the domestic market (i.e., tourism, education, healthcare). Specifically, there is scant research looking at the overseas customer perspective. This study attempts to identify the main drivers of satisfaction and dissatisfaction for overseas customers. The critical incident technique (CIT) method was used to collect and analyse the data. A total of 107 critical incidents regarding drivers of satisfaction and dissatisfaction for overseas customers in a higher educational service context were collected. Findings of this study show that drivers of service satisfaction and dissatisfaction for overseas customers derive from elements of the core service and peripheral service. However, the findings show that elements of the peripheral service (living environment, socialization and interaction with others, and personal performance) are more important for international students.

Design led innovation as a means to sustain social innovation enterprises

This proposition challenges the notion that clean technology firms, who form part of the emerging social innovation enterprise sector, do not have the resources to gain value from Design Led innovation practices, due to their size and operational constraints. Much has been written on the benefits of linking design and design thinking to organisational strategy and business transformation. The term Design Led in the context of this proposition is defined as the tools and approaches which enable design thinking to be embedded as a cultural transformation within a business. Being Design Led requires a company to have a vision for top line growth within their business, which is based on deep customer insights and expanded through customer and stakeholder engagements, with the outcomes being mapped to all aspects of the business to enable the vision to be achieved.