Sleepiness : how a biological drive can influence other risky road user behaviours

The Safe System approach to road safety utilises a holistic view of the interactions among vehicles, roads and road users. Yet, the contribution of each of these factors to crashes is vastly different. The role of road users is widely acknowledged as an overwhelming contributor to road crashes. Substantial gains have been made with improvements to vehicle and roads over a number of years. However, improvements of the road user’s behaviour has been (in some cases) less substantial. A road user behaviour that is relatively unregulated is driver sleepiness, which is part of the ‘fatal five’ of risky road user behaviours. The effect of sleepiness is ubiquitous – sleepiness is a state that most, if not all drivers on our roads has experienced, and is habitually exposed to. The quality and quantity of daily sleep is integral to our level of neurobehavioural performance during wakefulness and as such can have a compounding effect on a number of other risky driving behaviours. This paper will discuss the potential influence of sleepiness as an interceding factor for a number of risky driving behaviours. Little effort has been given to increasing awareness of the deleterious and wide ranging effects that sleepiness has on road safety. Given the wide ranging influence of sleepiness, improvements of ‘sleep health’ as a protective factor at the community or individual level could lead to significant reductions in road trauma and increases of general well being. A discussion of potential actions to reduce sleepiness is required if reductions of road trauma are to continue.

A social matching system : using implicit and explicit information for personalized recommendation in online dating service

Online dating websites enable a specific form of social networking and their efficiency can be increased by supporting proactive recommendations based on participants' preferences with the use of data mining. This research develops two-way recommendation methods for people-to-people recommendation for large online social networks such as online dating networks. This research discovers the characteristics of the online dating networks and utilises these characteristics in developing efficient people-to-people recommendation methods. Methods developed support improved recommendation accuracy, can handle data sparsity that often comes with large data sets and are scalable for handling online networks with a large number of users.

Identifying the unknown in user space memory

This thesis is a study of how the contents of volatile memory on the Windows operating system can be better understood and utilised for the purposes of digital forensic investigations. It proposes several techniques to improve the analysis of memory, with a focus on improving the detection of unknown code such as malware. These contributions allow the creation of a more complete reconstruction of the state of a computer at acquisition time, including whether or not the computer has been infected by malicious code.

The multi-platform user engagement and data visualisation tool

The ability to identify and assess user engagement with transmedia productions is vital to the success of individual projects and the sustainability of this mode of media production as a whole. It is essential that industry players have access to tools and methodologies that offer the most complete and accurate picture of how audiences/users engage with their productions and which assets generate the most valuable returns of investment. Drawing upon research conducted with Hoodlum Entertainment, a Brisbane-based transmedia producer, this project involved an initial assessment of the way engagement tends to be understood, why standard web analytics tools are ill-suited to measuring it, how a customised tool could offer solutions, and why this question of measuring engagement is so vital to the future of transmedia as a sustainable industry. Working with data provided by Hoodlum Entertainment and Foxtel Marketing, the outcome of the study was a prototype for a custom data visualisation tool that allowed access, manipulation and presentation of user engagement data, both historic and predictive. The prototyped interfaces demonstrate how the visualization tool would collect and organise data specific to multiplatform projects by aggregating data across a number of platform reporting tools. Such a tool is designed to encompass not only platforms developed by the transmedia producer but also sites developed by fans. This visualisation tool accounted for multiplatform experience projects whose top level is comprised of people, platforms and content. People include characters, actors, audience, distributors and creators. Platforms include television, Facebook and other relevant social networks, literature, cinema and other media that might be included in the multiplatform experience. Content refers to discreet media texts employed within the platform, such as tweet, a You Tube video, a Facebook post, an email, a television episode, etc. Core content is produced by the creators’ multiplatform experiences to advance the narrative, while complimentary content generated by audience members offers further contributions to the experience. Equally important is the timing with which the components of the experience are introduced and how they interact with and impact upon each other. Being able to combine, filter and sort these elements in multiple ways we can better understand the value of certain components of a project. It also offers insights into the relationship between the timing of the release of components and user activity associated with them, which further highlights the efficacy (or, indeed, failure) of assets as catalysts for engagement. In collaboration with Hoodlum we have developed a number of design scenarios experimenting with the ways in which data can be visualised and manipulated to tell a more refined story about the value of user engagement with certain project components and activities. This experimentation will serve as the basis for future research.

Channel tracking in SDMA-based multi-user MIMO-OFDM communications systems

Capacity of current and future high data rate wireless communications depend significantly on how well changes in the wireless channel are predicted and tracked. Generally, this can be estimated by transmitting known symbols. However, this increases overheads if the channel varies over time. Given today’s bandwidth demand and the increased necessity for mobile wireless devices, the contributions of this research are very significant. This study has developed a novel and efficient channel tracking algorithm that can recursively update the channel estimation for wireless broadband communications reducing overheads, therefore increasing the speed of wireless communication systems.

Adding SUGAR : service user and carer collaboration in mental health nursing research

‘SUGAR: Service users and carers group advising on research’ is an exciting initiative established to develop collaborative working in mental health nursing research between mental health service users, carers, researchers and practitioners at City University London, UK. This paper will describe the background to SUGAR and how and why it was established; how the group operates; some of the achievements to date including researcher reflections; and case studies of how this collaboration influences our research. Written reflective narratives of service user and carer experiences of SUGAR were analysed using constant comparative methods by the members. Common themes are presented with illustrative quotes. The article highlights the benefits and possible limitations identified so far by members of SUGAR; outlines future plans and considers the findings in relation to literature on involvement and empowerment. This paper has been written by staff and members of SUGAR and is the first venture into collaborative writing of the group and reflects the shared ethos of collaborative working.

Exenatide extended-release; clinical trials, patient preference, and economic considerations

Type 2 diabetes remains an escalating world-wide problem, despite a range of treatments. The revelation that insulin secretion is under the control of a gut hormone, glucagon-like peptide 1 (GLP-1) led to a new paradigm in the management of type 2 diabetes, medicines that directly stimulate, or that prolong the actions of the endogenous GLP-1, at its receptors. Exenatide is an agonist at the GLP-1 receptors, and was initially developed as a subcutaneous twice daily medication, ExBID. The clinical trials with ExBID established a role for exenatide in the treatment of type 2 diabetes. Subsequently, once weekly exenatide (ExQW) was shown to have advantages over ExBID, and there is now more emphasis on the development of ExQW. ExQW alone reduces glycosylated haemoglobin (HbA1c) and body weight, and is well tolerated. ExQW has been compared to sitagliptin, pioglitazone and metformin, and shown to have a greater ability to reduce HbA1c than these other medicines. The only preparation of insulin, which ExQW has been compared to, is insulin glargine, and the ExQW has some favourable properties in this comparison, notably causing weight loss, compared to the gain with insulin glargine. ExQW has been compared to another GLP-1 receptor agonist, liraglutide, and ExQW is non-inferior to liraglutide in reducing HbA1c. The small amount of evidence available, shows that subjects with type 2 diabetes, prefer ExQW to ExBID, and that adherence was high to these in the clinical trial setting. Healthcare and economic modelling suggests that ExQW will reduce diabetic complications and be cost-effective, compared to other medications, with long term use. Little is known about whether subjects with type 2 diabetes prefer ExQW to other medicines, and whether adherence is good to ExQW in practice, and these important topics require further study.

Anticipated user experience in the early stages of product development

This research investigates users' anticipation of their future experiences with interactive products to support design for experience in the early stages of product development. This research generates new knowledge of anticipated user experience (AUX), which reveals users' tendency to perceive the pragmatic quality of products as the main determinant of their positive future experiences. The AUX Framework has been an important outcome of this study. The exploration of the components of this framework allows a better prediction and understanding of users' underlying needs and potential usage contexts valuable for the early design phases.

A survey of user modelling in social media websites

With the widespread of social media websites in the internet, and the huge number of users participating and generating infinite number of contents in these websites, the need for personalisation increases dramatically to become a necessity. One of the major issues in personalisation is building users’ profiles, which depend on many elements; such as the used data, the application domain they aim to serve, the representation method and the construction methodology. Recently, this area of research has been a focus for many researchers, and hence, the proposed methods are increasing very quickly. This survey aims to discuss the available user modelling techniques for social media websites, and to highlight the weakness and strength of these methods and to provide a vision for future work in user modelling in social media websites.

Optimal camera planning under versatile user constraints in multi-camera image processing systems

The selection of optimal camera configurations (camera locations, orientations, etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we propose a statistical framework of the problem as well as propose a trans-dimensional simulated annealing algorithm to effectively deal with it. We compare our approach with a state-of-the-art method based on binary integer programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than two alternative heuristics designed to deal with the scalability issue of BIP. Last, we show the versatility of our approach using a number of specific scenarios.

Integrating time forgetting mechanisms into topic-based user interest profiling

The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

Acquiring user information needs for recommender systems

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.

Product feature taxonomy learning based on user reviews

In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to exploit in order to identify the information associated with specific reviewed items. Opinion mining has been widely used to identify the significant features of items (e.g., cameras) based upon user reviews. Feature extraction is the most critical step to identify useful information from texts. Most existing approaches only find individual features about a product without revealing the structural relationships between the features which usually exist. In this paper, we propose an approach to extract features and feature relationships, represented as a tree structure called feature taxonomy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature taxonomy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that our proposed approach is able to capture the product features and relations effectively.

Cross-cultural web user interface design for Indigenous internet users

This project investigated 1) Australian web designers’ cultural perceptions towards Australian Indigenous users and 2) Australian Indigenous cultural features in terms of user interface design. In doing so, it reviews the literature of cross-cultural user interface design by focusing on feasible models and arguments to articulate and integrate Australian Indigenous Internet users’ cultural needs of web user interface. The online survey results collected from 101 Indigenous users and 126 Web designers showed a distinctive difference between them on the integration of Indigenous users' cultural in Web sites. The interview data collected from 14 Indigenous users and 14 web designers suggested practical approaches to the design implications of Indigenous culture.