Positively playful : when videogames lead to player wellbeing

Videogames are an increasingly popular entertainment choice, yet we have a limited understanding of their potential wellbeing benefits. The current research used an online survey (N = 429) to investigate how gameplay choices and the psychological experience of gameplay impact on player wellbeing. Specifically, a hierarchical multiple regression was conducted to determine if, controlling for age and gender, current gameplay choices (amount of play, game genre, mode of play) and play experience (flow, psychological need satisfaction) predicted current wellbeing. Results indicated that age, social play, relatedness during gameplay and flow were positively associated with player wellbeing. Implications for our understanding of player wellbeing, as well as directions for future research are discussed.

A pilot study on understanding the journey of advanced prostate cancer patients

Objective: To understand the journey of advanced prostate cancer patients for supporting development of an innovative patient journey browser. Background: Prostate cancer is one of the common cancers in Australia. Due to the chronic nature of the disease, it is important to have effective disease management strategy and care model. Multi-disciplinary care is a well-proven approach for chronic disease management. The Multi-disciplinary team (MDT) can function more effectively if all the required information is available for the clinical decision support. The development of innovative technology relies on an accurate understanding of the advanced prostate cancer patient’s journey over a prolonged period. This need arises from the fact that advanced prostate cancer patients may follow various treatment paths and change their care providers. As a result of this, it is difficult to understand the actual sources of patient’s clinical records and their treatment patterns. The aim of the research is to understand variable sources of clinical records, treatment patterns, alternative therapies, over the counter (OTC) medications of advanced prostate cancer patients. This study provides better and holistic understanding of advanced prostate cancer journey. Methods: The study was conducted through an on-line survey developed to seek and analyse the responses from the participants. The on-line questionnaire was carefully developed through consultations with the clinical researchers at the Australian Prostate Cancer Research Centre-Queensland, prostate cancer support group representatives and health informaticians at the Australian e-Health Research Centre. The non-identifying questionnaire was distributed to the patients through prostate cancer support groups in Queensland, Australia. The pilot study was carried out between August 2010 and December 2010. Results: The research made important observations about the advanced prostate cancer journey. It showed that General Practitioner (GP) was the common source of patient’s clinical records (41%) followed by Urologist (14%) and other clinicians (14%). The data analysis also showed that selenium was the common complementary supplement (55%) used by the patients and about 48% patients did not use any OTC drugs. The most common OTC used by the patients was Paracetamol (about 45%). Conclusion: The results have provided a foundation to the architecture of the proposed technology solution. The outcomes of this study are incorporated in design of the proposed patient journey browser system. A basic version of the system is currently being used at the advanced prostate cancer MDT meetings.

Topic dependent language modelling for spoken term detection

This paper investigates the effect of topic dependent language models (TDLM) on phonetic spoken term detection (STD) using dynamic match lattice spotting (DMLS). Phonetic STD consists of two steps: indexing and search. The accuracy of indexing audio segments into phone sequences using phone recognition methods directly affects the accuracy of the final STD system. If the topic of a document in known, recognizing the spoken words and indexing them to an intermediate representation is an easier task and consequently, detecting a search word in it will be more accurate and robust. In this paper, we propose the use of TDLMs in the indexing stage to improve the accuracy of STD in situations where the topic of the audio document is known in advance. It is shown that using TDLMs instead of the traditional general language model (GLM) improves STD performance according to figure of merit (FOM) criteria.

An accurate rating aggregation method for generating item reputation

Many websites presently provide the facility for users to rate items quality based on user opinion. These ratings are used later to produce item reputation scores. The majority of websites apply the mean method to aggregate user ratings. This method is very simple and is not considered as an accurate aggregator. Many methods have been proposed to make aggregators produce more accurate reputation scores. In the majority of proposed methods the authors use extra information about the rating providers or about the context (e.g. time) in which the rating was given. However, this information is not available all the time. In such cases these methods produce reputation scores using the mean method or other alternative simple methods. In this paper, we propose a novel reputation model that generates more accurate item reputation scores based on collected ratings only. Our proposed model embeds statistical data, previously disregarded, of a given rating dataset in order to enhance the accuracy of the generated reputation scores. In more detail, we use the Beta distribution to produce weights for ratings and aggregate ratings using the weighted mean method. Experiments show that the proposed model exhibits performance superior to that of current state-of-the-art models.

Improving matching process in social network using implicit and explicit user information

Personalised social matching systems can be seen as recommender systems that recommend people to others in the social networks. However, with the rapid growth of users in social networks and the information that a social matching system requires about the users, recommender system techniques have become insufficiently adept at matching users in social networks. This paper presents a hybrid social matching system that takes advantage of both collaborative and content-based concepts of recommendation. The clustering technique is used to reduce the number of users that the matching system needs to consider and to overcome other problems from which social matching systems suffer, such as cold start problem due to the absence of implicit information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased, using both user information (explicit data) and user behavior (implicit data).

A recommendation method for online dating networks based on social relations and demographic information

A new relationship type of social networks - online dating - are gaining popularity. With a large member base, users of a dating network are overloaded with choices about their ideal partners. Recommendation methods can be utilized to overcome this problem. However, traditional recommendation methods do not work effectively for online dating networks where the dataset is sparse and large, and a two-way matching is required. This paper applies social networking concepts to solve the problem of developing a recommendation method for online dating networks. We propose a method by using clustering, SimRank and adapted SimRank algorithms to recommend matching candidates. Empirical results show that the proposed method can achieve nearly double the performance of the traditional collaborative filtering and common neighbor methods of recommendation.

Creating flexible mappings between building information models and cost information

During the early design stages of construction projects, accurate and timely cost feedback is critical to design decision making. This is particularly challenging for cost estimators, as they must quickly and accurately estimate the cost of the building when the design is still incomplete and evolving. State-of-the-art software tools typically use a rule-based approach to generate detailed quantities from the design details present in a building model and relate them to the cost items in a cost estimating database. In this paper, we propose a generic approach for creating and maintaining a cost estimate using flexible mappings between a building model and a cost estimate. The approach uses queries on the building design that are used to populate views, and each view is then associated with one or more cost items. The benefit of this approach is that the flexibility of modern query languages allows the estimator to encode a broad variety of relationships between the design and estimate. It also avoids the use of a common standard to which both designers and estimators must conform, allowing the estimator added flexibility and functionality to their work.

Information accountability and health big data analytics: A consent-based model

With the ever increasing amount of eHealth data available from various eHealth systems and sources, Health Big Data Analytics promises enticing benefits such as enabling the discovery of new treatment options and improved decision making. However, concerns over the privacy of information have hindered the aggregation of this information. To address these concerns, we propose the use of Information Accountability protocols to provide patients with the ability to decide how and when their data can be shared and aggregated for use in big data research. In this paper, we discuss the issues surrounding Health Big Data Analytics and propose a consent-based model to address privacy concerns to aid in achieving the promised benefits of Big Data in eHealth.

Managing and sharing health data through information accountability protocols

Concerns over the security and privacy of patient information are one of the biggest hindrances to sharing health information and the wide adoption of eHealth systems. At present, there are competing requirements between healthcare consumers' (i.e. patients) requirements and healthcare professionals' (HCP) requirements. While consumers want control over their information, healthcare professionals want access to as much information as required in order to make well-informed decisions and provide quality care. In order to balance these requirements, the use of an Information Accountability Framework devised for eHealth systems has been proposed. In this paper, we take a step closer to the adoption of the Information Accountability protocols and demonstrate their functionality through an implementation in FluxMED, a customisable EHR system.

The design and implementation of an Information Accountability Framework for eHealth systems

This tutorial primarily focuses on the implementation of Information Accountability (IA) protocols defined in an Information Accountability Framework (IAF) in eHealth systems. Concerns over the security and privacy of patient information are one of the biggest hindrances to sharing health information and the wide adoption of eHealth systems. At present, there are competing requirements between healthcare consumers' (i.e. patients) requirements and healthcare professionals' (HCP) requirements. While consumers want control over their information, healthcare professionals want access to as much information as required in order to make well-informed decisions and provide quality care. This conflict is evident in the review of Australia's PCEHR system and in recent studies of patient control of access to their eHealth information. In order to balance these requirements, the use of an Information Accountability Framework devised for eHealth systems has been proposed. Through the use of IA protocols, so-called Accountable-eHealth systems (AeH) create an eHealth environment where health information is available to the right person at the right time without rigid barriers whilst empowering the consumers with information control and transparency. In this half-day tutorial, we will discuss and describe the technical challenges surrounding the implementation of the IAF protocols into existing eHealth systems and demonstrate their use. The functionality of the protocols and AeH systems will be demonstrated, and an example of the implementation of the IAF protocols into an existing eHealth system will be presented and discussed.

An Efficient and Verifiable Solution to the Millionaire Problem

A new solution to the millionaire problem is designed on the base of two new techniques: zero test and batch equation. Zero test is a technique used to test whether one or more ciphertext contains a zero without revealing other information. Batch equation is a technique used to test equality of multiple integers. Combination of these two techniques produces the only known solution to the millionaire problem that is correct, private, publicly verifiable and efficient at the same time.

Overcoming reputation and proof-of-work systems in botnets

Reputation and proof-of-work systems have been outlined as methods bot masters will soon use to defend their peer-to-peer botnets. These techniques are designed to prevent sybil attacks, such as those that led to the downfall of the Storm botnet. To evaluate the effectiveness of these techniques, a botnet that employed these techniques was simulated, and the amount of resources required to stage a successful sybil attack against it measured. While the proof-of-work system was found to increase the resources required for a successful sybil attack, the reputation system was found to lower the amount of resources required to disable the botnet.

Impact of zooming and enhancing Region of Interests for optimizing user experience on mobile sports video

In mobile videos, small viewing size and bitrate limitation often cause unpleasant viewing experiences, which is particularly important for fast-moving sports videos. For optimizing the overall user experience of viewing sports videos on mobile phones, this paper explores the benefits of emphasizing Region of Interest (ROI) by 1) zooming in and 2) enhancing the quality. The main goal is to measure the effectiveness of these two approaches and determine which one is more effective. To obtain a more comprehensive understanding of the overall user experience, the study considers user’s interest in video content and user’s acceptance of the perceived video quality, and compares the user experience in sports videos with other content types such as talk shows. The results from a user study with 40 subjects demonstrate that zooming and ROI-enhancement are both effective in improving the overall user experience with talk show and mid-shot soccer videos. However, for the full-shot scenes in soccer videos, only zooming is effective while ROI-enhancement has a negative effect. Moreover, user’s interest in video content directly affects not only the user experience and the acceptance of video quality, but also the effect of content type on the user experience. Finally, the overall user experience is closely related to the degree of the acceptance of video quality and the degree of the interest in video content. This study is valuable in exploiting effective approaches to improve user experience, especially in mobile sports video streaming contexts, whereby the available bandwidth is usually low or limited. It also provides further understanding of the influencing factors of user experience.