Development of Mobile Web App "ESP" for DeepBlue to interrogate how an audience is thinking

DeepBlue is much more than just an orchestra. Their innovative approach to audience engagement led it to develop ESP, their Electronic Show Programme web app which allows for real-time (synchronous) and delayed (asynchronous) audience interaction, customer feedback and research. The show itself is driven invisibly by a music technology operating system (currently QUT's Yodel) that allows them to adapt to a wide range of performance venues and varied types of presentation. DeepBlue's community engagement program has enabled over 5,500 young musicians and community choristers to participate in professional productions, it is also a cornerstone of DeepBlue's successful business model. You can view the ESP mobile web app at m.deepblue.net.au if you view this and only the landing page is active, there is not a show taking place or imminent. ESP prototype has already been used for 18 months. Imagine knowing what your audience really thinks – in real time so you can track their feelings and thoughts through the show. This tool has been developed and used by the performing group DeepBlue since late 2012 in Australia and Asia (even translated into Vietnamese). It has mostly superseded DeepBlue's SMS realtime communication during a show. It enables an event presenter or performance group to take the pulse of an audience through a series of targeted questions that can be anonymous or attributed. This will help build better, long-lasting, and more meaningful relationships with groups and individuals in the community. This can take place on a tablet, mobile phone or future platforms. There are three organisations trialling it so far.

DeepBlue India Stories

Original Staged Music Performance incorporating Projected Sand Art and Narrator at Woodford Festival 2013

‘Healthy mobile check-ins’ study: using GIS in smartphones to track use of urban environment by survivors of endometrial cancer

Aims/Objectives Our study aims to test the capacity of a newly developed smartphone innovation to obtain data on social, structural, and spatial determinants of the daily health-related behaviours of women living in urban Brisbane neighbourhoods who have survived endometrial cancer. Methods The women used a mobile web app designed specifically for the project to record GIS/location data on every destination they visited within their local urban neighbourhoods over a two-week period. Additionally, we gathered textual data on the social context/reasons for travel, as well as mode of transport to reach these destinations. The data was transported to SPSS and Google Earth for statistical and spatial analysis. We then met with the women to discuss lifestyle interventions to maximise their use of their local neighbourhoods in ways that could increase their physical activity levels and improve their overall health and well-being. These interventions will be evaluated and translated into a large-scale national study if effective. Results Initial findings about patterns in the group’s use of the local urban environment will be displayed, including daily distances travelled, types of locations visited, walking levels, use of public transport, use of green spaces and use of health-related resources. Any socio-demograpahic differences found between the women will be reported. Qualitative, quantitative, and spatial/mapping data will be displayed Conclusion The benefits and limitations of the mobile website designed to collect a range of data types about human-neighbourhood interactions with implications for intervention design will be discussed.

The Steering Clear First Offender Drink Driving Program

This program is a research based, guided intervention program, designed for first time drink driving offenders which provides them with information and strategies to avoid drink driving in the future. It is an innovative program with the ability to tailor specific information to different individuals based on their level of risk of reoffending and help them develop their own plan to prevent them from drink driving. It aims to teach offenders the skills to implement their own plan when they determine they are at risk of future drink driving. The program provides information about: What a standard drink is and how blood alcohol content (BAC) is determined; How alcohol affects the body, reaction time, and decision making; The consequences of drink driving and what happens after a second offence; How to deal with risky drink driving situations in the future; How to build a personalised plan to avoid drink driving in the future, and; Levels of alcohol consumption and its impact on daily life. It also includes access to a mobile friendly web app that can be used anytime after completing the program. This is tool that will aid offenders in tracking their drinks and build on plans to prevent future drink driving.

An innovative online intervention: The Steering Clear First Offender Drink Driving Program

Drink driving remains a substantial public health issue warranting investigation. First offender drink drivers are seen to be less risky than repeat offenders, though the majority of first offenders report drink driving prior to detection, and many continue to drink drive following conviction. Few first offenders are offered treatment programs, and as such there is a need to address drink driving behaviour at this stage. A comprehensive approach including first offender treatment is needed to address the problem. Online interventions have demonstrated effectiveness in reducing risky behaviours such as harmful substance use. Such interventions allow for personalised tailored content to be delivered to individuals targeting specific mechanisms of behavioural change. This method also allows for targeting screening to ensure relevance of content on an individual level. However, there have been no research based online programs to date aimed at reducing repeat drink driving by first offenders. The Steering Clear First Offender Drink Driving Program is a self-guided, research based online program aimed at reducing recidivism by first time drink driving offenders. It includes a specialised web app to track drinks and build plans to prevent future drink driving. This allows for elongation of learning and encouragement of sustained behavioural change using self-monitoring after initial program completion. An outline of the program is discussed and the qualitative experience of the program on a sample of first offenders recruited at the time of court appearance is described.

The role of sensation seeking and need for cognition on web-site evaluations : a resource-matching perspective

The Internet theoretically enables marketers to personalize a Website to an individual consumer. This article examines optimal Website design from the perspective of personality trait theory and resource-matching theory. The influence of two traits relevant to Internet Web-site processing—sensation seeking and need for cognition— were studied in the context of resource matching and different levels of Web-site complexity. Data were collected at two points of time: personality-trait data and a laboratory experiment using constructed Web sites. Results reveal that (a) subjects prefer Web sites of a medium level of complexity, rather than high or low complexity; (b)high sensation seekers prefer complex visual designs, and low sensation seekers simple visual designs, both in Web sites of medium complexity; and (c) high need-for-cognition subjects evaluated Web sites with high verbal and low visual complexity more favourably.

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).

Information extraction from web services : a comparison of Tokenisation algorithms

Most web service discovery systems use keyword-based search algorithms and, although partially successful, sometimes fail to satisfy some users information needs. This has given rise to several semantics-based approaches that look to go beyond simple attribute matching and try to capture the semantics of services. However, the results reported in the literature vary and in many cases are worse than the results obtained by keyword-based systems. We believe the accuracy of the mechanisms used to extract tokens from the non-natural language sections of WSDL files directly affects the performance of these techniques, because some of them can be more sensitive to noise. In this paper three existing tokenization algorithms are evaluated and a new algorithm that outperforms all the algorithms found in the literature is introduced.

Matching relevance features with ontological concepts

In order to comprehend user information needs by concepts, this paper introduces a novel method to match relevance features with ontological concepts. The method first discovers relevance features from user local instances. Then, a concept matching approach is developed for matching these features to accurate concepts in a global knowledge base. This approach is significant for the transition of informative descriptor and conceptional descriptor. The proposed method is elaborately evaluated by comparing against three information gathering baseline models. The experimental results shows the matching approach is successful and achieves a series of remarkable improvements on search effectiveness.

Link discovery for Chinese/English cross-language web information retrieval

Nowadays people heavily rely on the Internet for information and knowledge. Wikipedia is an online multilingual encyclopaedia that contains a very large number of detailed articles covering most written languages. It is often considered to be a treasury of human knowledge. It includes extensive hypertext links between documents of the same language for easy navigation. However, the pages in different languages are rarely cross-linked except for direct equivalent pages on the same subject in different languages. This could pose serious difficulties to users seeking information or knowledge from different lingual sources, or where there is no equivalent page in one language or another. In this thesis, a new information retrieval task—cross-lingual link discovery (CLLD) is proposed to tackle the problem of the lack of cross-lingual anchored links in a knowledge base such as Wikipedia. In contrast to traditional information retrieval tasks, cross language link discovery algorithms actively recommend a set of meaningful anchors in a source document and establish links to documents in an alternative language. In other words, cross-lingual link discovery is a way of automatically finding hypertext links between documents in different languages, which is particularly helpful for knowledge discovery in different language domains. This study is specifically focused on Chinese / English link discovery (C/ELD). Chinese / English link discovery is a special case of cross-lingual link discovery task. It involves tasks including natural language processing (NLP), cross-lingual information retrieval (CLIR) and cross-lingual link discovery. To justify the effectiveness of CLLD, a standard evaluation framework is also proposed. The evaluation framework includes topics, document collections, a gold standard dataset, evaluation metrics, and toolkits for run pooling, link assessment and system evaluation. With the evaluation framework, performance of CLLD approaches and systems can be quantified. This thesis contributes to the research on natural language processing and cross-lingual information retrieval in CLLD: 1) a new simple, but effective Chinese segmentation method, n-gram mutual information, is presented for determining the boundaries of Chinese text; 2) a voting mechanism of name entity translation is demonstrated for achieving a high precision of English / Chinese machine translation; 3) a link mining approach that mines the existing link structure for anchor probabilities achieves encouraging results in suggesting cross-lingual Chinese / English links in Wikipedia. This approach was examined in the experiments for better, automatic generation of cross-lingual links that were carried out as part of the study. The overall major contribution of this thesis is the provision of a standard evaluation framework for cross-lingual link discovery research. It is important in CLLD evaluation to have this framework which helps in benchmarking the performance of various CLLD systems and in identifying good CLLD realisation approaches. The evaluation methods and the evaluation framework described in this thesis have been utilised to quantify the system performance in the NTCIR-9 Crosslink task which is the first information retrieval track of this kind.

A people-to-people matching system using graph mining techniques

A people-to-people matching system (or a match-making system) refers to a system in which users join with the objective of meeting other users with the common need. Some real-world examples of these systems are employer-employee (in job search networks), mentor-student (in university social networks), consume-to-consumer (in marketplaces) and male-female (in an online dating network). The network underlying in these systems consists of two groups of users, and the relationships between users need to be captured for developing an efficient match-making system. Most of the existing studies utilize information either about each of the users in isolation or their interaction separately, and develop recommender systems using the one form of information only. It is imperative to understand the linkages among the users in the network and use them in developing a match-making system. This study utilizes several social network analysis methods such as graph theory, small world phenomenon, centrality analysis, density analysis to gain insight into the entities and their relationships present in this network. This paper also proposes a new type of graph called “attributed bipartite graph”. By using these analyses and the proposed type of graph, an efficient hybrid recommender system is developed which generates recommendation for new users as well as shows improvement in accuracy over the baseline methods.

Is there an app for that? A case study of the potentials and limitations of the participator turn and networked publics for classical music audience engagement

The participatory turn, fuelled by discourses and rhetoric regarding social media, and in the aftermath of the dot.com crash of the early 2000s, enrols to some extent an idea of being able to deploy networks to achieve institutional aims. The arts and cultural sector in the UK, in the face of funding cuts, has been keen to engage with such ideas in order to demonstrate value for money; by improving the efficiency of their operations, improving their respective audience experience and ultimately increasing audience size and engagement. Drawing on a case study compiled via a collaborative research project with a UK-based symphony orchestra (UKSO) we interrogate the potentials of social media engagement for audience development work through participatory media and networked publics. We argue that the literature related to mobile phones and applications (‘apps’) has focused primarily on marketing for engagement where institutional contexts are concerned. In contrast, our analysis elucidates the broader potentials and limitations of social-media-enabled apps for audience development and engagement beyond a marketing paradigm. In the case of UKSO, it appears that the technologically deterministic discourses often associated with institutional enrolment of participatory media and networked publics may not necessarily apply due to classical music culture. More generally, this work raises the contradictory nature of networked publics and argues for increased critical engagement with the concept.

Mobile app rating scale: A new tool for assessing the quality of health mobile apps

Background The use of mobile apps for health and well being promotion has grown exponentially in recent years. Yet, there is currently no app-quality assessment tool beyond “star”-ratings. Objective The objective of this study was to develop a reliable, multidimensional measure for trialling, classifying, and rating the quality of mobile health apps. Methods A literature search was conducted to identify articles containing explicit Web or app quality rating criteria published between January 2000 and January 2013. Existing criteria for the assessment of app quality were categorized by an expert panel to develop the new Mobile App Rating Scale (MARS) subscales, items, descriptors, and anchors. There were sixty well being apps that were randomly selected using an iTunes search for MARS rating. There were ten that were used to pilot the rating procedure, and the remaining 50 provided data on interrater reliability. Results There were 372 explicit criteria for assessing Web or app quality that were extracted from 25 published papers, conference proceedings, and Internet resources. There were five broad categories of criteria that were identified including four objective quality scales: engagement, functionality, aesthetics, and information quality; and one subjective quality scale; which were refined into the 23-item MARS. The MARS demonstrated excellent internal consistency (alpha = .90) and interrater reliability intraclass correlation coefficient (ICC = .79). Conclusions The MARS is a simple, objective, and reliable tool for classifying and assessing the quality of mobile health apps. It can also be used to provide a checklist for the design and development of new high quality health apps.