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The global demand for web-based applications regarding financial products and services drives the financial sector to innovate through Information and Communication Technology (ICT) projects. The ICT projects are launched for the diffusion (spread) and implementation of new software or hardware by using web-based platforms in order to offer innovative financial products and services across the branch bank system. These projects are initiated, diffused, managed and implemented by global actors, so-called ICT change agents. Despite the increased recruitment of ICT change agents, there is relatively little research available regarding ICT change agents in financial services projects. Specifically, little consideration is given to the interaction process between formal and informal ICT change agents' roles. Based on a case study methodology in Australia and Germany, this research indicates that deadline-oriented projects drive ICT change agents to play various formal and informal roles. Their formal roles are performed in accordance with organisational settings and project management standards, whereas their informal operations are due to the rapid-changing and global nature of ICT technologies. The findings are summed up in a new framework which indicates that both types of roles impact on the outcomes of financial services technology projects. [ABSTRACT FROM AUTHOR]

Delivering technology projects on time with a specified budget and resources has emerged as a strategic imperative in the highly competitive business world. One of the project challenges is increasingly tied to diffusion (spread) of Information and Communication Technology (ICT) innovation. This paper presents an empirical study that examines how ICT innovation is diffused within technology projects. Based on the case study methodology within 12 organisations in Australia and Germany, it emerged that ICT innovation is diffused formally alongside standard project management phases and informally within informal networks. The findings are synthesised in a new framework that seeks to inform theory and practice about formal and informal processes of ICT diffusion in technology projects. [ABSTRACT FROM AUTHOR]

This paper presents a computational approach for predicting the Australian stock market index -- AORD using multi-layer feed-forward neural networks from the time series data of AORD and various interrelated markets. This effort aims to discover an effective neural network or set of adaptive neural networks for this prediction purpose, which can exploit or model various dynamical swings and inter-market influences discovered from professional technical analysis and quantitative analysis. Within a limited range defined by our empirical knowledge, three aspects of effectiveness on data selection are considered: effective inputs from the target market (AORD) itself, a sufficient set of interrelated markets, and effective inputs from the interrelated markets. Two traditional dimensions of the neural network architecture are also considered: the optimal number of hidden layers, and the optimal number of hidden neurons for each hidden layer. Three important results were obtained: A 6-day cycle was discovered in the Australian stock market during the studied period: the time signature used as additional inputs provides useful information; and a basic neural network using six daily returns of AORD and one daily returns of SP500 plus the day of the week as inputs exhibits up to 80% directional prediction correctness. [ABSTRACT FROM AUTHOR]

Background: Regression-based research has successfully identified independent predictors of smoking cessation, both its initiation and maintenance. However, it is unclear how these various independent predictors interact with each other and conjointly influence smoking behaviour. As a proof-of-concept, this study used decision tree analysis (DTA) to identify the characteristics of smoker subgroups with high versus low smoking cessation initiation probability based on the conjoint effects of four predictor variables, and determine any variations by socio-economic status (SES).Methods: Data come from the Australian arm of the ITC project, a longitudinal cohort study of adult smokers followed up approximately annually. Reported wanting to quit smoking, worries about smoking negative health impact, quitting self-efficacy and quit intentions assessed in 2005 were used as predictors and reported quit attempts at the 2006 follow-up survey were used as the outcome for the initial model calibration and validation analyses (n = 1475), and further cross-validated using the 2012-2013 data (n = 787).Results: DTA revealed that while all four predictor variables conjointly contributed to the identification of subgroups with high versus low smoking cessation initiation probability, quit intention was the most important predictor common across all SES strata. The relative importance of the other predictors showed differences by SES.Conclusions: Modifiable characteristics of smoker subgroups associated with making a quit attempt and any variations by SES can be successfully identified using a decision tree analysis approach, to provide insights as to who might benefit from targeted intervention, thus, underscoring the value of this approach to complement the conventional regression-based approach. [ABSTRACT FROM AUTHOR]

Ground parrot vocalisation can be considered as an audio event. Test-based diverse density multiple instance learning (TB-DD-MIL) is proposed for detecting this event in audio files recorded in the field. The proposed method is motivated by the advantages of multiple instance learning from incomplete training data. Spectral features suitable for encoding the vocal source information of the ground parrot vocalization are also investigated. The proposed method was benchmarked against a dataset collected in various environmental conditions and an audio detection evaluation scheme is proposed. The evaluation includes a study on performance of the various vocal source features and comparison with other classification techniques. Experimental results indicated that the most appropriate feature to encode ground parrot calls is the spectral bandwidth and the proposed TB-DD-MIL method outperformed other existing classification methods.