Online banking customization via tag-based interaction

In this paper, we describe ongoing work on online banking customization with a particular focus on interaction. The scope of the study is confined to the Australian banking context where the lack of customization is evident. This paper puts forward the notion of using tags to facilitate personalized interactions in online banking. We argue that tags can afford simple and intuitive interactions unique to every individual in both online and mobile environments. Firstly, through a review of related literature, we frame our work in the customization domain. Secondly, we define a range of taggable resources in online banking. Thirdly, we describe our preliminary prototype implementation with respect to interaction customization types. Lastly, we conclude with a discussion on future work.

A case study of how primary school teachers in Papua New Guinea understand outcomes-based education

This case study explored how a group of primary school teachers in Papua New Guinea (PNG) understood Outcomes-based Education (OBE). OBE measures students. learning against specific outcomes. These outcomes are derived from a country.s vision of the kind of citizen that the education system should produce. While countries such as Australia, South Africa, New Zealand and the United States have abandoned OBE, others such as PNG have adopted it in various ways. How teachers understand OBE in PNG is important because such understandings are likely to influence how they implement the OBE curriculum. There has been no research to date which has investigated PNG primary school teachers. understandings and experiences with OBE. This study used a single exploratory case study design to investigate how twenty primary school teachers from the National Capital District (NCD) in PNG understood OBE. The study, underpinned by an intepretivist paradigm, explored the research question: How do primary school teachers understand outcomes-based education in PNG? The data comprised surveys, in-depth interviews and documents. Data were analysed thematically and using explanation building techniques. The findings revealed that OBE is viewed by teachers as a way to equip them with additional strategies for planning and programming, teaching and learning, and assessment. Teachers also described how OBE enabled both students and teachers to become more engaged and develop positive attitudes towards teaching and learning. There was also a perception that OBE enhanced students. future life skills through increased local community support. While some teachers commented on how the OBE reforms provided them with increased professional development opportunities, the greatest impediment to implementing OBE was perceived to be a lack of sufficient teaching and learning resources. The process of planning and programming classroom activities was also regarded as onerous. Some teachers indicated that they had been required to implement OBE without adequate in-service training support. The social constructivist theory of knowledge which underpins OBE.s student-centred pedagogy can cause tensions within PNG.s cultural contexts of teaching and learning. Teachers need to be aware of these tensions when conducting peer or group learning under OBE in PNG. By exploring how these PNG primary teachers understood OBE, the study highlighted how teachers engaged with OBE concepts when interpreting syllabus documents and how they applied these concepts to curriculum. Identifying differences in teacher understanding of OBE provides guidance for both the design of materials to support the implementation of OBE and for the design of in-service training. Thus, the outcomes of this study will inform educators about the implementation of OBE in PNG. In addition, the outcomes will provide much needed insight into how a mandated curriculum and pedagogical reform impacts teachers‟ practices in PNG.

Good faith in contractual performance and enforcement : Australian doctrinal hurdles

In the ongoing and spirited debate about the relative merits of an obligation of good faith in contractual performance and enforcement, widely divergent views have been expressed about the appropriateness and content of the putative obligation. However, relatively less time has been devoted to discussion of the sparseness of tools available to facilitate doctrinal development and the hurdles necessarily imposed by such limited doctrinal resources. This article seeks to examine the Australian doctrinal position against the backdrop of good faith as it finds application in the wider global context.

Vector quantization based Gaussian modeling for speaker verification

Gaussian mixture models (GMMs) have become an established means of modeling feature distributions in speaker recognition systems. It is useful for experimentation and practical implementation purposes to develop and test these models in an efficient manner particularly when computational resources are limited. A method of combining vector quantization (VQ) with single multi-dimensional Gaussians is proposed to rapidly generate a robust model approximation to the Gaussian mixture model. A fast method of testing these systems is also proposed and implemented. Results on the NIST 1996 Speaker Recognition Database suggest comparable and in some cases an improved verification performance to the traditional GMM based analysis scheme. In addition, previous research for the task of speaker identification indicated a similar system perfomance between the VQ Gaussian based technique and GMMs

Approximate Membership Localization within a Web-Based Join Framework

In this paper, we propose a search-based approach to join two tables in the absence of clean join attributes. Non-structured documents from the web are used to express the correlations between a given query and a reference list. To implement this approach, a major challenge we meet is how to efficiently determine the number of times and the locations of each clean reference from the reference list that is approximately mentioned in the retrieved documents. We formalize the Approximate Membership Localization (AML) problem and propose an efficient partial pruning algorithm to solve it. A study using real-word data sets demonstrates the effectiveness of our search-based approach, and the efficiency of our AML algorithm.

REGAL : a regularization based algorithm for reinforcement learning in weakly communicating MDPs

We provide an algorithm that achieves the optimal regret rate in an unknown weakly communicating Markov Decision Process (MDP). The algorithm proceeds in episodes where, in each episode, it picks a policy using regularization based on the span of the optimal bias vector. For an MDP with S states and A actions whose optimal bias vector has span bounded by H, we show a regret bound of ~ O(HS p AT ). We also relate the span to various diameter-like quantities associated with the MDP, demonstrating how our results improve on previous regret bounds.

Data preprocessing for anomaly based network intrusion detection : a review

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches. Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.

Evaluating effects of eco-driving at traffic intersections based on traffic micro-simulation

Eco-driving is an initiative driving behavior which aims to reduce fuel consumption and emissions from automobiles. Recently, it has attracted increasing interests and has been adopted by many drivers in Australia. Although many of the studies have revealed considerable benefits in terms of fuel consumption and emissions after utilising eco-driving, most of the literature investigated eco-driving effects on individual driver but not traffic flow. The driving behavior of eco-drivers will potentially affect other drivers and thereby affects the entire traffic flow. To comprehensively assess and understand how effectively eco-driving can perform, therefore, measurement on traffic flow is necessary. In this paper, we proposed and demonstrated an evaluation method based on a microscopic traffic simulator (Aimsun). We focus on one particular eco-driving style which involves moderate and smooth acceleration. We evaluated both traffic performance (travel time) and environmental performance (fuel consumption and CO2 emission) at traffic intersection level in a simple simulation model. The before-and-after comparisons indicated potentially negative impacts when using eco-driving, which highlighted the necessity to carefully evaluate and improve eco-driving before wide promotion and implementation.

Optimal budget allocation in budget-based access control

In dynamic and uncertain environments, where the needs of security and information availability are difficult to balance, an access control approach based on a static policy will be suboptimal regardless of how comprehensive it is. Risk-based approaches to access control attempt to address this problem by allocating a limited budget to users, through which they pay for the exceptions deemed necessary. So far the primary focus has been on how to incorporate the notion of budget into access control rather than what or if there is an optimal amount of budget to allocate to users. In this paper we discuss the problems that arise from a sub-optimal allocation of budget and introduce a generalised characterisation of an optimal budget allocation function that maximises organisations expected benefit in the presence of self-interested employees and costly audit.