Discovering interesting information with advances in web technology

The Web is a steadily evolving resource comprising much more than mere HTML pages. With its ever-growing data sources in a variety of formats, it provides great potential for knowledge discovery. In this article, we shed light on some interesting phenomena of the Web: the deep Web, which surfaces database records as Web pages; the Semantic Web, which de���nes meaningful data exchange formats; XML, which has established itself as a lingua franca for Web data exchange; and domain-speci���c markup languages, which are designed based on XML syntax with the goal of preserving semantics in targeted domains. We detail these four developments in Web technology, and explain how they can be used for data mining. Our goal is to show that all these areas can be as useful for knowledge discovery as the HTML-based part of the Web.

A data mining framework for relevance feature discovery

This thesis is a study for automatic discovery of text features for describing user information needs. It presents an innovative data-mining approach that discovers useful knowledge from both relevance and non-relevance feedback information. The proposed approach can largely reduce noises in discovered patterns and significantly improve the performance of text mining systems. This study provides a promising method for the study of Data Mining and Web Intelligence.

Intelligent tutoring system for learning PHP

This thesis investigates the possibility of using an adaptive tutoring system for beginning programming students. The work involved, designing, developing and evaluating such a system and showing that it was effective in increasing the students��� test scores. In doing so, Artificial Intelligence techniques were used to analyse PHP programs written by students and to provide feedback based on any specific errors made by them. Methods were also included to provide students with the next best exercise to suit their particular level of knowledge.

Representing and discovering adversarial team behaviors using player roles

In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a "role-based" representation instead of one based on player "identity" can best exploit the playing structure. As vision-based systems currently do not provide perfect detection/tracking (e.g. missed or false detections), we show that our compact representation can effectively "denoise" erroneous detections as well as enabe temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data.

Speaker attribution of Australian broadcast news data

Speaker attribution is the task of annotating a spoken audio archive based on speaker identities. This can be achieved using speaker diarization and speaker linking. In our previous work, we proposed an efficient attribution system, using complete-linkage clustering, for conducting attribution of large sets of two-speaker telephone data. In this paper, we build on our proposed approach to achieve a robust system, applicable to multiple recording domains. To do this, we first extend the diarization module of our system to accommodate multi-speaker (>2) recordings. We achieve this through using a robust cross-likelihood ratio (CLR) threshold stopping criterion for clustering, as opposed to the original stopping criterion of two speakers used for telephone data. We evaluate this baseline diarization module across a dataset of Australian broadcast news recordings, showing a significant lack of diarization accuracy without previous knowledge of the true number of speakers within a recording. We thus propose applying an additional pass of complete-linkage clustering to the diarization module, demonstrating an absolute improvement of 20% in diarization error rate (DER). We then evaluate our proposed multi-domain attribution system across the broadcast news data, demonstrating achievable attribution error rates (AER) as low as 17%.

Does activity in computer game play have an impact on creative behavior?

This paper describes a behaviour analysis designed to measure the creative potential of computer game activities. The research approach applies a behavioural and verbal protocol to analyze the factors that influence the creative processes used by people as they play computer games from the puzzle genre. Creative components are measured by examining task motivation as well as domain-relevant and creativity-relevant skills factors. This paper focuses on how three puzzle games embody activity that might facilitate creative processes. The findings show that game playing activities significantly impact upon creative potential of computer games.

Algorithm clustering for multi-algorithm processor design

An Application Specific Instruction-set Processor (ASIP) is a specialized processor tailored to run a particular application/s efficiently. However, when there are multiple candidate applications in the application���s domain it is difficult and time consuming to find optimum set of applications to be implemented. Existing ASIP design approaches perform this selection manually based on a designer���s knowledge. We help in cutting down the number of candidate applications by devising a classification method to cluster similar applications based on the special-purpose operations they share. This provides a significant reduction in the comparison overhead while resulting in customized ASIP instruction sets which can benefit a whole family of related applications. Our method gives users the ability to quantify the degree of similarity between the sets of shared operations to control the size of clusters. A case study involving twelve algorithms confirms that our approach can successfully cluster similar algorithms together based on the similarity of their component operations.

Designing for participation in local suburban communications

People have difficulty accessing means to easily publicise and discuss their ideas and concerns with their local community. The aim of this research has been to design and evaluate capacity for Internet technologies coupled with public displays to engage a diverse range of community members in the making of a shared local suburban communications network. The research problem relates to the challenges of community building, in particular discovering mechanisms that work to engage a target community and motivate participation. In an effort to understand genuine participation and barriers to use, the study was embedded in a local community and purposely longitudinal. This research contributes knowledge about the limitations of public displays to increase visibility of local communications, the need for long-term and networked visibility of community-building communications, the integral role of community facilitators, and the challenges of sustaining shared communications.

Curiosity to cupboard : self reported disengagement with energy use feedback over time

This paper discusses findings made during a study of energy use feedback in the home (eco-feedback), well after the novelty has worn off. Contributing towards four important knowledge gaps in the research, we explore eco-feedback over longer time scales, focusing on instances where the feedback was not of lasting benefit to users rather than when it was. Drawing from 23 semi-structured interviews with Australian householders, we found that an initially high level of engagement gave way over time to disinterest, neglect and in certain cases, technical malfunction. Additionally, preconceptions concerned with the ���purpose��� of the feedback were found to affect use. We propose expanding the scope of enquiry for eco-feedback in several ways, and describe how eco-feedback that better supports decision-making in the ���maintenance phase���, i.e. once the initial novelty has worn off, may be key to longer term engagement.

Crowd monitoring using computer vision

Novel computer vision techniques have been developed for automatic monitoring of crowed environments such as airports, railway stations and shopping malls. Using video feeds from multiple cameras, the techniques enable crowd counting, crowd flow monitoring, queue monitoring and abnormal event detection. The outcome of the research is useful for surveillance applications and for obtaining operational metrics to improve business efficiency.

Creating efficiencies in teaching : where are the right resources when you need them?

Early career engineering academics are encouraged to join and contribute to established research groups at the leading edge of their discipline. This is often facilitated by various staff development and support programs. Given that academics are often appointed primarily on the basis of their research skills and outputs, such an approach is justified and is likely to result in advancing the individual academic���s career. It also enhances their capacity to attract competitive research funding, while contributing to the overall research performance of their institution, with further potential for an increased share of government funding. In contrast, there is much less clarity of direction or availability of support mechanisms for those academics in their role as teachers. Following a general induction to teaching and learning at their institution, they would commonly think about preparing some lecture materials, whether for delivery in a face-to-face or on-line modality. Typically they would look for new references and textbooks to act as a guide for preparing the content. They would probably find out how the course has been taught before, and what laboratory facilities and experiments have been used. In all of these and other related tasks, the majority of newly appointed academics are guided strongly by their own experiences as students, rather than any firm knowledge of pedagogical principles. At a time of increased demands on academics��� time, and high expectations of performance and productivity in both research and teaching, it is essential to examine possible actions to support academics in enhancing their teaching performance in effective and efficient ways. Many resources have been produced over the years in engineering schools around the world, with very high intellectual and monetary costs. In Australia, the last few years have seen a surge in the number of ALTC/OLT projects and fellowships addressing a range of engineering education issues and providing many resources. There are concerns however regarding the extent to which these resources are being effectively utilised. Why are academics still re-inventing the wheel and creating their own version of teaching resources and pedagogical practice? Why do they spend so much of their precious time in such an inefficient way? A symposium examining the above issues was conducted at the AAEE2012 conference, and some pointers to possible responses to the above questions were obtained. These are explored in this paper and supplemented by the responses to a survey of a group of engineering education leaders on some of the aspects of these research questions. The outcomes of the workshop and survey results have been analysed in view of the literature and the ALTC/OLT sponsored learning and teaching projects and resources. Other factors are discussed, including how such resources can be found, how their quality might be evaluated, and how assessment may be appropriately incorporated, again using readily available resources. This study found a strong resonance between resources reuse with work on technology acceptance (Davis, 1989), suggesting that technology adoption models could be used to encourage resource sharing. Efficient use of outstanding learning materials is an enabling approach. The paper provides some insights on the factors affecting the re-use of available resources, and makes some recommendations and suggestions on how the issue of resources re-use might be incorporated in the process of applying and completing engineering education projects.

Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach, which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this chapter we propose two approaches which measure multi-level association rules to help evaluate their interestingness by considering the database���s underlying taxonomy. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

The power of hands-on exercises in SCADA cyber security education

For decades Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) have used computers to monitor and control physical processes in many critical industries, including electricity generation, gas pipelines, water distribution, waste treatment, communications and transportation. Increasingly these systems are interconnected with corporate networks via the Internet, making them vulnerable and exposed to the same risks as those experiencing cyber-attacks on a conventional network. Very often SCADA networks services are viewed as a specialty subject, more relevant to engineers than standard IT personnel. Educators from two Australian universities have recognised these cultural issues and highlighted the gap between specialists with SCADA systems engineering skills and the specialists in network security with IT background. This paper describes a learning approach designed to help students to bridge this gap, gain theoretical knowledge of SCADA systems' vulnerabilities to cyber-attacks via experiential learning and acquire practical skills through actively participating in hands-on exercises.

Brain-inspired sensor fusion for navigating robots

Current state of the art robot mapping and navigation systems produce impressive performance under a narrow range of robot platform, sensor and environmental conditions, in contrast to animals such as rats that produce ���good enough��� maps that enable them to function under an incredible range of situations. In this paper we present a rat-inspired featureless sensor-fusion system that assesses the usefulness of multiple sensor modalities based on their utility and coherence for place recognition, without knowledge as to the type of sensor. We demonstrate the system on a Pioneer robot in indoor and outdoor environments with abrupt lighting changes. Through dynamic weighting of the sensors, the system is able to perform correct place recognition and mapping where the static sensor weighting approach fails.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.