On detecting differences between groups

Understanding the differences between contrasting groups is a fundamental task in data analysis. This realization has led to the development of a new special purpose data mining technique, contrast-set mining. We undertook a study with a retail collaborator to compare contrast-set mining with existing rule-discovery techniques. To our surprise we ob- served that straightforward application of an existing commercial rule-discovery system, Magnum Opus, could successfully perform the contrast-set-mining task. This led to the realization that contrast-set mining is a special case of the more general rule-discovery task. We present the results of our study together with a proof of this conclusion.

The development of a wisdom autonomic grid

It is not a simple matter to develop an integrative approach that exploits  synergies between knowledge management and knowledge discovery in   order to monitor and manage the full lifecycle of knowledge and provides  services quickly, reliably and securely. One of the main problems is the heterogeneity of the involved resources that represent knowledge. Data mining systems produce knowledge in a form meant to be understandable  to machines and on the other hand in knowledge management systems the  priority is placed on the readability and usability of knowledge by humans.  The Semantic Web is a promising platform to unify this heterogeneity and, in conjunction with novel techniques for Web Intelligence it could offer more  then just knowledge - wisdom. The Wisdom Autonomic Grid is an original proposal of a knowledge based Grid that is able to configure and reconfigure itself under varying and unpredictable conditions and optimize its working, performs something akin to healing and provides self-protection, as  visioned in the IBM Autonomic Computing initiative. This paper presents an original framework for creating advanced applications to integrate  knowledge discovery and knowledge management in the Autonomic Grid  and Web environments.

Analytical performance modeling of the enterprise grid computing

This paper addresses the problem of performance modeling of large-scale heterogeneous distributed systems with emphases on enterprise grid computing systems. To this end, we present an analytical model that can be employed to explore the effectiveness of different design approaches so that one can have an intelligent choice during design and evaluation a cost-effective large-scale heterogeneous distributed computing system. The model is validated through comprehensive simulation.

Dynamic reconfiguration of multi-agent systems based on autonomy oriented computing

Dynamic reconfiguration has been listed as one of the key challenges in support of agent adaptation to environments, which has attracted much attention of researchers world wide. To tackle this tough problem, an agent-based dynamic reconfiguration model (ADRM) is proposed from the autonomy-oriented computing (AOC) point of view. The ERA (environment-reactive rules-agents) algorithm used in AOC is improved to support the organization formation behavior, which is essential in dynamic reconfiguration. To test the efficiency of this model and the effectiveness of different reactive behaviors, the performance of this model was investigated under different selection probabilities.

Analytical interconnection networks model for multi-cluster computing systems

This paper addresses the problem of interconnection networks performance modeling of large-scale distributed systems with emphases on multi-cluster computing systems. The study of interconnection networks is important because the overall performance of a distributed system is often critically hinged on the effectiveness of its interconnection network. We present an analytical model that considers stochastic quantities as well as processor heterogeneity of the target system. The model is validated through comprehensive simulation, which demonstrates that the proposed model exhibits a good degree of accuracy for various system sizes and under different operating conditions.

Analysis of multi-cluster computing systems with processor heterogeneity

This paper addresses the problem of performance modeling of heterogeneous multi-cluster computing systems. We present an analytical model that can be employed to explore the effectiveness of different design approaches so that one can have an intelligent choice during design and evaluation of a cost effective large-scale heterogeneous distributed computing system. The proposed model considers stochastic quantities as well as processor heterogeneity of the target system. The analysis is based on a parametric fat-tree network, the m-port n-tree, and a deterministic routing algorithm. The correctness of the proposed model is validated through comprehensive simulation of different types of clusters.

Gender and IT : ongoing challenges for computing and information technology education in Australian secondary education

# 1. Introduction. Exploring the gender and IT problem and possible ways forward /​ Julianne Lynch
# 2. The imagined curriculum: who studies Computing and Information Technology subjects at the senior secondary level? /​ Margaret Vickers and My Trinh Ha
# 3. A question of attention: challenges for researching the under representation of girls in Computing and Information Technology subjects /​ Leonie Rowan
# 4. The nature and purpose of Computing and Information Technology subjects in the senior secondary school curriculum in New South Wales /​ Toni Downes
# 5. The social construction of Computing and Information Technology subject subculture /​ Catherine Harris
# 6. Boy nerds, girl nerds: constituting and negotiating Computing and Information Technology and peer groups as gendered subjects in schooling /​ Kerry Robinson and Cristyn Davies
# 7. CIT teachers' cultures in a globalising world /​ Carol Reid and Jose van der Akker
# 8. Perceptions of changing pedagogies in Computing and Information Technology /​ Susanne Gannon

Girls and computing-still a way to go

Years after the introduction of computing in Australian schools, computer classrooms are still heavily dominated by male students studying subjects which have little appeal to female students, explains the author. This article looks at why girls are less likely to choose computing as a subject to study or to consider computing for a future career.

An evolutionary computing approach to minimize dynamic hedging error

The objective of our present paper is to derive a computationally efficient genetic pattern learning algorithm to evolutionarily derive the optimal rebalancing weights (i.e. dynamic hedge ratios) to engineer a structured financial product out of a multiasset, best-of option. The stochastic target function is formulated as an expected squared cost of hedging (tracking) error which is assumed to be partly dependent on the governing Markovian process underlying the individual asset returns and partly on
randomness i.e. pure white noise. A simple haploid genetic algorithm is advanced as an alternative numerical scheme, which is deemed to be
computationally more efficient than numerically deriving an explicit solution to the formulated optimization model. An extension to our proposed scheme is suggested by means of adapting the Genetic Algorithm parameters based on fuzzy logic controllers.

Toward pervasive computing in restaurant

In this paper, an example of pervasive computing in restaurant, a wireless web-based ordering system is presented. By using mobile devices such as Personal Digital Assistants (PDA) and WebPad, customers can get many benefits when making orders in restaurants. With this system, customers get faster and better services, restaurant staff cooperate more efficiently with less working mistakes, and enterprise owners thus receive more business profits. This system has multi-tiered web-based system architecture with good integration and scalability features, and is client device operating system fully independent. Details of design and implementation of this system are presented.