Developing and rewarding advanced teaching expertise in higher education - a different approach

The Australian university sector is undergoing a major change, with a significant increase in the emphasis on quality teaching and learning. This change is being driven by the national government. The paper asks if the correct response to this change of emphasis should be a change in the attitude to the appropriate mix of research and teaching skills within an institution, and within individual staff of the institution. The same question could be asked of how to better develop teaching expertise in many higher education sectors globally. It is proposed that to create excellent teaching and learning within an institution may better be achieved by allowing staff to become experts in a narrow field of teaching rather than
generalists across the basics of teaching. The creation of a Community of Experts in teaching parallels the process of creating a Community of Experts in Research and can bring similar benefits in the teaching area to what it does in the research area.

Application of the adaptive reuse potential model in Hong Kong : a case study of Lui Seng Chun

There is an increasing complexity and interplay between all of the issues associated with property portfolio decisions. This paper explores the relationships between financial, environmental and social parameters associated with building adaptive reuse by way of a case study. A new model predicting adaptive reuse potential is applied to a heritage building in Hong Kong known as Lui Seng Chun. Such application can assist in the transformation of the building and property industry towards more sustainable practices, strategies and outcomes, by providing a means by which the industry can identify and rank existing buildings that have high potential for adaptive reuse. In Hong Kong's case it provides an ability for sustainable, responsive energy and natural resource management by allowing issues regarding excessive and inappropriate resource use to be identified and assessed, and appropriate management strategies to be implemented. Given the building's current age and condition, Lui Seng Chun has at least 25 years of physical life remaining. The further application of a multi-criteria sustainability evaluation tool supports the conclusion that an adaptive reuse strategy for this building will make a demonstrable contribution to the economic, social and environmental amenity of Hong Kong. The application of these techniques to other buildings with significant "embedded physical life" is highly recommended.

Enhancing web-based adaptive learning with colored timed petri net

One of the issues for Web-based learning applications is to adaptively provide personalized instructions for different learning activities. This paper proposes a high level colored timed Petri Net based approach to providing some level of adaptation for different users and learning activities. Examples are given to demonstrate how to realize adaptive interfaces and personalization. Future directions are also discussed at the end of this paper.

Architecture of adaptive spam filtering based on machine learning algorithms

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to distinguish between spam and legitimate email messages. Much work has been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection FP problem is unacceptable sometimes. In this paper, an adaptive spam filtering model has been proposed based on Machine learning (ML) algorithms which will get better accuracy by reducing FP problems. This model consists of individual and combined filtering approach from existing well known ML algorithms. The proposed model considers both individual and collective output and analyzes them by an analyzer. A dynamic feature selection (DFS) technique also proposed in this paper for getting better accuracy.

Interpreting Fanger's comfort equation within the adaptive paradigm

This paper reviews the evolution of Fanger's heat balance equation in regard of adaptive opportunities. Heat balance and adaptive response are integrated into one model as two fundamental aspects of human-environment interaction that define thermal comfort perception, rather than being seen as two concepts of alternative comfort paradigms. The paper suggests to extent Fanger's model with a heat storage term in order to account for comfort perception under transient thermal conditions, and to review Fanger's modelling assumptions in order to allow for a greater variety of adaptive response options. In the presented model heat exchange is modulated through adaptation of physiological, environmental and behavioural parameters in the human-environment system defined through Fanger's heat exchange equations. A computational prototype is implemented to determine 'comfortable' values and ranges of the six comfort dimensions alternatively to Fanger's comfort indices. Thereby values of for example 'comfortable' clothing and metabolic rate are results rather than necessary input parameters, which are difficult to determine. This approach allows generating design advice for physical, organisational and social environments based on heat balance calculation in the six-dimensional opportunity space defined through Fanger's comfort equation. A starting point for the development of a dynamic adaptive comfort model is set.

Adaptive communal detection in search of adversarial identity crime

This paper is on adaptive real-time searching of credit application data streams for identity crime with many search parameters. Specifically, we concentrated on handling our domain-specific adversarial activity problem with the adaptive Communal Analysis Suspicion Scoring (CASS) algorithm. CASS's main novel theoretical contribution is in the formulation of State-of- Alert (SoA) which sets the condition of reduced, same, or heightened watchfulness; and Parameter-of-Change (PoC) which improves detection ability with pre-defined parameter values for each SoA. With pre-configured SoA policy and PoC strategy, CASS determines when, what, and how much to adapt its search parameters to ongoing adversarial activity. The above approach is validated with three sets of experiments, where each experiment is conducted on several million real credit applications and measured with three appropriate performance metrics. Significant improvements are achieved over previous work, with the discovery of some practical insights of adaptivity into our domain.

Adaptive spike detection for resilient data stream mining

Automated adversarial detection systems can fail when under attack by adversaries. As part of a resilient data stream mining system to reduce the possibility of such failure, adaptive spike detection is attribute ranking and selection without class-labels. The first part of adaptive spike detection requires weighing all attributes for spiky-ness to rank them. The second part involves filtering some attributes with extreme weights to choose the best ones for computing each example’s suspicion score. Within an identity crime detection domain, adaptive spike detection is validated on a few million real credit applications with adversarial activity. The results are F-measure curves on eleven experiments and relative weights discussion on the best experiment. The results reinforce adaptive spike detection’s effectiveness for class-label-free attribute ranking and selection.