Challenges in designing culturally-adaptive ICT-based learning environments

This paper argues that two of the major problems facing local courseware developers and educators in creating culturally-sensitive lCT-based learning environments are the difficulties of representing cultural artefacts in courseware and of having a model of 'culture' that can be implemented in lCT-based systems. A developing learning-object model is presented; one that can be adopted to address aspects of this problem by providing a systematic way to approach implementing and representing cultural artefacts in courseware. This also provides a basis for some aspects of computer-based cultural preservation initiatives. While this approach may have higher short term development costs, the potential long term cultural benefits to the society far outweigh short term considerations.

Towards a renewable adaptive recyclable and environmental architecture

Research in pursuit of an effective response to the demands for a sustainable architecture has lead towards the conception of a Renewable, Adaptive, Recyclable and Environmental (R.A.R.E.) building typology. The term R.A.R.E. expresses issues that have assumed central importance in the current architectural debate. This paper establishes the principles of the typology, drawing on the contents and pedagogical methods applied in a building technology academic course, at fourth year level. The R.A.R.E methodology is presented to and explored by students in the search for a definition of an innovative architecture, which is both progressive and sustainable. The unit is structured into eight subjects: Sustainable Site & Climate Analysis; Flexible & Adaptive Structural Systems; Renewable & Environmental Building Materials; Modular Building Systems; Innovative Building Envelope Systems; Renewable & Non-conventional Energy Systems; Innovative Heating, Ventilation & Air Conditioning Systems; Water Collection & Storage Systems. Through a holistic and integrated approach, the unit presents a comprehensive overview of these ‘Sustainable Building Categories’, so that the students can produce a guide towards the design requirements of a Renewable, Adaptive, Recyclable and Environmental (R.A.R.E.) Architecture.

A roadmap to Renewable Adaptive Recyclable Environmental (R.A.R.E.) architecture

R.A.R.E. stands for Renewable Adaptive Recyclable Environmental Architecture; the acronym expresses a demand that is becoming increasingly important today in the eyes of designers and clients. The paper draws on the contents and the pedagogical methods applied in a Building Technology Unit (SRT 450) – at forth year level – at the School of Architecture and Building, Deakin University, Australia. The unit is basically structured upon eight subjects derived as relevant to the research and development for a R.A.R.E. Architecture: Sustainable Site & Climate Analysis; Flexible & Adaptive Structural Systems; Renewable Adaptive & Environmental Building Materials; Modular Building Systems; Innovative Building Envelope Systems; Renewable or Non-conventional Energy Systems; Innovative Heating, Ventilation & Air Conditioning; Water Storage & Systems. The overall objective of the unit is to present a comprehensive overview of all these Sustainable Building Categories (SBCs) so that the students can produce a guide towards the design of a R.A.R.E. Architecture. The push towards a holistic and integrated approach will contribute to the definition of an innovative architecture, which is both progressive and sustainable.

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.

Adaptive load shedding for mining frequent patterns from data streams

Most algorithms that focus on discovering frequent patterns from data streams assumed that the machinery is capable of managing all the incoming transactions without any delay; or without the need to drop transactions. However, this assumption is often impractical due to the inherent characteristics of data stream environments. Especially under high load conditions, there is often a shortage of system resources to process the incoming transactions. This causes unwanted latencies that in turn, affects the applicability of the data mining models produced – which often has a small window of opportunity. We propose a load shedding algorithm to address this issue. The algorithm adaptively detects overload situations and drops transactions from data streams using a probabilistic model. We tested our algorithm on both synthetic and real-life datasets to verify the feasibility of our algorithm.

A role for skeletal muscle stearoyl-CoA desaturase 1 in control of thermogenesis

An enhanced metabolic efficiency for accelerating the recovery of fat mass (or catch-up fat) is a characteristic feature of body weight regulation after weight loss or growth retardation and is the outcome of an "adipose-specific" suppression of thermogenesis, i.e., a feedback control system in which signals from the depleted adipose tissue fat stores exert a suppressive effect on thermogenesis. Using a previously described rat model of semistarvation-refeeding in which catch-up fat results from suppressed thermogenesis per se, we report here that the gene expression of stearoyl-coenzyme A desaturase 1 (SCD1) is elevated in skeletal muscle after 2 wk of semistarvation and remains elevated in parallel to the phase of suppressed thermogenesis favoring catch-up fat during refeeding. These elevations in the SCD1 transcript are skeletal muscle specific and are associated with elevations in microsomal ^9 desaturase enzyme activity, in the ^9 desaturation index, and in the relative content of SCD1-derived monounsaturates in several lipid fractions extracted from skeletal muscle. An elevated skeletal muscle SCD1, by desaturating the products of de novo lipogenesis and diverting them away from mitochondrial oxidation, would inhibit substrate cycling between de novo lipogenesis and lipid oxidation, thereby leading to a state of suppressed thermogenesis that regulates the body’s fat stores.—Mainieri, D., Summermatter, S., Seydoux, J., Montani, J. P., Rusconi, S., Russell, A. P., Boss, O., Buchala, A. J., Dulloo, A. G. A role for skeletal muscle stearoyl-CoA desaturase 1 in control of thermogenesis.

Self-adaptive clock synchronization for computational grid

This paper presents an innovative method to synchronize physical clocks for a computational grid, in particular for a computational grid linked through the asynchronous Intranet or Internet environments. The method discussed is an asynchronous self-adaptive clock synchronization mechanism. Two strategies for clock synchronisation are introduced. (1) Use continuous time intervals to calculate the precision of clocks, which can reduce the effect of network delay efficiently. (2) Every node synchronizes its clock with its leader actively. In addition, a node self-adaptive model is presented, and the relationship between the clock precision and synchronization time is induced, hence a node can predict when it should begin the synchronization process. Detailed simulation and extension of this issue are provided at the end of the paper. The presented model is both practical and feasible.