Historical landscape languages : creating symbolic space over generations

Landscape creation takes time, patience, water, and languages of visions. Landscape ‘history’ is not short in time but progressive, evolutionary and slow. In the case of the Adelaide Park Lands, the spectre of the myth of the Park Lands as a historical statement is very evident in planning and design discourses in South Australia. It is a mental creation of a number of actors who sought initially to remove all vegetation and evidence of human sedentary occupation. These actors applied languages to argue for a certain type of landscape as well as for the human, water, plant and financial resources to construct this picture. Some were simple letters to editors, some were political statements and pronouncements, some were the actual endeavours and expressions of municipal officials and city gardeners who sought to plant and craft representations of what they thought were ideal visions, and some were consultants who equally sought to apply their ideals.

This paper considers the historiography of the Adelaide Park Lands through the languages of those who argued for and enabled its planting using their words, languages and action examples.

A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems

In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base.

Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification

In this paper, an Evolutionary Artificial Neural Network (EANN) that combines the Fuzzy ARTMAP (FAM) network and a Hybrid Evolutionary Programming (HEP) model is introduced. The proposed FAM-HEP model, which combines the strengths of FAM and HEP, is able to construct its network structure autonomously as well as to perform learning and evolutionary search and adaptation concurrently. The effectiveness of the proposed FAM-HEP network is assessed empirically using several benchmark data sets and a real medical diagnosis problem. The performance of FAM-HEP is analyzed, and the results are compared with those of FAM-EP, FAM, and other classification models. In general, the results of FAM-HEP are better than those of FAM-EP and FAM, and are comparable with those from other classification models. The study also reveals the potential of FAM-HEP as an innovative EANN model for undertaking pattern classification problems in general, and a promising computerized decision support tool for tackling medical diagnosis tasks in particular.

The use of laptop computers in programming lectures

This research explores the effect of the use of laptop computers on students’ learning experiences during lectures. Our methodology involves embedding laptops with visualization software as a learning aid during lectures. We then employ a framework of seven principles of good practice in higher education to evaluate the impact of the use of laptop computers on the learning experience of computer programming students. Overall, we found that students were highly motivated and supportive of this innovative use of laptop computers with lectures.

Using linear programming for weights identification of generalized bonferroni means in R

The generalized Bonferroni mean is able to capture some interaction effects between variables and model mandatory requirements. We present a number of weights identification algorithms we have developed in the R programming language in order to model data using the generalized Bonferroni mean subject to various preferences. We then compare its accuracy when fitting to the journal ranks dataset.

Minimizing impact of bounded uncertainty on McNaughton's scheduling algorithm via interval programming

Uncertainty of data affects decision making process as it increases the risk and the costs of the decision. One of the challenges in minimizing the impact of the bounded uncertainty on any scheduling algorithm is the lack of information, as only the upper bound and the lower bound are provided without any known probability or membership function. On the contrary, probabilistic uncertainty can use probability distributions and fuzzy uncertainty can use the membership function. McNaughton's algorithm is used to find the optimum schedule that minimizes the makespan taking into consideration the preemption of tasks. The challenge here is the bounded inaccuracy of the input parameters for the algorithm, namely known as bounded uncertain data. This research uses interval programming to minimise the impact of bounded uncertainty of input parameters on McNaughton’s algorithm, it minimises the uncertainty of the cost function estimate and increase its optimality. This research is based on the hypothesis that doing the calculations on interval values then approximate the end result will produce more accurate results than approximating each interval input then doing numerical calculations.

Community languages, linguistic isolation and social exclusion in the Australian context

The detrimental impacts of social exclusion to health and well-being are well-known and are of increasing concern around the world. For many of the population sub-groups who are most at risk of social exclusion, linguistic isolation—the inability to use and understand the majority language—is a major barrier to full participation in the life of the community as well as to full integration into the society in which its members live. This paper, using data obtained from community-based research in Melbourne, Australia, will discuss the problem of linguistic isolation in the context of Australian multicultural policy and use of languages other than English among members of culturally and linguistically diverse (CALD) communities. The experience of members of two specific CALD communities, speakers of Arabic and speakers of Indonesian, will be discussed to illustrate the impacts of linguistic isolation on health and well-being and to elucidate the relationship between CALD status and social exclusion in these communities.

Exploring second life as a learning environment for computer programming

Computer programming can be challenging for beginners because of the need to understand abstract programming concepts. In this paper, we study the use of the Second Life (SL) virtual world for learning computer programming concepts. We conduct an empirical study for learning computer programming in SL by addressing affordances of SL for experiential problem-based learning pedagogies. We present preliminary findings, the promises and the limitations of Second Life as an environment for learning computer programming.