Passing time in Clifton Court : an ethnographic study of a unit for confused older people

An exploration of how the residents of a 30 bed unit for confused older people spent their time and a description of the impact upon this of any associated factors. The study demonstrates that it is possible for for elders with dementia and mental health problems to enjoy a range of activities, however, these need to be individualised to take into account the skills they have to meet the challenges with which they are presented. The informed contribution from the activities personnel and the support of nurses is vital to ensure a high quality of social interaction. Sound leadership at the clinical level is essential.

A study on reliability in graph discovery

A critical question in data mining is that can we always trust what discovered by a data mining system unconditionally? The answer is obviously not. If not, when can we trust the discovery then? What are the factors that affect the reliability of the discovery? How do they affect the reliability of the discovery? These are some interesting questions to be investigated.

In this paper we will firstly provide a definition and the measurements of reliability, and analyse the factors that affect the reliability. We then examine the impact of model complexity, weak links, varying sample sizes and the ability of different learners to the reliability of graphical model discovery. The experimental results reveal that (1) the larger sample size for the discovery, the higher reliability we will get; (2) the stronger a graph link is, the easier the discovery will be and thus the higher the reliability it can achieve; (3) the complexity of a graph also plays an important role in the discovery. The higher the complexity of a graph is, the more difficult to induce the graph and the lower reliability it would be.

Reducing performance bias for unbalanced text mining

In text categorization applications, class imbalance, which refers to an uneven data distribution where one class is represented by far more less instances than the others, is a commonly encountered problem. In such a situation, conventional classifiers tend to have a strong performance bias, which results in high accuracy rate on the majority class but very low rate on the minorities. An extreme strategy for unbalanced, learning is to discard the majority instances and apply one-class classification to the minority class. However, this could easily cause another type of bias, which increases the accuracy rate on minorities by sacrificing the majorities. This paper aims to investigate approaches that reduce these two types of performance bias and improve the reliability of discovered classification rules. Experimental results show that the inexact field learning method and parameter optimized one-class classifiers achieve more balanced performance than the standard approaches.

Water, land use change and 'new forests': what are the challenges for South-Western Victoria?

Land use change has occurred rapidly in southwestern Victoria over the last decade and is expected to continue, albeit at a slower pace. One of these changes has been the development of 'new forests', that is industrial and farm forestry plantations and environmental plantings. Some of the challenges that these land use changes pose for water and natural resource managers are discussed. Land use change is expected to substantially reduce potential water yield in four of the region's seven drainage basins (A).

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.

Utility of real-time decision-making in commercial data stream mining domains

The objective is to measure utility of real-time commercial decision making. It is important due to a higher possibility of mistakes in real-time decisions, problems with recording actual occurrences, and significant costs associated with predictions produced by algorithms. The first contribution is to use overall utility and represent individual utility with a monetary value instead of a prediction. The second is to calculate the benefit from predictions using the utility-based decision threshold. The third is to incorporate cost of predictions. For experiments, overall utility is used to evaluate communal and spike detection, and their adaptive versions. The overall utility results show that with fewer alerts, communal detection is better than spike detection. With more alerts, adaptive communal and spike detection are better than their static versions. To maximise overall utility with all algorithms, only 1% to 4% in the highest predictions should be alerts.

On the communal analysis suspicion scoring for identity crime in streaming credit applications

This paper describes a rapid technique: communal analysis suspicion scoring (CASS), for generating numeric suspicion scores on streaming credit applications based on implicit links to each other, over both time and space. CASS includes pair-wise communal scoring of identifier attributes for applications, definition of categories of suspiciousness for application-pairs, the incorporation of temporal and spatial weights, and smoothed k-wise scoring of multiple linked application-pairs. Results on mining several hundred thousand real credit applications demonstrate that CASS reduces false alarm rates while maintaining reasonable hit rates. CASS is scalable for this large data sample, and can rapidly detect early symptoms of identity crime. In addition, new insights have been observed from the relationships between applications.

Nutritional related aspects of the artificial propagation and larval rearing of Macquarie perch, Macquaria Australasica (Cuvier, 1830)

Studies the underlying role of nutrition in the lack of response of captive fish to hypophysation. Aspects studied include morphological characteristics, histology of ovaries, proximate analysis, fatty and amino acid profiles of oocytes, muscle, liver and diets of wild and tank-reared fish, egg and larval quality, amino acid composition of eggs and larvae at different developmental stages, larval feeding and hormone treatments.

Oral sensitivity to fatty acids, food consumption and BMI in human subjects

Fatty acids are the chemical moieties that are thought to stimulate oral nutrient sensors, which detect the fat content of foods. In animals, oral hypersensitivity to fatty acids is associated with decreased fat intake and body weight. The aims of the present study were to investigate oral fatty acid sensitivity, food selection and BMI in human subjects. The study included two parts; study 1 established in thirty-one subjects (29 (sem 1&middot;4) years, 22&middot;8 (sem 0&middot;5) kg/m<sup>2) taste thresholds using 3-AFC (3-Alternate Forced Choice Methodology) for oleic, linoleic and lauric acids, and quantified oral lipase activity. During study 2, fifty-four subjects (20 (sem 0&middot;3) years, 21&middot;5 (sem 0&middot;4) kg/m2) were screened for oral fatty acid sensitivity using oleic acid (1&middot;4 mm), and they were defined as hypo- or hypersensitive via triplicate triangle tests. Habitual energy and macronutrient intakes were quantified from 2 d diet records, and BMI was calculated from height and weight. Subjects also completed a fat ranking task using custard containing varying amounts (0, 2, 6 and 10 %) of fat. Study 1 reported median lipase activity as 2 &mu;mol fatty acids/min per l, and detection thresholds for oleic, linoleic and lauric acids were 2&middot;2 (sem 0&middot;1), 1&middot;5 (sem 0&middot;1) and 2&middot;6 (sem 0&middot;3) mm. Study 2 identified twelve hypersensitive subjects, and hypersensitivity was associated with lower energy and fat intakes, lower BMI (P < 0&middot;05) and an increased ability to rank custards based on fat content (P < 0&middot;05). Sensitivity to oleic acid was correlated to performance in the fat ranking task (r 0&middot;4, P < 0&middot;05). These data suggest that oral fatty acid hypersensitivity is associated with lower energy and fat intakes and BMI, and it may serve as a factor that influences fat consumption in human subjects.