Application of a brain inspired model for profiling multi-view crime patterns

With the massive amount of crime data generated daily, this has put law enforcement under intensive stress. This means that law enforcement has to compete against the time to solve crime. In addition, the focus of crime investigation has been expanded from the ability to catch the criminals towards the ability to act before a crime happens (i.e pre-crime). Given such situation, creation of crime profiles is very important to law enforcement, especially in understanding the behaviours of criminals and identifying the characteristics of similar crimes. In fact, crime profiles could be used to solve similar crimes and thus pre-crime action could be conducted. In this paper, a brain inspired conceptual model is proposed and a structurally adaptive neural network is deployed for its implementation. Subsequently, the proposed model is applied for the identification and presentation of multi-view crime patterns. Such multi-view crime patterns could be useful for the construction of crime profiles. Moreover, the suitability of the proposed model in crime profiling is discussed and demonstrated through some experimental results.

Growing Self Organizing Map with an Imposed Binary Search Tree for Discovering Temporal Input Patterns

In this paper the Binary Search Tree Imposed Growing Self Organizing Map (BSTGSOM) is presented as an extended version of the Growing Self Organizing Map (GSOM), which has proven advantages in knowledge discovery applications. A Binary Search Tree imposed on the GSOM is mainly used to investigate the dynamic perspectives of the GSOM based on the inputs and these generated temporal patterns are stored to further analyze the behavior of the GSOM based on the input sequence. Also, the performance advantages are discussed and compared with that of the original GSOM.

Determining trends and environmental drivers from long-term marine mammal and seabird data : examples from Southern Australia

Climate change is acknowledged as an emerging threat for top-order marine predators, yet obtaining evidence of impacts is often difficult. In south-eastern Australia, a marine global warming hotspot, evidence suggests that climate change will profoundly affect pinnipeds and seabirds. Long-term data series are available to assess some species' responses to climate. Researchers have measured a variety of chronological and population variables, such as laying dates, chick or pup production, colony-specific abundance and breeding success. Here, we consider the challenges in accurately assessing trends in marine predator data, using long-term data series that were originally collected for other purposes, and how these may be driven by environmental change and variability. In the past, many studies of temporal changes and environmental drivers used linear analyses and we demonstrate the (theoretical) relationship between the magnitude of a trend, its variability, and the duration of a data series required to detect a linear trend. However, species may respond to environmental change in a nonlinear manner and, based on analysis of time-series from south-eastern Australia, it appears that the assumptions of a linear model are often violated, particularly for measures of population size. The commonly measured demographic variables exhibit different degrees of variation, which influences the ability to detect climate signals. Due to their generally lower year-to-year variability, we illustrate that monitoring of variables such as mass and breeding chronology should allow detection of temporal trends earlier in a monitoring programme than observations of breeding success and population size. Thus, establishing temporal changes with respect to climate change from a monitoring programme over a relatively short time period requires careful a priori choice of biological variables. © 2014 Springer-Verlag Berlin Heidelberg.

Examining the effect of time constraint on the online mastery learning approach towards improving postgraduate students' achievement

Time control plays a critical role within the online mastery learning (OML) approach. This paper examines the two commonly implemented mastery learning strategies – personalised system of instructions and learning for mastery (LFM) – by focusing on what occurs when there is an instructional time constraint. Using a large data set from a postgraduate finance course offered at an Australian university, we explore students' online quiz-completion patterns, then empirically investigate whether the imposition of an instructional time constraint in the OML approach has an impact on their final-examination performance. Our results suggest that the LFM strategy with an instructional time constraint has a positive impact on students' learning behaviour and contributes to better overall academic performance. Further, our findings suggest that facilitators should be encouraged to implement an instructional time constraint when adopting an OML approach.

Bayesian classification of catchments using spatial data: a first step to improved modelling of catchment effects on stream ecological condition

A major challenge facing freshwater ecologists and managers is the development of models that link stream ecological condition to catchment scale effects, such as land use. Previous attempts to make such models have followed two general approaches. The bottom-up approach employs mechanistic models, which can quickly become too complex to be useful. The top-down approach employs empirical models derived from large data sets, and has often suffered from large amounts of unexplained variation in stream condition.

We believe that the lack of success of both modelling approaches may be at least partly explained by scientists considering too wide a breadth of catchment type. Thus, we believe that by stratifying large sets of catchments into groups of similar types prior to modelling, both types of models may be improved. This paper describes preliminary work using a Bayesian classification software package, ‘Autoclass’ (Cheeseman and Stutz 1996) to create classes of catchments within the Murray Darling Basin based on physiographic data.

Autoclass uses a model-based classification method that employs finite mixture modelling and trades off model fit versus complexity, leading to a parsimonious solution. The software provides information on the posterior probability that the classification is ‘correct’ and also probabilities for alternative classifications. The importance of each attribute in defining the individual classes is calculated and presented, assisting description of the classes. Each case is ‘assigned’ to a class based on membership probability, but the probability of membership of other classes is also provided. This feature deals very well with cases that do not fit neatly into a larger class. Lastly, Autoclass requires the user to specify the measurement error of continuous variables.

Catchments were derived from the Australian digital elevation model. Physiographic data werederived from national spatial data sets. There was very little information on measurement errors for the spatial data, and so a conservative error of 5% of data range was adopted for all continuous attributes. The incorporation of uncertainty into spatial data sets remains a research challenge.

The results of the classification were very encouraging. The software found nine classes of catchments in the Murray Darling Basin. The classes grouped together geographically, and followed altitude and latitude gradients, despite the fact that these variables were not included in the classification. Descriptions of the classes reveal very different physiographic environments, ranging from dry and flat catchments (i.e. lowlands), through to wet and hilly catchments (i.e. mountainous areas). Rainfall and slope were two important discriminators between classes. These two attributes, in particular, will affect the ways in which the stream interacts with the catchment, and can thus be expected to modify the effects of land use change on ecological condition. Thus, realistic models of the effects of land use change on streams would differ between the different types of catchments, and sound management practices will differ.

A small number of catchments were assigned to their primary class with relatively low probability. These catchments lie on the boundaries of groups of catchments, with the second most likely class being an adjacent group. The locations of these ‘uncertain’ catchments show that the Bayesian classification dealt well with cases that do not fit neatly into larger classes.

Although the results are intuitive, we cannot yet assess whether the classifications described in this paper would assist the modelling of catchment scale effects on stream ecological condition. It is most likely that catchment classification and modelling will be an iterative process, where the needs of the model are used to guide classification, and the results of classifications used to suggest further refinements to models.

Beyond the topological map : developing alternate mappings in self organisation

The self organising map is a well established unsupervised
learning technique which is able to form sophisticated representations of an input data set. However, conventional Self Organising Map (SOM) algorithms are limited to the production of topological maps — that is, maps where distance between points on the map have a direct relationship to the Euclidean distance between the training vectors corresponding to those points.

It would be desirable to be able to create maps which form clusters on primitive attributes other than Euclidean distance; for example, clusters based upon orientation or shape. Such maps could provide a novel approach to pattern recognition tasks by providing a new method to associate groups of data.

In this paper, it is shown that the type of map produced by SOM algorithms is a direct consequence of the lateral connection strategy employed. Given this knowledge, a technique is required to establish the feasability of using an alternative lateral connection strategy. Such a technique is presented. Using this technique, it is possible to rule out lateral connection strategies that will not produce output states useful to the organisation process. This technique is demonstrated using conventional Laplacian interconnection as well as a number of novel interconnection strategies.

Using supervised and unsupervised techniques to determine groups of patients with different doctor-patient stability

Decision trees and self organising feature maps (SOFM) are frequently used to identify groups. This research aims to compare the similarities between any groupings found between supervised (Classification and Regression Trees - CART) and unsupervised classification (SOFM), and to identify insights into factors associated with doctor-patient stability. Although CART and SOFM uses different learning paradigms to produce groupings, both methods came up with many similar groupings. Both techniques showed that self perceived health and age are important indicators of stability. In addition, this study has indicated profiles of patients that are at risk which might be interesting to general practitioners.

Aggressive tax planning : differentiating those playing the game from those who don't

The study reported in this paper examined a group of Australian taxpayers who have expressed a preference for a creative and aggressive tax agent. The study attempted to understand how high-risk taxpayers and high-risk practitioners form their partnerships by examining aggressive taxpayers' attitudes and perceptions of the Australian tax system. Data were taken from 2040 Australian taxpayers who had responded to a national survey on tax issues. Results from a series of independent sample t-tests revealed that there are a number of important differences between 'aggressive' and 'non-aggressive' taxpayers. Finally, a logistic regression analysis was used to determine which variables most effectively differentiated aggressive taxpayers from non-aggressive taxpayers. The findings are discussed in a regulatory context and possible solutions for how tax authorities might deal with this high-risk group of taxpayer are suggested.

Queensland arts cuts: the real story

In the first part of an exclusive 3-part series, Ben Eltham exposes the machinations and implications of money and mates in Queensland's arts sector. Article investigates the behind-the-scenes policy decisions inside the Queensland government that resulted in significant funding cuts to small-to-medium cultural organsations in Queensland in late 2013.

Consistency and stability in aggregation operators : an application to missing data problems

In this work we analyze the key issue of the relationship that should hold between the operators in a family {An} of aggregation operators in order to understand they properly define a consistent whole. Here we extend some of the ideas about stability of a family of aggregation operators into a more general framework, formally defining the notions of i – L and j – R strict stability for families of aggregation operators. The notion of strict stability of order k is introduced as well. Finally, we also present an application of the strict stability conditions to deal with missing data problems in an information aggregation process. For this analysis, we have focused in the weighted mean family and the quasi-arithmetic weighted means families.

An assessment of existing bicycle advisory pavement marking on urban roads

Bicycle advisory treatments are used to advise road users of the potential presence of cyclists and of the location where cyclists may be expected to ride on a road. They include pavement markings, warning signs, guide signs, and as such have no regulatory function. The most common type of bicycle advisory pavement markings is the shared lane marking. Other forms of bicycle advisory pavement marking have also been trialled and used in several local jurisdictions. The bicycle awareness zone is an example of such facility which has been trialled and used in southeast Queensland, Australia since the late 1990s. A bicycle awareness zone is similar to shared lane marking in principle but differs in the type of logo and, in some cases, location of its placement on the road. This study assesses the operational and safety issues at three bicycle awareness zone sites by analysing video-assisted observation data collected in 2011 by Queensland Department of Transport and Main Roads, Australia. Of the several applications of bicycle awareness zones, this study only covers a particular application where the centre of the bicycle symbol is placed exactly over the parking edge line. Unlike previous studies, which mostly covered before-and-after evaluations of bicycle advisory pavement markings, the focus of this study is to assess whether the placement of bicycle awareness zone symbols has been successful. The aggregated results from video-assisted observational data show that the cyclists did not always track themselves over the centre of the symbols. Rather, both the cyclists' lateral tracking positions and road user interactions varied with the widths of kerbside parallel parking space. Since the bicycle awareness zone symbols are not positioned on the cyclists' desired line of ride on some roads, their operational effectiveness and safety value are questioned.