The impact of snares on the continuity of adolescent-onset antisocial behaviour: A test of Moffitt's developmental taxonomy

Moffitt’s dual typology of ‘life-course persistent’ and ‘adolescence limited’ offending has received extensive empirical attention, but the extent to which the antisocial behaviour of adolescence limited offenders is constrained to adolescence is relatively under-examined.Using data from the Australian Mater University Study of Pregnancy and its Outcomes, we explore Moffitt’s concept of snares, or those factors that may lead to an adolescent persisting in antisocial behaviour such as drug addiction, educational failure, and contact with the justice system. The Mater University Study of Pregnancy and its Outcomes is a longitudinal study of mother–child dyads from the pre-natal stage to 21 years of age. Findings show that one-third of individuals identified as having an adolescent onset of antisocial behaviour persisted with this antisocial behaviour as young adults. This continuity can, in part, be explained by snares and the research suggests that reducing exposure to snares may lead to less antisocial behaviour in adulthood.

Quality-aware review selection based on product feature taxonomy

As of today, user-generated information such as online reviews has become increasingly significant for customers in decision making process. Meanwhile, as the volume of online reviews proliferates, there is an insistent demand to help the users tackle the information overload problem. In order to extract useful information from overwhelming reviews, considerable work has been proposed such as review summarization and review selection. Particularly, to avoid the redundant information, researchers attempt to select a small set of reviews to represent the entire review corpus by preserving its statistical properties (e.g., opinion distribution). However, one significant drawback of the existing works is that they only measure the utility of the extracted reviews as a whole without considering the quality of each individual review. As a result, the set of chosen reviews may consist of low-quality ones even its statistical property is close to that of the original review corpus, which is not preferred by the users. In this paper, we proposed a review selection method which takes review quality into consideration during the selection process. Specifically, we examine the relationships between product features based upon a domain ontology to capture the review characteristics based on which to select reviews that have good quality and preserve the opinion distribution as well. Our experimental results based on real world review datasets demonstrate that our proposed approach is feasible and able to improve the performance of the review selection effectively.

Taxonomy of factors which influence heavy metal build-up on urban road surfaces

Heavy metals build-up on urban road surfaces is a complex process and influenced by a diverse range of factors. Although numerous research studies have been conducted in the area of heavy metals build-up, limited research has been undertaken to rank these factors in terms of their influence on the build-up process. This results in limitations in the identification of the most critical factor/s for accurately estimating heavy metal loads and for designing effective stormwater treatment measures. The research study undertook an in-depth analysis of the factors which influence heavy metals build-up based on data generated from a number of different geographical locations around the world. Traffic volume was found to be the highest ranked factor in terms of influencing heavy metals build-up while land use was ranked the second. Proximity to arterial roads, antecedent dry days and road surface roughness has a relatively lower ranking. Furthermore, the study outcomes advances the conceptual understanding of heavy metals build-up based on the finding that with increasing traffic volume, total heavy metal build-up load increases while the variability decreases. The outcomes from this research study are expected to contribute to more accurate estimation of heavy metals build-up loads leading to more effective stormwater treatment design.

The construction of a taxonomy of early childhood educators’ work

Despite considerable research on the impact of early childhood education (ECE), there is little empirical evidence about what the everyday work of educators entails. This paper reports on the development of a tool to capture generalisable data on the everyday work of educators so as to inform effective workforce policy. This tool—a taxonomy of early childhood educators’ work—was developed by drawing on the expertise of six early years’ experts from Australia and the United States of America (USA) and includes time-use diaries, focus groups and interviews with 21 early childhood educators working in long day care and preschool services. The taxonomy, which we present here, consists of 10 domains, each with a number of sub-classes. We propose that this taxonomy is a useful codification system for ascertaining the everyday work tasks, activities and actions of early childhood educators in diverse early years’ settings. It is anticipated that the taxonomy will prove a valuable tool for subsequent research investigating the early childhood workforce.

Taxonomy and redescription of the Swamp Antechinus, Antechinus minimus (E. Geoffroy) (Marsupialia: Dasyuridae)

We provide a taxonomic redescription of the dasyurid marsupial Swamp Antechinus, Antechinus minimus (Geoffroy, 1803). In the past, A. minimus has been classified as two subspecies: the nominate A. minimus minimus (Geoffroy, 1803), which is found throughout much of Tasmania (including southern Bass Strait islands) and A. minimus maritimus (Finlayson, 1958), which is found on mainland Australia (as well as some near-coastal islands) and is patchily distributed in mostly coastal areas between South Gippsland (Victoria) and Robe (South Australia). Based on an assessment of morphology and DNA, we conclude that A. minimus is both distinctly different from all extant congeners and that the two existing subspecies of Swamp Antechinus are appropriately taxonomically characterised. In our genetic phylogenies, the Swamp Antechinus was monophyletic with respect to all 14 known extant congeners; moreover, A. minimus was well-positioned in a large clade, together with all four species in the Dusky Antechinus complex, to the exclusion of all other antechinus. Within A. minimus, between subspecies there were subtle morphological differences (A. m. maritimus skulls tend to be broader, with larger molar teeth, than A. m. minimus, but these differences were not significant); there was distinct, but only moderately deep genetic differences (3.9–4.5% at mtDNA) between A. minimus subspecies. Comparatively, across Bass Strait, the two subspecies of A. minimus are morphologically and genetically markedly less divergent than recently recognised species pairs within the Dusky Antechinus complex, found in Victoria (A. mimetes) and Tasmania (A. swainsonii) (9.4–11.6% divergent at mtDNA)

Integrating science through Bayesian Belief Networks : case study of Lyngbya in Moreton Bay

Bayesian Belief Networks (BBNs) are emerging as valuable tools for investigating complex ecological problems. In a BBN, the important variables in a problem are identified and causal relationships are represented graphically. Underpinning this is the probabilistic framework in which variables can take on a finite range of mutually exclusive states. Associated with each variable is a conditional probability table (CPT), showing the probability of a variable attaining each of its possible states conditioned on all possible combinations of it parents. Whilst the variables (nodes) are connected, the CPT attached to each node can be quantified independently. This allows each variable to be populated with the best data available, including expert opinion, simulation results or observed data. It also allows the information to be easily updated as better data become available ----- ----- This paper reports on the process of developing a BBN to better understand the initial rapid growth phase (initiation) of a marine cyanobacterium, Lyngbya majuscula, in Moreton Bay, Queensland. Anecdotal evidence suggests that Lyngbya blooms in this region have increased in severity and extent over the past decade. Lyngbya has been associated with acute dermatitis and a range of other health problems in humans. Blooms have been linked to ecosystem degradation and have also damaged commercial and recreational fisheries. However, the causes of blooms are as yet poorly understood.

Search engine content analysis

Search engines have forever changed the way people access and discover knowledge, allowing information about almost any subject to be quickly and easily retrieved within seconds. As increasingly more material becomes available electronically the influence of search engines on our lives will continue to grow. This presents the problem of how to find what information is contained in each search engine, what bias a search engine may have, and how to select the best search engine for a particular information need. This research introduces a new method, search engine content analysis, in order to solve the above problem. Search engine content analysis is a new development of traditional information retrieval field called collection selection, which deals with general information repositories. Current research in collection selection relies on full access to the collection or estimations of the size of the collections. Also collection descriptions are often represented as term occurrence statistics. An automatic ontology learning method is developed for the search engine content analysis, which trains an ontology with world knowledge of hundreds of different subjects in a multilevel taxonomy. This ontology is then mined to find important classification rules, and these rules are used to perform an extensive analysis of the content of the largest general purpose Internet search engines in use today. Instead of representing collections as a set of terms, which commonly occurs in collection selection, they are represented as a set of subjects, leading to a more robust representation of information and a decrease of synonymy. The ontology based method was compared with ReDDE (Relevant Document Distribution Estimation method for resource selection) using the standard R-value metric, with encouraging results. ReDDE is the current state of the art collection selection method which relies on collection size estimation. The method was also used to analyse the content of the most popular search engines in use today, including Google and Yahoo. In addition several specialist search engines such as Pubmed and the U.S. Department of Agriculture were analysed. In conclusion, this research shows that the ontology based method mitigates the need for collection size estimation.

Protocol engineering for protection against denial-of-service attacks

Denial-of-service attacks (DoS) and distributed denial-of-service attacks (DDoS) attempt to temporarily disrupt users or computer resources to cause service un- availability to legitimate users in the internetworking system. The most common type of DoS attack occurs when adversaries °ood a large amount of bogus data to interfere or disrupt the service on the server. The attack can be either a single-source attack, which originates at only one host, or a multi-source attack, in which multiple hosts coordinate to °ood a large number of packets to the server. Cryptographic mechanisms in authentication schemes are an example ap- proach to help the server to validate malicious tra±c. Since authentication in key establishment protocols requires the veri¯er to spend some resources before successfully detecting the bogus messages, adversaries might be able to exploit this °aw to mount an attack to overwhelm the server resources. The attacker is able to perform this kind of attack because many key establishment protocols incorporate strong authentication at the beginning phase before they can iden- tify the attacks. This is an example of DoS threats in most key establishment protocols because they have been implemented to support con¯dentiality and data integrity, but do not carefully consider other security objectives, such as availability. The main objective of this research is to design denial-of-service resistant mechanisms in key establishment protocols. In particular, we focus on the design of cryptographic protocols related to key establishment protocols that implement client puzzles to protect the server against resource exhaustion attacks. Another objective is to extend formal analysis techniques to include DoS- resistance. Basically, the formal analysis approach is used not only to analyse and verify the security of a cryptographic scheme carefully but also to help in the design stage of new protocols with a high level of security guarantee. In this research, we focus on an analysis technique of Meadows' cost-based framework, and we implement DoS-resistant model using Coloured Petri Nets. Meadows' cost-based framework is directly proposed to assess denial-of-service vulnerabil- ities in the cryptographic protocols using mathematical proof, while Coloured Petri Nets is used to model and verify the communication protocols using inter- active simulations. In addition, Coloured Petri Nets are able to help the protocol designer to clarify and reduce some inconsistency of the protocol speci¯cation. Therefore, the second objective of this research is to explore vulnerabilities in existing DoS-resistant protocols, as well as extend a formal analysis approach to our new framework for improving DoS-resistance and evaluating the performance of the new proposed mechanism. In summary, the speci¯c outcomes of this research include following results; 1. A taxonomy of denial-of-service resistant strategies and techniques used in key establishment protocols; 2. A critical analysis of existing DoS-resistant key exchange and key estab- lishment protocols; 3. An implementation of Meadows's cost-based framework using Coloured Petri Nets for modelling and evaluating DoS-resistant protocols; and 4. A development of new e±cient and practical DoS-resistant mechanisms to improve the resistance to denial-of-service attacks in key establishment protocols.

What do clients learn from counseling?

Thirty-five clients who received counseling participated in this exploratory study by completing a letter to a friend that described in as much detail as possible what they had learned from counseling. The participants’ written responses were analyzed using a content analysis approach. The analysis indicated that the data were best categorized in terms of three broad areas of learnings (Self, Relations with Others, and the Process of Learning and Change). The Self taxonomy was found to consist of six hierarchical levels. The Relations with Others taxonomy consisted of five hierarchical levels, while the Process of Learning and Change taxonomy consisted of five hierarchical levels. The results suggested that these three taxonomies offer a promising and exciting way to view the impact of counseling within a learning framework. If these taxonomies are found to be stable in future research and clients are easily classified using the taxonomies then this approach may have implications for counseling. It may well be that to maximise the learnings counselors could use specific strategies and techniques to enhance their clients’ learning in the three areas.

Bayesian model averaging for harmful algal bloom prediction

Harmful Algal Blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been developed, and in common with most predictive models in ecology, those that have been developed do not fully account for uncertainties in parameters and model structure. This makes management decisions based on these predictions more risky than might be supposed. We used a probit time series model and Bayesian Model Averaging (BMA) to predict occurrences of blooms of Lyngbya majuscula, a toxic cyanophyte, in Deception Bay, Queensland, Australia. We found a suite of useful predictors for HAB occurrence, with Temperature figuring prominently in models with the majority of posterior support, and a model consisting of the single covariate average monthly minimum temperature showed by far the greatest posterior support. A comparison of alternative model averaging strategies was made with one strategy using the full posterior distribution and a simpler approach that utilised the majority of the posterior distribution for predictions but with vastly fewer models. Both BMA approaches showed excellent predictive performance with little difference in their predictive capacity. Applications of BMA are still rare in ecology, particularly in management settings. This study demonstrates the power of BMA as an important management tool that is capable of high predictive performance while fully accounting for both parameter and model uncertainty.

Location constraints in digital rights management

Digital rights management allows information owners to control the use and dissemination of electronic documents via a machine-readable licence. This paper describes the design and implementation of a system for creating and enforcing licences containing location constraints that can be used to restrict access to sensitive documents to a defined area. Documents can be loaded onto a portable device and used in the approved areas, but cannot be used if the device moves to another area. Our contribution includes a taxonomy for access control in the presence of requests to perform non-instantaneous controlled actions.

A study of entrepreneurship : Taiwanese digital content companies in China

China’s accession to the World Trade Organisation (WTO) has greatly enhanced global interest in investment in the Chinese media market, where demand for digital content is growing rapidly. The East Asian region is positioned as a growth area in many forms of digital content and digital service industries. China is attempting to catch up and take its place as a production centre to offset challenges from neighbouring countries. Meanwhile, Taiwan is seeking to use China both as an export market and as a production site for its digital content. This research investigates entry strategies of Taiwanese digital content firms into the Chinese market. By examining the strategies of a sample of Taiwan-based companies, this study also explores the evolution of their market strategies. However, the focus is on how distinctive business practices such as guanxi are important to Taiwanese business and to relations with Mainland China. This research examines how entrepreneurs manage the characteristics of digital content products and in turn how digital content entrepreneurs adapt to changing market circumstances. This project selected five Taiwan-based digital content companies that have business operations in China: Wang Film, Artkey, CnYES, Somode and iPartment. The study involved a field trip, undertaken between November 2006 and March 2007 to Shanghai and Taiwan to conduct interviews and to gather documentation and archival reports. Six senior managers and nine experts were interviewed. Data were analysed according to Miller’s firm-level entrepreneurship theory, foreign direct investment theory, Life Cycle Model and guanxi philosophy. Most studies of SMEs have focused on free market (capitalist) environments. In contrast, this thesis examines how Taiwanese digital content firms’ strategies apply in the Chinese market. I identified three main types of business strategy: cost-reduction, innovation and quality-enhancement; and four categories of functional strategies: product, marketing, resource acquisition and organizational restructuring. In this study, I introduce the concept of ‘entrepreneurial guanxi’, special relationships that imply mutual obligation, assurance and understanding to secure and exchange favors in entrepreneurial activities. While guanxi is a feature of many studies of business in Pan-Chinese society, it plays an important mediating role in digital content industries. In this thesis, I integrate the ‘Life Cycle Model’ with the dynamic concept of strategy. I outline the significant differences in the evolution of strategy between two types of digital content companies: off-line firms (Wang Film and Artkey) and web-based firms (CnYES, Somode and iPartment). Off-line digital content firms tended to adopt ‘resource acquisition strategies’ in their initial stages and ‘marketing strategies’ in second and subsequent stages. In contrast, web-based digital content companies mainly adopted product and marketing strategies in the early stages, and would adopt innovative approaches towards product and marketing strategies in the whole process of their business development. Some web-based digital content companies also adopted organizational restructuring strategies in the final stage. Finally, I propose the ‘Taxonomy Matrix of Entrepreneurial Strategies’ to emphasise the two dimensions of this matrix: innovation, and the firm’s resource acquisition for entrepreneurial strategy. This matrix is divided into four cells: Effective, Bounded, Conservative, and Impoverished.

Information enrichment for quality recommender systems

The explosive growth of the World-Wide-Web and the emergence of ecommerce are the major two factors that have led to the development of recommender systems (Resnick and Varian, 1997). The main task of recommender systems is to learn from users and recommend items (e.g. information, products or books) that match the users’ personal preferences. Recommender systems have been an active research area for more than a decade. Many different techniques and systems with distinct strengths have been developed to generate better quality recommendations. One of the main factors that affect recommenders’ recommendation quality is the amount of information resources that are available to the recommenders. The main feature of the recommender systems is their ability to make personalised recommendations for different individuals. However, for many ecommerce sites, it is difficult for them to obtain sufficient knowledge about their users. Hence, the recommendations they provided to their users are often poor and not personalised. This information insufficiency problem is commonly referred to as the cold-start problem. Most existing research on recommender systems focus on developing techniques to better utilise the available information resources to achieve better recommendation quality. However, while the amount of available data and information remains insufficient, these techniques can only provide limited improvements to the overall recommendation quality. In this thesis, a novel and intuitive approach towards improving recommendation quality and alleviating the cold-start problem is attempted. This approach is enriching the information resources. It can be easily observed that when there is sufficient information and knowledge base to support recommendation making, even the simplest recommender systems can outperform the sophisticated ones with limited information resources. Two possible strategies are suggested in this thesis to achieve the proposed information enrichment for recommenders: • The first strategy suggests that information resources can be enriched by considering other information or data facets. Specifically, a taxonomy-based recommender, Hybrid Taxonomy Recommender (HTR), is presented in this thesis. HTR exploits the relationship between users’ taxonomic preferences and item preferences from the combination of the widely available product taxonomic information and the existing user rating data, and it then utilises this taxonomic preference to item preference relation to generate high quality recommendations. • The second strategy suggests that information resources can be enriched simply by obtaining information resources from other parties. In this thesis, a distributed recommender framework, Ecommerce-oriented Distributed Recommender System (EDRS), is proposed. The proposed EDRS allows multiple recommenders from different parties (i.e. organisations or ecommerce sites) to share recommendations and information resources with each other in order to improve their recommendation quality. Based on the results obtained from the experiments conducted in this thesis, the proposed systems and techniques have achieved great improvement in both making quality recommendations and alleviating the cold-start problem.