Sustaining the next generation of teacher-researchers to work for social justice

This chapter explores a research project involving teachers working with some of the most disadvantaged young people in South Australia, children growing up in poverty, in families struggling with homelessness and ill-health, in the outer southern suburbs. Additionally, there were particular children were struggling with intellectual, emotional and social difficulties which were extreme enough for them not be included in a mainstream class. The research project made two crucial interrelated moves to support teachers to tackle this tough work. First, the project had an explicit social justice agenda. We were not simply researching literacy outcomes, but literacy pedagogies for the students teachers were most worried about. And we wanted to understand how the material conditions of students’ everyday lifeworlds impacted on the working conditions of teachers’ schoolworlds. We sought to open up a discursive space where teachers could talk about poverty, violence, racism and classism in ways that would take them beyond despair and into new imaginings and positive action. Second, the project was designed to start from the urgent questions of early career teachers and to draw on the accumulated practice wisdom of their chosen mentors. Hence we designed not only a teacher-researcher community, but cross-generational networks. Our aim was to build the capacities of both generations to address long-standing educational problems in new ways that drew overtly on their different and complementary resources.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Hybrid-Game Strategies for multi-objective design optimization in engineering

A number of Game Strategies (GS) have been developed in past decades. They have been used in the fields of economics, engineering, computer science and biology due to their efficiency in solving design optimization problems. In addition, research in multi-objective (MO) and multidisciplinary design optimization (MDO) has focused on developing robust and efficient optimization methods to produce a set of high quality solutions with low computational cost. In this paper, two optimization techniques are considered; the first optimization method uses multi-fidelity hierarchical Pareto optimality. The second optimization method uses the combination of two Game Strategies; Nash-equilibrium and Pareto optimality. The paper shows how Game Strategies can be hybridised and coupled to Multi-Objective Evolutionary Algorithms (MOEA) to accelerate convergence speed and to produce a set of high quality solutions. Numerical results obtained from both optimization methods are compared in terms of computational expense and model quality. The benefits of using Hybrid-Game Strategies are clearly demonstrated

Comment

The support vector machine (SVM) has played an important role in bringing certain themes to the fore in computationally oriented statistics. However, it is important to place the SVM in context as but one member of a class of closely related algorithms for nonlinear classification. As we discuss, several of the “open problems” identified by the authors have in fact been the subject of a significant literature, a literature that may have been missed because it has been aimed not only at the SVM but at a broader family of algorithms. Keeping the broader class of algorithms in mind also helps to make clear that the SVM involves certain specific algorithmic choices, some of which have favorable consequences and others of which have unfavorable consequences—both in theory and in practice. The broader context helps to clarify the ties of the SVM to the surrounding statistical literature.

Overcoming the complexity of diagnostic problems due to sensor network architecture

In fault detection and diagnostics, limitations coming from the sensor network architecture are one of the main challenges in evaluating a system’s health status. Usually the design of the sensor network architecture is not solely based on diagnostic purposes, other factors like controls, financial constraints, and practical limitations are also involved. As a result, it quite common to have one sensor (or one set of sensors) monitoring the behaviour of two or more components. This can significantly extend the complexity of diagnostic problems. In this paper a systematic approach is presented to deal with such complexities. It is shown how the problem can be formulated as a Bayesian network based diagnostic mechanism with latent variables. The developed approach is also applied to the problem of fault diagnosis in HVAC systems, an application area with considerable modeling and measurement constraints.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Lessons learned from implementation of a demonstration program to reduce the burden of anemia and hookworm in women in Yen Bai Province, Viet Nam

Background Iron deficiency, anemia and hookworm disease are important public health problems for women of reproductive age living in developing countries and affect the health of newborns and infants. Iron supplementation and deworming treatment are effective in addressing these problems in both pregnant and non-pregnant women. Daily iron supplementation and deworming after the first trimester is recommended for pregnant women although these programs usually do not operate efficiently or effectively. Weekly iron-folic acid supplementation and regular deworming for non-pregnant women may be a viable approach for improving iron status and preventing anemia during the reproductive years. Addressing these diseases at a population level before women become pregnant could significantly improve women's health before and during pregnancy, as well as their infants' growth and development. Methods and Results This paper describes the major processes undertaken in a demonstration intervention of preventive weekly iron-folic acid supplementation with regular deworming for all 52,000 women aged 15–45 years in two districts of Yen Bai province, in northern Viet Nam. The intervention strategy included extensive consultation with community leaders and village, commune, district and provincial health staff, and training for village health workers. Distribution of the drugs was integrated with the existing health service infrastructure and the village health workers were the direct point of contact with women. Iron-folic acid tablets and deworming treatment were provided free of charge from May 2006. An independent Vietnamese NGO was commissioned to evaluate compliance and identify potential problems. The program resulted in effective distribution of iron-folic acid tablets and deworming treatment to all villages in the target districts, with full or partial compliance of 85%. Conclusion Training for health staff, the strong commitment of all partners and the use of appropriate educational materials led to broad support for weekly iron-folic acid supplementation and high participation in the regular deworming days. In March 2008 the program was expanded to all districts in the province, a target population of approximately 250,000 WRA, and management was handed over to provincial authorities.

The Bayesian conditional independence model for measurement error: applications in ecology

The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.

Efficient hybrid-game strategies coupled to evolutionary algorithms for robust multidisciplinary design optimization in aerospace engineering

A number of game strategies have been developed in past decades and used in the fields of economics, engineering, computer science, and biology due to their efficiency in solving design optimization problems. In addition, research in multiobjective and multidisciplinary design optimization has focused on developing a robust and efficient optimization method so it can produce a set of high quality solutions with less computational time. In this paper, two optimization techniques are considered; the first optimization method uses multifidelity hierarchical Pareto-optimality. The second optimization method uses the combination of game strategies Nash-equilibrium and Pareto-optimality. This paper shows how game strategies can be coupled to multiobjective evolutionary algorithms and robust design techniques to produce a set of high quality solutions. Numerical results obtained from both optimization methods are compared in terms of computational expense and model quality. The benefits of using Hybrid and non-Hybrid-Game strategies are demonstrated.

QoS-aware web service composition using genetic algorithms

Web service technology is increasingly being used to build various e-Applications, in domains such as e-Business and e-Science. Characteristic benefits of web service technology are its inter-operability, decoupling and just-in-time integration. Using web service technology, an e-Application can be implemented by web service composition — by composing existing individual web services in accordance with the business process of the application. This means the application is provided to customers in the form of a value-added composite web service. An important and challenging issue of web service composition, is how to meet Quality-of-Service (QoS) requirements. This includes customer focused elements such as response time, price, throughput and reliability as well as how to best provide QoS results for the composites. This in turn best fulfils customers’ expectations and achieves their satisfaction. Fulfilling these QoS requirements or addressing the QoS-aware web service composition problem is the focus of this project. From a computational point of view, QoS-aware web service composition can be transformed into diverse optimisation problems. These problems are characterised as complex, large-scale, highly constrained and multi-objective problems. We therefore use genetic algorithms (GAs) to address QoS-based service composition problems. More precisely, this study addresses three important subproblems of QoS-aware web service composition; QoS-based web service selection for a composite web service accommodating constraints on inter-service dependence and conflict, QoS-based resource allocation and scheduling for multiple composite services on hybrid clouds, and performance-driven composite service partitioning for decentralised execution. Based on operations research theory, we model the three problems as a constrained optimisation problem, a resource allocation and scheduling problem, and a graph partitioning problem, respectively. Then, we present novel GAs to address these problems. We also conduct experiments to evaluate the performance of the new GAs. Finally, verification experiments are performed to show the correctness of the GAs. The major outcomes from the first problem are three novel GAs: a penaltybased GA, a min-conflict hill-climbing repairing GA, and a hybrid GA. These GAs adopt different constraint handling strategies to handle constraints on interservice dependence and conflict. This is an important factor that has been largely ignored by existing algorithms that might lead to the generation of infeasible composite services. Experimental results demonstrate the effectiveness of our GAs for handling the QoS-based web service selection problem with constraints on inter-service dependence and conflict, as well as their better scalability than the existing integer programming-based method for large scale web service selection problems. The major outcomes from the second problem has resulted in two GAs; a random-key GA and a cooperative coevolutionary GA (CCGA). Experiments demonstrate the good scalability of the two algorithms. In particular, the CCGA scales well as the number of composite services involved in a problem increases, while no other algorithms demonstrate this ability. The findings from the third problem result in a novel GA for composite service partitioning for decentralised execution. Compared with existing heuristic algorithms, the new GA is more suitable for a large-scale composite web service program partitioning problems. In addition, the GA outperforms existing heuristic algorithms, generating a better deployment topology for a composite web service for decentralised execution. These effective and scalable GAs can be integrated into QoS-based management tools to facilitate the delivery of feasible, reliable and high quality composite web services.

The validation of the rating for subdivision neighbourhood sustainability design (RSNSD) In Bangkok Metropolitan Region (BMR), Thailand

In recent years, the problems resulting from unsustainable subdivision development have become significant problems in the Bangkok Metropolitan Region (BMR), Thailand. Numbers of government departments and agencies have tried to eliminate the problems by introducing the rating tools to encourage the higher sustainability levels of subdivision development in BMR, such as the Environmental Impact Assessment Monitoring Award (EIA-MA) and the Thai’s Rating for Energy and Environmental Sustainability of New construction and major renovation (TREES-NC). However, the EIA-MA has included the neighbourhood designs in the assessment criteria, but this requirement applies to large projects only. Meanwhile, TREES-NC has focused only on large scale buildings such as condominiums, office buildings, and is not specific for subdivision neighbourhood designs. Recently, the new rating tool named “Rating for Subdivision Neighbourhood Sustainability Design (RSNSD)” has been developed. Therefore, the validation process of RSNSD is still required. This paper aims to validate the new rating tool for subdivision neighbourhood design in BMR. The RSNSD has been validated by applying the rating tool to eight case study subdivisions. The result of RSNSD by data generated through surveying subdivisions will be compared to the existing results from the EIA-MA. The selected cases include of one “Excellent Award”, two “Very Good Award”, and five non-rated subdivision developments. This paper expects to prove the credibility of RSNSD before introducing to the real subdivision development practises. The RSNSD could be useful to encourage higher sustainability subdivision design level, and then protect the problems from further subdivision development in BMR.

The application of Bowen’s family system theory (BFST) to the Malay population in Malaysia

The rapid economic development and social changes in Malaysia recently have led to many psychosocial problems in young people, such as drug addiction, child sexual abuse and mental illness. The Malaysian government is beginning to focus more attention on its social welfare and human service needs in order to alleviate these psychosocial problems. Although counselling is accepted and widespread in Malaysia, the practice of family therapy is not as accepted as it is still a widely held belief that family problems need to be kept within the family. However, changes are imminent and thus the theoretical basis of family therapy needs to be culturally relevant. Bowen‟s Family Systems Theory (BFST) is already one of the major theories taught to tertiary counselling students in Malaysian universities. The main tenet of Bowen‟s theory is that the family as a system may be unstable unless each member of the family is well differentiated. High differentiation levels in the family allow a person to both leave the family‟s boundaries in search of uniqueness and to continually return to the family fold in order to establish a more mature sense of belonging. The difficulty, however, is that while Bowen has claimed that his theory is universal nearly all of the research confirming the theory has been conducted in the United States of America. The only known study outside America, however, did show that Bowen‟s theory applied to a Filipino population but, one of the theory‟s propositions that differentiation is intergenerational was not supported in this non-American sample. The American sample that was compared to the Malay sample was taken from Skowron and Friedlander‟s (1998) study. One hundred and twenty-seven faculty staff in an American university completed the Differentiation of Self Inventory (DSI) to measure level of differentiation of self. This thesis therefore, set out to determine whether Bowen‟s theory applied to another non-American sample, the Malaysian community. The research also investigated if the intergenerational effect was present in the Malaysian sample as well as explored the role of socio-economic status on Bowen‟s theory of differentiation and gender effect. Three hundred and seventy-four families completed four measures to examine these research questions: the Differentiation of Self Inventory (DSI), the Family Inventory of Life Event (FILE), the Depression Anxiety and Stress Scale (DASS) and the Connor-Davidson Resilience Scale (CD-RISC). The results of the study showed that differentiation of self is a valid construct for the Malay population. However, all four subscales of the Differentiation of Self Inventory (DSI); emotional reactivity (ER), emotional cut-off (EC), fusion with other (FO) and I position (IP), showed significant differences compared to the American sample from Skowron and Friedlander‟s (1998) study. The Malay sample scored higher in emotional reaction (ER), fusion with other (FO), but lower on emotional cut-off (EC) and I position (IP) than the American sample. The intergenerational effect was found in the Malay population as the parent‟s level of differentiation correlated with their children‟s level of differentiation. It was found that stress as measured by the Family Inventory of Life Event (FILE) and as measured by the Depression Anxiety and Stress Scale (DASS) were not correlated with the level of differentiation of self in parents. However, gender had a significant effect in predicting the level of differentiation among parents in Malay population with females scores higher on emotional reactivity (ER) and fusion with other (FO) than males. An additional finding was that resilience can be predicted from the level of differentiation of self in children in the Malay sample. There was also a positive correlation between the level of differentiation of self in parents and resilience in their children. Findings from this study indicate that the concept of differentiation of self is applicable to a Malay sample; however, the implementation of the theory should be applied with cultural sensitivity.

M-ary trees for combinatorial asset management decision problems

A novel m-ary tree based approach is presented to solve asset management decisions which are combinatorial in nature. The approach introduces a new dynamic constraint based control mechanism which is capable of excluding infeasible solutions from the solution space. The approach also provides a solution to the challenges with ordering of assets decisions.

A prospective study investigating the impact of school belonging factors on negative affect in adolescents

School belonging, measured as a unidimensional construct, is an important predictor of negative affective problems in adolescents, including depression and anxiety symptoms. A recent study found that one such measure, the Psychological Sense of School Membership (PSSM) scale, actually comprises three factors: Caring Relations, Acceptance, and Rejection. We explored the relations of these factors with negative affect in a sample of 504 Australian grade 7 and 8 students who completed the PSSM and Children’s Depression Inventory (CDI) at three time points. Each school belonging factor contributed to the prediction of negative affect in cross-sectional analyses. Scores on the Acceptance factor predicted subsequent negative affect for boys and girls, even controlling for prior negative affect. For girls, the Rejection factor was also significant in the prospective analysis. These findings have implications for the design of interventions and are further confirmation that school belonging should be considered a multidimensional construct.

Exploring the contested role of mandatory reporting laws in the identification of severe child abuse and neglect

Significant numbers of children are severely abused and neglected by parents and caregivers. Infants and very young children are the most vulnerable and are unable to seek help. To identify these situations and enable child protection and the provision of appropriate assistance, many jurisdictions have enacted ‘mandatory reporting laws’ requiring designated professionals such as doctors, nurses, police and teachers to report suspected cases of severe child abuse and neglect. Other jurisdictions have not adopted this legislative approach, at least partly motivated by a concern that the laws produce dramatic increases in unwarranted reports, which, it is argued, lead to investigations which infringe on people’s privacy, cause trauma to innocent parents and families, and divert scarce government resources from deserving cases. The primary purpose of this paper is to explore the extent to which opposition to mandatory reporting laws is valid based on the claim that the laws produce ‘overreporting’. The first part of this paper revisits the original mandatory reporting laws, discusses their development into various current forms, explains their relationship with policy and common law reporting obligations, and situates them in the context of their place in modern child protection systems. This part of the paper shows that in general, contemporary reporting laws have expanded far beyond their original conceptualisation, but that there is also now a deeper understanding of the nature, incidence, timing and effects of different types of severe maltreatment, an awareness that the real incidence of maltreatment is far higher than that officially recorded, and that there is strong evidence showing the majority of identified cases of severe maltreatment are the result of reports by mandated reporters. The second part of this paper discusses the apparent effect of mandatory reporting laws on ‘overreporting’ by referring to Australian government data about reporting patterns and outcomes, with a particular focus on New South Wales. It will be seen that raw descriptive data about report numbers and outcomes appear to show that reporting laws produce both desirable consequences (identification of severe cases) and problematic consequences (increased numbers of unsubstantiated reports). Yet, to explore the extent to which the data supports the overreporting claim, and because numbers of unsubstantiated reports alone cannot demonstrate overreporting, this part of the paper asks further questions of the data. Who makes reports, about which maltreatment types, and what are the outcomes of those reports? What is the nature of these reports; for example, to what extent are multiple numbers of reports made about the same child? What meaning can be attached to an ‘unsubstantiated’ report, and can such reports be used to show flaws in reporting effectiveness and problems in reporting laws? It will be suggested that available evidence from Australia is not sufficiently detailed or strong to demonstrate the overreporting claim. However, it is also apparent that, whether adopting an approach based on public health and or other principles, much better evidence about reporting needs to be collected and analyzed. As well, more nuanced research needs to be conducted to identify what can reasonably be said to constitute ‘overreports’, and efforts must be made to minimize unsatisfactory reporting practice, informed by the relevant jurisdiction’s context and aims. It is also concluded that, depending on the jurisdiction, the available data may provide useful indicators of positive, negative and unanticipated effects of specific components of the laws, and of the strengths, weaknesses and needs of the child protection system.