Teaching Chinese history : the issue of approach

In China, the study of history has never been a detached academic pursuit, as it has always been indistinguishable from political directions. Chinese political leaders have been unable to adopt a disinterested approach towards history, be it distant or contemporary. On the other hand, Western historians interpret Chinese history from their own point of view, and they often view Chinese history as an extension of Western history. Often, they have been preoccupied with the concern to explain or justify their own record or involvement rather than to produce an objective account, especially in regard to the late nineteenth and early twentieth centuries’ history of China. This study questions the conventional approach to China’s past, be that of a Confucian, a Communist or a Western ethnocentric historiographer. It explores the possibility of establishing a “Chinese experience-based” approach while maintaining “impartiality and neutrality”, looking to historical studies outside of China to achieve this.

Matching objects across the textured-smooth continuum

The problem of 3D object recognition is of immense practical importance, with the last decade witnessing a number of breakthroughs in the state of the art. Most of the previous work has focused on the matching of textured objects using local appearance descriptors extracted around salient image points. The recently proposed bag of boundaries method was the first to address directly the problem of matching smooth objects using boundary features. However, no previous work has attempted to achieve a holistic treatment of the problem by jointly using textural and shape features which is what we describe herein. Due to the complementarity of the two modalities, we fuse the corresponding matching scores and learn their relative weighting in a data specific manner by optimizing discriminative performance on synthetically distorted data. For the textural description of an object we adopt a representation in the form of a histogram of SIFT based visual words. Similarly the apparent shape of an object is represented by a histogram of discretized features capturing local shape. On a large public database of a diverse set of objects, the proposed method is shown to outperform significantly both purely textural and purely shape based approaches for matching across viewpoint variation.

The use of video recording in professional development of teachers and teacher educators in Indonesia

Design-based research has gained currency in educational research over the past decade due to its strength to bridge the divide between theoretical research and educational practice in naturalistic settings. Design based approaches involve a process of designing mathematical tasks, observing the enacted design in classrooms and reflecting on the process from analysing the classroom artifacts. Video plays a central role in supporting teachers and teacher educators to study and reflect on students’ mathematical thinking and in capturing the dynamic of classroom teaching and learning process.

This chapter will examine and analyse practitioner’s lenses in capturing the dynamic and complexity of classroom mathematical learning using video segments, and classroom artifacts including digital photos of classroom moments and students’ work. Practitioners’ lenses are taken as a window to capture key teaching and learning moments from the lessons. Analysis of this selection of these video segments along with other classroom artifacts based on practitioners’ lenses provide insights into practitioners’ views on key teaching and learning moments in mathematics lessons.

A proof of concept for using brief Dialectical Behavior Therapy as a treatment for problem gambling

Fourteen ‘treatment resistant’ problem gamblers received 9 weeks of Dialectical Behavior Therapy (DBT) at specialist problem gambling services delivered in Melbourne, Australia. This study is the first to investigate the effectiveness of a brief DBT treatment for problem gambling, with a focus on measuring change in the four DBT process skills (mindfulness, distress tolerance, emotion dysregulation, and negative relationships). Although there were no statistically significant improvements in measures of gambling behaviour, 83% of participants were abstinent or reduced their gambling expenditure pre- to post-treatment. Participants also reported statistically and clinically significant improvements in psychological distress,  mindfulness, and distress tolerance. Moreover, there were no increases in alcohol or substance use. These results are discussed in the context of focusing on a single DBT process skill, and the benefits of using group-based approaches.

Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in practice. While inherently insensitive to visible spectrum illumination changes, IR data introduces specific challenges of its own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, when comparing two thermal IR images of faces, we mutually normalize their poses and facial expressions by using an active appearance model (AAM) to generate synthetic images of the two faces with a neutral facial expression and in the same view (the average of the two input views). This is achieved by piecewise affine warping which follows AAM fitting. A major contribution of our work is the use of an AAM ensemble in which each AAM is specialized to a particular range of poses and a particular region of the thermal IR face space. Combined with the contributions from our previous work which addressed the problem of reliable AAM fitting in the thermal IR spectrum, and the development of a person-specific representation robust to transient changes in the pattern of facial temperature emissions, the proposed ensemble framework accurately matches faces across the full range of yaw from frontal to profile, even in the presence of scale variation (e.g. due to the varying distance of a subject from the camera). The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces and a newly acquired data set of thermal IR motion videos. Our approach achieved perfect recognition performance on both data sets, significantly outperforming the current state of the art methods even when they are trained with multiple images spanning a range of head views.

Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, to normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first publication which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances detail in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the exact pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieved 100% identification rate, significantly outperforming previously described methods

Pedagogy of oppressed community engagement : socially inclusive visioning of urban change

It is generally accepted that good practice in policy making and urban change initiatives requires community engagement, where community-based approaches are emphasised as a means of socially inclusive visioning. Communities expect greater transparency, accountability and engagement. This expectation is not always met, with many studies focusing on the perceived tickbox effect - where engagement is a process that has to be undertaken rather than being welcomed and embraced as an integral part of planning for urban change. This paper explores multi-disciplinary concepts and looks at ways these can be linked to community engagement in planning, particularly in larger urban Councils. In this brief glimpse at the wide variety of disciplines that could be drawn on, the paper uses information systems, teaching models, organisational theory and public policy to highlight the potential for altering concepts of community engagement. It concludes that, from these particular examples: the use of double-loop learning could help to empower the community (from organisational theory), collaboration and participation necessitate the co-ordination and exchange of information and knowledge within and between organisations (information systems), the preconception that the authority holds all the knowledge ready to be handed out to the community (teaching models) needs to be challenged, and partnerships are important in empowering people (public policy).

Perceptions of Place - evaluating experiential qualities of streetscapes

Working with a small set of 60 images from a collection of over 1000 digital photographs, the following paper presents ongoing research into the experiential qualities of street scapes. This paper examines the initial results of a survey of 75 voluntary based perceptive studies and focusses on a sample cohort of 30 participants (20 cognate and 10 non-cognate respondents) who were asked to evaluate a streetscape experience based on favourable and unfavourable perceptive qualities. Using a spatial sequencing process, akin to the 'Serial Vision' methodology of Gordon Cullen, respondents were asked to rate a section of urban fabric based on favourable (hot or warm) and unfavourable (cold or cool) traits and to digitally capture these images. The study yields results which highlight the importance of the quality and effect of green attributes within the overall street scape experience. The implications for urban design practice (streetscape greening), are briefly explored.

Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

We collaborate with environmental scientists to study the hydrodynamics and water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the complex urban landform created by surrounding buildings. In this work, we study an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir with a limited number of wind sensors. Unlike existing sensor placement solutions that assume Gaussian process of target phenomena, this study measures the wind which inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons which follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind in the presence of surrounding buildings. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir surface in real time. 10 wind sensors are finally deployed around or on the water surface of an urban reservoir. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction approach provides accurate wind measurement which outperforms the state-of-the-art Gaussian model based or interpolation based approaches.

Science teacher educator partnerships with schools (STEPS): developing an interpretive framework for primary science teacher education

The STEPS Project responds to international concern about primary teachers’ lack of science knowledge and confidence to teach science, and recent questioning of the effectiveness of traditional approaches to teacher education. The project reviews and builds on established, innovative and successful practices at five universities, to develop and promote a framework supporting school‐based approaches to pre‐service teacher education. This paper will outline the processes involved in developing an Interpretive Framework, which will be a key outcome of the project. The Interpretive Framework identifies key elements to assist teacher educators in planning, implementing and sustaining school-based approaches to teacher education.

Early years education in the primary years programme: implementation strategies and programme outcomes

This mixed-methods study investigated implementation strategies and child learning outcomes in early years education in the Primary Years Programme. Researchers from Deakin University evaluated processes and outcomes in four early years programmes, two in Singapore and two in Melbourne, Australia. Researchers collected qualitative data through classroom observations, drawings and writing produced by children, and interviews with educators, coordinators and parents. Quantitative data was collected through assessments of children’s literacy, developmental school readiness and learning skills. Three of the early years programmes appeared to support the development of learner profile attributes through inquiry-led learning and play-based approaches, while one site in Singapore was still in a developmental stage of implementing the programme. The study suggested that literacy skills at all sites were fairly developed; that children were performing at levels commensurate with or better in terms of school readiness; and that children were developing learning skills at higher rates than a comparative sample.

The Dutch Obesity Intervention in Teenagers (DOiT) cluster controlled implementation trial: intervention effects and mediators and moderators of adiposity and energy balance-related behaviours

 Background: The Dutch Obesity Intervention in Teenagers (DOiT) programme is an evidence-based obesity prevention programme tailored to adolescents attending the first two years of prevocational education in the Netherlands. The initial programme showed promising results during an effectiveness trial. The programme was adapted and prepared for nationwide dissemination. To gain more insight into the process of translating evidence-based approaches into ‘real world’ (i.e., ‘natural’) conditions, our research aims were to evaluate the impact of the DOiT-implementation programme on adolescents’ adiposity and energy balance-related behaviours during natural dissemination and to explore the mediating and moderating factors underlying the DOiT intervention effects.
Methods: We conducted a cluster-controlled implementation trial with 20 voluntary intervention schools (n=1002 adolescents) and 9 comparable control schools (n = 484 adolescents). We measured adolescents’ body height and weight, skinfold thicknesses, and waist circumference. We assessed adolescents’ dietary and physical activity behaviours by means of self-report. Data were collected at baseline and at 20-months follow-up. We used multivariable multilevel linear or logistic regression analyses to evaluate the intervention effects and to test the hypothesised behavioural mediating factors. We checked for potential effect modification by gender, ethnicity and education level.
Results: We found no significant intervention effects on any of the adiposity measures or behavioural outcomes. Furthermore, we found no mediating effects by any of the hypothesised behavioural mediators. Stratified analyses for gender showed that the intervention was effective in reducing sugar-containing beverage consumption in girls (B = -188.2 ml/day; 95% CI = -344.0; -32.3). In boys, we found a significant positive intervention effect on breakfast frequency (B = 0.29 days/week; 95% CI = 0.01; 0.58). Stratified analyses for education level showed an adverse intervention effect (B = 0.09; 95% CI = 0.02; 0.16) on BMI z-scores for adolescents attending the vocational education track.
Conclusions: Although not successful in changing adolescents’ adiposity, the DOiT-implementation programme had some beneficial effects on specific obesity-related behaviours in subgroups. This study underlines the difficulty of translating intervention effectiveness in controlled settings to real world contexts. Adaptations to the implementation strategy are needed in order to promote implementation as intended by the teachers.

Achieving socially inclusive practice: analysing power dynamics in a participatory approach to service development

The paper is concerned with what works and why in relation to socially inclusive practice in diverse community services settings. Drawing on the findings from a research study involving a participatory evaluation and service development project in a large community services organisation in Melbourne, Australia, the paper asserts that organisations can become more inclusive by paying attention to the ways in which they perpetuate existing oppression and by intentionally challenging associated limiting power/knowledge formations. The study was situated in rights-based approaches to health and social development and drew on Foucauldian conceptualisations of power that see the point of operation of power as the point of resistance, and therefore containing the possibility for change. The researchers worked in three diverse service contexts: children and families experiencing homelessness, people with disabilities, and older people with a history of homelessness and exclusion. The ongoing analysis of the power relations between and within the different stakeholder groups was important in ensuring that the development of the process did not reinscribe oppression and marginalisation. Within the growing body of work that theorises participatory and inclusive practices, the application of a Foucauldian framework offers new insights for anti-oppressive practice in the context of service development.

Detection on application layer DDoS using random walk model

Application Layer Distributed Denial of Service (ALDDoS) attacks have been increasing rapidly with the growth of Botnets and Ubiquitous computing. Differentiate to the former DDoS attacks, ALDDoS attacks cannot be efficiently detected, as attackers always adopt legitimate requests with real IP address, and the traffic has high similarity to legitimate traffic. In spite of that, we think, the attackers' browsing behavior will have great disparity from that of the legitimate users'. In this paper, we put forward a novel user behavior-based method to detect the application layer asymmetric DDoS attack. We introduce an extended random walk model to describe user browsing behavior and establish the legitimate pattern of browsing sequences. For each incoming browser, we observe his page request sequence and predict subsequent page request sequence based on random walk model. The similarity between the predicted and the observed page request sequence is used as a criterion to measure the legality of the user, and then attacker would be detected based on it. Evaluation results based on real collected data set has demonstrated that our method is very effective in detecting asymmetric ALDDoS attacks. © 2014 IEEE.

Internet traffic classification using constrained clustering

Statistics-based Internet traffic classification using machine learning techniques has attracted extensive research interest lately, because of the increasing ineffectiveness of traditional port-based and payload-based approaches. In particular, unsupervised learning, that is, traffic clustering, is very important in real-life applications, where labeled training data are difficult to obtain and new patterns keep emerging. Although previous studies have applied some classic clustering algorithms such as K-Means and EM for the task, the quality of resultant traffic clusters was far from satisfactory. In order to improve the accuracy of traffic clustering, we propose a constrained clustering scheme that makes decisions with consideration of some background information in addition to the observed traffic statistics. Specifically, we make use of equivalence set constraints indicating that particular sets of flows are using the same application layer protocols, which can be efficiently inferred from packet headers according to the background knowledge of TCP/IP networking. We model the observed data and constraints using Gaussian mixture density and adapt an approximate algorithm for the maximum likelihood estimation of model parameters. Moreover, we study the effects of unsupervised feature discretization on traffic clustering by using a fundamental binning method. A number of real-world Internet traffic traces have been used in our evaluation, and the results show that the proposed approach not only improves the quality of traffic clusters in terms of overall accuracy and per-class metrics, but also speeds up the convergence.