A comparative study of early childhood curriculum documents focused on education for sustainability in South Korea and Australia

This thesis is about a comparative study of early childhood education (ECE) curriculum documents focused on education for sustainability (EfS) in South Korea and Australia. It examined how the national ECE curriculum documents in two culturally different contexts align with contemporary concepts of sustainability and activist early childhood education for sustainability (ECEfS) principles. Drawing on systems theory, Korean and Australian ECE curriculum documents were used as the primary sources for this study within the framework of critical document analysis (CDA). This study offers a step forward in developing culturally inclusive/holistic understandings of sustainability and more contextualised/localised approaches to ECEfS.

Mining topical relevant patterns for multi-document summarization

Multi-document summarization addressing the problem of information overload has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of multi-document summarization systems. In this paper, we proposed a novel pattern-based topic model (PBTMSum) for the task of the multi-document summarization. PBTMSum combining pattern mining techniques with LDA topic modelling could generate discriminative and semantic rich representations for topics and documents so that the most representative and non-redundant sentences can be selected to form a succinct and informative summary. Extensive experiments are conducted on the data of document understanding conference (DUC) 2007. The results prove the effectiveness and efficiency of our proposed approach.

Effective 20 newsgroups dataset cleaning

The rapid increase in the number of text documents available on the Internet has created pressure to use effective cleaning techniques. Cleaning techniques are needed for converting these documents to structured documents. Text cleaning techniques are one of the key mechanisms in typical text mining application frameworks. In this paper, we explore the role of text cleaning in the 20 newsgroups dataset, and report on experimental results.

Invertebrate predation and trophic cascades in a pelagic food web : The multiple roles of Chaoborus flavicans (Meigen) in a clay-turbid lake

In lake ecosystems, both fish and invertebrate predators have dramatic effects on their prey communities. Fish predation selects large cladocerans while invertebrate predators prefer prey of smaller size. Since invertebrate predators are the preferred food items for fish, their occurrence at high densities is often connected with the absence or low number of fish. It is generally believed that invertebrate predators can play a significant role only if the density of planktivorous fish is low. However, in eutrophic clay-turbid Lake Hiidenvesi (southern Finland), a dense population of predatory Chaoborus flavicans larvae coexists with an abundant fish population. The population covers the stratifying area of the lake and attains a maximum population density of 23000 ind. m-2. This thesis aims to clarify the effects of Chaoborus flavicans on the zooplankton community and the environmental factors facilitating the coexistence of fish and invertebrate predators. In the stratifying area of Lake Hiidenvesi, the seasonal succession of cladocerans was exceptional. The spring biomass peak of cladocerans was missing and the highest biomass occurred in midsummer. In early summer, the consumption rate by chaoborids clearly exceeded the production rate of cladocerans and each year the biomass peak of cladocerans coincided with the minimum chaoborid density. In contrast, consumption by fish was very low and each study year cladocerans attained maximum biomass simultaneously with the highest consumption by smelt (Osmerus eperlanus). The results indicated that Chaoborus flavicans was the main predator of cladocerans in the stratifying area of Lake Hiidenvesi. The clay turbidity strongly contributed to the coexistence of chaoborids and smelt at high densities. Turbidity exceeding 30 NTU combined with light intensity below 0.1 μE m-2 s-1provides an efficient daytime refuge for chaoborids, but turbidity alone is not an adequate refuge unless combined with low light intensity. In the non-stratifying shallow basins of Lake Hiidenvesi, light intensity exceeds this level during summer days at the bottom of the lake, preventing Chaoborus forming a dense population in the shallow parts of the lake. Chaoborus can be successful particularly in deep, clay-turbid lakes where they can remain high in the water column close to their epilimnetic prey. Suspended clay alters the trophic interactions by weakening the link between fish and Chaoborus, which in turn strengthens the effect of Chaoborus predation on crustacean zooplankton. Since food web management largely relies on manipulations of fish stocks and the cascading effects of such actions, the validity of the method in deep clay-turbid lakes may be questioned.

SSMBS: a web server to locate sequentially separated motifs in biological sequences

The identification of sequence (amino acids or nucleotides) motifs in a particular order in biological sequences has proved to be of interest. This paper describes a computing server, SSMBS, which can locate anddisplay the occurrences of user-defined biologically important sequence motifs (a maximum of five) present in a specific order in protein and nucleotide sequences. While the server can efficiently locate motifs specified using regular expressions, it can also find occurrences of long and complex motifs. The computation is carried out by an algorithm developed using the concepts of quantifiers in regular expressions. The web server is available to users around the clock at http://dicsoft1.physics.iisc.ernet.in/ssmbs/.

A blind indic script recognizer for multi-script documents

We report a hierarchical blind script identifier for 11 different Indian scripts. An initial grouping of the 11 scripts is accomplished at the first level of this hierarchy. At the subsequent level, we recognize the script in each group. The various nodes of this tree use different feature-classifier combinations. A database of 20,000 words of different font styles and sizes is collected and used for each script. Effectiveness of Gabor and Discrete Cosine Transform features has been independently, evaluated using nearest neighbor linear discriminant and support vector machine classifiers. The minimum and maximum accuracies obtained, using this hierarchical mechanism, are 92.2% and 97.6%, respectively.

A combinatorial procurement auction for QoS-aware web services

Business processes and application functionality are becoming available as internal web services inside enterprise boundaries as well as becoming available as commercial web services from enterprise solution vendors and web services marketplaces. Typically there are multiple web service providers offering services capable of fulfilling a particular functionality, although with different Quality of Service (QoS). Dynamic creation of business processes requires composing an appropriate set of web services that best suit the current need. This paper presents a novel combinatorial auction approach to QoS aware dynamic web services composition. Such an approach would enable not only stand-alone web services but also composite web services to be a part of a business process. The combinatorial auction leads to an integer programming formulation for the web services composition problem. An important feature of the model is the incorporation of service level agreements. We describe a software tool QWESC for QoS-aware web services composition based on the proposed approach.

Localization of handwritten text in documents using moment invariants and Delaunay triangulation

This paper describes an approach based on Zernike moments and Delaunay triangulation for localization of hand-written text in machine printed text documents. The Zernike moments of the image are first evaluated and we classify the text as hand-written using the nearest neighbor classifier. These features are independent of size, slant, orientation, translation and other variations in handwritten text. We then use Delaunay triangulation to reclassify the misclassified text regions. When imposing Delaunay triangulation on the centroid points of the connected components, we extract features based on the triangles and reclassify the text. We remove the noise components in the document as part of the preprocessing step so this method works well on noisy documents. The success rate of the method is found to be 86%. Also for specific hand-written elements such as signatures or similar text the accuracy is found to be even higher at 93%.

Frechet distance based approach for searching online handwritten documents

We propose a novel, language-neutral approach for searching online handwritten text using Frechet distance. Online handwritten data, which is available as a time series (x,y,t), is treated as representing a parameterized curve in two-dimensions and the problem of searching online handwritten text is posed as a problem of matching two curves in a two-dimensional Euclidean space. Frechet distance is a natural measure for matching curves. The main contribution of this paper is the formulation of a variant of Frechet distance that can be used for retrieving words even when only a prefix of the word is given as query. Extensive experiments on UNIPEN dataset(1) consisting of over 16,000 words written by 7 users show that our method outperforms the state-of-the-art DTW method. Experiments were also conducted on a Multilingual dataset, generated on a PDA, with encouraging results. Our approach can be used to implement useful, exciting features like auto-completion of handwriting in PDAs.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

"Standards are living documents ..."

"Standards are living documents which reflect progress in science, technology and systems" - Standards Australia.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Techno Teacher Moms: Web 2.0 Connecting Mothers in the Home Education Community

Home education is on the rise in Australia. However, unlike parents who choose mainstream schooling, these parents often lack the support of a wider community to help them on their educational and parenting journey. This support is especially lacking as many people in the wider community find the choice to home education confronting. As such, these parents may feel isolated and alienated in the general population as their choice to home educate is questioned at best, and ridiculed at worst. These parents often find sanctuary online in homeschool groups on Facebook. This chapter explores the ways that Facebook Groups are used by marginalized and disenfranchised families who home educate to meet with others who are likeminded and aligned with their beliefs and philosophies. It is through these groups that parents, in relation to schooling it is especially mothers, are able to ask for advice, to vent, to explore options and find connections that may be lacking in the wider community.

Selective dissemination of XML documents using GAs and SVM

XML has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Self Adaptive Migration Model Genetic Algorithm (SAMCA)[5] and multi class Support Vector Machine (SVM) are used to learn a user model. Based on the feedback from the users the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.