Improved shear design rules for lipped channel beams with web openings

Cold-formed steel Lipped Channel Beams (LCB) with web openings are commonly used as floor joists and bearers in building structures. The shear behaviour of these beams is more complicated and their shear capacities are considerably reduced by the presence of web openings. However, limited research has been undertaken on the shear behaviour and strength of LCBs with web openings. Hence a detailed numerical study was undertaken to investigate the shear behaviour and strength of LCBs with web openings. Finite element models of simply supported LCBs under a mid-span load with aspect ratios of 1.0 and 1.5 were developed and validated by comparing their results with test results. They were then used in a detailed parametric study to investigate the effects of various influential parameters. Experimental and numerical results showed that the current design rules in cold-formed steel structures design codes are very conservative. Improved design equations were therefore proposed for the shear strength of LCBs with web openings based on both experimental and numerical results. This paper presents the details of finite element modelling of LCBs with web openings, validation of finite element models, and the development of improved shear design rules. The proposed shear design rules in this paper can be considered for inclusion in the future versions of cold-formed steel design codes.

Nonparametric semantic segmentation for 3D street scenes

In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.

Employees' adoption of enterprise web 2.0 : the role of technological attributes

Although the adoption of Enterprise Web 2.0 within organisations is beneficial, it could meet with employees’ resistance and the adoption process can be lengthy. The successful implementation of Enterprise Web 2.0 is based on employee involvement and adoption of such social technology. This paper is part of a larger research project that explored the adoption of Web 2.0 by individuals within enterprises. Using a qualitative study, the findings show that there are number of adoption influences including technological, individual and contextual issues. This paper presents Web 2.0 technological attributes that influence its adoption. The found attributes are: friendliness, reliability, mobility, technical compatibility, discoverability, transparency and Web 2.0 type.

Mapping semantic knowledge for unsupervised text categorisation

Text categorisation is challenging, due to the complex structure with heterogeneous, changing topics in documents. The performance of text categorisation relies on the quality of samples, effectiveness of document features, and the topic coverage of categories, depending on the employing strategies; supervised or unsupervised; single labelled or multi-labelled. Attempting to deal with these reliability issues in text categorisation, we propose an unsupervised multi-labelled text categorisation approach that maps the local knowledge in documents to global knowledge in a world ontology to optimise categorisation result. The conceptual framework of the approach consists of three modules; pattern mining for feature extraction; feature-subject mapping for categorisation; concept generalisation for optimised categorisation. The approach has been promisingly evaluated by compared with typical text categorisation methods, based on the ground truth encoded by human experts.

E-learning 2.0 experiences within higher education : theorising students' and teachers' experiences in Web 2.0 learning

This study explores and evaluates students’ and teachers’ experiences when using a range of Web 2.0 tools in Higher Education teaching and learning. It contributes to our understanding of how Web 2.0 learning communities are constructed, experienced and the nature of the participation therein. This research extends our knowledge and understanding of the Web 2.0 phenomena, and provides a framework that can assist with improving future Web 2.0 implementation.

Retrieval of health advice on the web: AEHRC at ShARe/CLEF eHealth Evaluation Lab Task 3

This paper details the participation of the Australian e- Health Research Centre (AEHRC) in the ShARe/CLEF 2013 eHealth Evaluation Lab { Task 3. This task aims to evaluate the use of information retrieval (IR) systems to aid consumers (e.g. patients and their relatives) in seeking health advice on the Web. Our submissions to the ShARe/CLEF challenge are based on language models generated from the web corpus provided by the organisers. Our baseline system is a standard Dirichlet smoothed language model. We enhance the baseline by identifying and correcting spelling mistakes in queries, as well as expanding acronyms using AEHRC's Medtex medical text analysis platform. We then consider the readability and the authoritativeness of web pages to further enhance the quality of the document ranking. Measures of readability are integrated in the language models used for retrieval via prior probabilities. Prior probabilities are also used to encode authoritativeness information derived from a list of top-100 consumer health websites. Empirical results show that correcting spelling mistakes and expanding acronyms found in queries signi cantly improves the e ectiveness of the language model baseline. Readability priors seem to increase retrieval e ectiveness for graded relevance at early ranks (nDCG@5, but not precision), but no improvements are found at later ranks and when considering binary relevance. The authoritativeness prior does not appear to provide retrieval gains over the baseline: this is likely to be because of the small overlap between websites in the corpus and those in the top-100 consumer-health websites we acquired.

The adoption of Web 2.0 within enterprises : employees' perspectives

This project explores employees’ adoption of Web 2.0 within organisations. It shows that the adoption of Web 2.0 is a challenging and dynamic process that changes over time. The adoption is, also, influenced by a number of interrelated issues including: People Traits, Social Influence, Trust, Technological Attributes, Relevance of Web 2.0, Web 2.0 Maturity, Organisational Support, and Organisational Practice.

The heterogeneous cluster ensemble method using hubness for clustering text documents

We propose a cluster ensemble method to map the corpus documents into the semantic space embedded in Wikipedia and group them using multiple types of feature space. A heterogeneous cluster ensemble is constructed with multiple types of relations i.e. document-term, document-concept and document-category. A final clustering solution is obtained by exploiting associations between document pairs and hubness of the documents. Empirical analysis with various real data sets reveals that the proposed meth-od outperforms state-of-the-art text clustering approaches.

A concept-based retrieval method for entity-oriented search

Entity-oriented retrieval aims to return a list of relevant entities rather than documents to provide exact answers for user queries. The nature of entity-oriented retrieval requires identifying the semantic intent of user queries, i.e., understanding the semantic role of query terms and determining the semantic categories which indicate the class of target entities. Existing methods are not able to exploit the semantic intent by capturing the semantic relationship between terms in a query and in a document that contains entity related information. To improve the understanding of the semantic intent of user queries, we propose concept-based retrieval method that not only automatically identifies the semantic intent of user queries, i.e., Intent Type and Intent Modifier but introduces concepts represented by Wikipedia articles to user queries. We evaluate our proposed method on entity profile documents annotated by concepts from Wikipedia category and list structure. Empirical analysis reveals that the proposed method outperforms several state-of-the-art approaches.

Surrogacy : competing interests or a tangled web?

Overseas commercial surrogacy is a legally challenging but commonly utilised form of assisted reproductive technology. Not only does it raise complex and competing policy issues, but it tests the relevant Family Law legislation which underpins parenting orders. Decisions handed down by the judiciary are inconsistent. Legislation is inadequate. But still the surge in surrogacy continues as surrogacy destinations such as India and Thailand continue to increase in popularity. Part one of this article addresses the competing interests of the illegality of overseas commercial surrogacy arrangements with the welfare of the child born as a result of such arrangements, and the inconsistent approaches taken by the judiciary. Part two concerns the interpretation of Family Law legislation by the courts in an attempt to provide intended couples and their children with certainty and finality, again resulting in inconsistent judicial decisions. Overseas commercial surrogacy is legally problematic, and intended parents need to be aware of its limitations.

Personalized ontology learning for enhancing text mining effectiveness

Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves significant improvements in most information filtering measurements. This research contributes to the fields of ontological filtering, user profiling, and knowledge representation. The related outputs are critical when systems are expected to return proper mining results and provide personalized services. The scientific findings have the potential to facilitate the design of advanced preference mining models, where impact on people's daily lives.

Exploiting Web 2.0 technologies to forge live connectedness in class

The economics of supporting learning has seen institutional encouragement of a wide range of blended learning initiatives in face to face and online teaching and learning. This has become one of the key drivers for the adoption of technology in teaching, in a manner occassionally guilty of putting the cart before the horse. Learning spaces are increasingly equipped with a dizzying array of technological options testifying to institutional and governmental investment and commitment in supporting face to face blended learning (QUT, 2011, C/4.2). Yet innovation within traditional learning and teaching models faces a number of challenges both at an institutional level and at the teaching coal face. Web 2.0 technologies present a vast array of opportunities to harness and capture the attention of students in engaging learning opportunitites. This presentation will explore technologies supportive of active learning pedagogies.

Not losing the plot : creating, collecting and curating qualitative data through a web-based application

Collecting regular personal reflections from first year teachers in rural and remote schools is challenging as they are busily absorbed in their practice, and separated from each other and the researchers by thousands of kilometres. In response, an innovative web-based solution was designed to both collect data and be a responsive support system for early career teachers as they came to terms with their new professional identities within rural and remote school settings. Using an emailed link to a web-based application named goingok.com, the participants are charting their first year plotlines using a sliding scale from ‘distressed’, ‘ok’ to ‘soaring’ and describing their self-assessment in short descriptive posts. These reflections are visible to the participants as a developing online journal, while the collections of de-identified developing plotlines are visible to the research team, alongside numerical data. This paper explores important aspects of the design process, together with the challenges and opportunities encountered in its implementation. A number of the key considerations for choosing to develop a web application for data collection are initially identified, and the resultant application features and scope are then examined. Examples are then provided about how a responsive software development approach can be part of a supportive feedback loop for participants while being an effective data collection process. Opportunities for further development are also suggested with projected implications for future research.