A fuzzy neural network approach for contractor pre-qualification

Nonlinearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which cause the process more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractor’s ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The FNN is a practical approach for modelling contractor prequalification.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.

Tag based collaborative filtering for recommender systems

Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user’s important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches.

Collaborative Filtering Recommender Systems based on Popular Tags

The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences for making personalized recommendations. However, the uncontrolled vocabulary causes a lot of problems to profile users accurately, such as ambiguity, synonyms, misspelling, low information sharing etc. To solve these problems, this paper proposes to use popular tags to represent the actual topics of tags, the content of items, and also the topic interests of users. A novel user profiling approach is proposed in this paper that first identifies popular tags, then represents users’ original tags using the popular tags, finally generates users’ topic interests based on the popular tags. A collaborative filtering based recommender system has been developed that builds the user profile using the proposed approach. The user profile generated using the proposed approach can represent user interests more accurately and the information sharing among users in the profile is also increased. Consequently the neighborhood of a user, which plays a crucial role in collaborative filtering based recommenders, can be much more accurately determined. The experimental results based on real world data obtained from Amazon.com show that the proposed approach outperforms other approaches.

Collaborative filtering recommender systems using tag information

Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviours such as purchase behaviour, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.

Will they know enough? Pre-service primary teachers' knowledge base for teaching intergrated social sciences

A significant issue in primary teacher education is developing a knowledge base which prepares teachers to teach in a range of subject areas. In Australia, the problem in primary social science education is compounded by the integrated nature of the key learning area of Studies of Society and Environment (SOSE). Recent debates on teaching integrated social sciences omit discussions on the knowledge base for teaching. In this paper, a case study approach is used to investigate primary pre-service teachers’ approaches to developing a knowledge base in designing a SOSE curriculum unit. Data from five teacher-educators who taught primary SOSE curriculum indicates that novice teachers’ subject content knowledge, as revealed through their curriculum planning, lacked a disciplinary basis. However, understanding of inquiry learning, which is fundamental to social science education, was much stronger. This paper identifies a gap in the scholarship on teaching integrated social science and illustrates the need to support and develop primary teachers’ disciplinary knowledge in teacher education.

The effect of unlocking RGD-motifs in collagen I on pre-osteoblast adhesion and differentiation

Denaturation of extracellular matrix proteins exposes cryptic binding sites. It is hypothesized that binding of cell adhesion receptors to these cryptic binding sites regulates cellular behaviour during tissue repair and regeneration. To test this hypothesis, we quantify the adhesion of pre-osteoblastic cells to native (Col) and partially-denatured (pdCol) collagen I using single-cell force spectroscopy. During early stages of cell attachment (≤180 s) pre-osteoblasts (MC3T3-E1) adhered significantly stronger to pdCol compared to Col. RGD (Arg-Gly-Asp)-containing peptides suppressed this elevated cell adhesion. We show that the RGD-binding α5β1- and αv-integrins mediated pre-osteoblast adhesion to pdCol, but not to Col. On pdCol pre-osteoblasts had a higher focal adhesion kinase tyrosine-phosphorylation level that correlated with enhanced spreading and motility. Moreover, pre-osteoblasts cultured on pdCol showed a pronounced matrix mineralization activity. Our data suggest that partially-denatured collagen exposes RGD-motifs that trigger binding of α5β1- and αv-integrins. These integrins initiate cellular processes that stimulate osteoblast adhesion, spreading, motility and differentiation. Taken together, these quantitative insights reveal an approach for the development of alternative collagen I- based surfaces for tissue engineering applications.

Is the world their oyster? The global imagination of pre-service teachers

This paper reports on a qualitative interview study with eleven pre-service primary teachers in Queensland about their career plans exploring whether and how a global imagination motivates this next generation of teachers. The study is framed within sociological theory of globalisation, with regard to the growing possibilities for international mobility for work purposes, and the new life circumstances which make this imaginable. Teaching as a profession has changed and teachers are no longer as entangled with specific systems or geographical locations anymore. International recruitment campaigns are shown to pursue these pre-service teachers during their university preparation. The analysis of the interview data reveals the kind of impact these possibilities make on how pre-service teachers imagine their career, and what other considerations enhance or limit their global imagination. The findings are used to reflect on the highly localised governance of pre-service teacher preparation and the limited state-bound imaginaries to which these pre-service teachers are unnecessarily confined in their preparation.

Automated road pavement marking detection from high resolution aerial images based on Multi-resolution image analysis and anisotropic Gaussian filtering

Road features extraction from remote sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: i) road centerline extraction from low resolution image, ii) road surface detection in the original image, and iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach.

Critical social theory and transformative learning : evidence in pre-service teachers' service-learning reflection logs

This paper reports on the opportunities for transformational learning experienced by a group of pre-service teachers who were engaged in service-learning as a pedagogical process with a focus on reflection. Critical social theory informed the design of the reflection process as it enabled a move away from knowledge transmission toward knowledge transformation. The structured reflection log was designed to illustrate the critical social theory expectations of quality learning that teach students to think critically: ideology critique and utopian critique. Butin's lenses and a reflection framework informed by the work of Bain, Ballantyne, Mills and Lester were used in the design of the service-learning reflection log. Reported data provide evidence of transformational learning and highlight how the students critique their world and imagine how they could contribute to a better world in their work as a beginning teacher.