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.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

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.

L'étendue internationale des nouvelles firmes de hautes technologies: le rôle du capital humain et de l'intention stratégique des fondateurs

De récentes recherches ont mis l’accent sur l’importance pour les nouvelles entreprises internationales de l’existence de ressources et de compétences spécifiques. La présente recherche, qui s’inscrit dans ce courant, montre en particulier l’importance du capital humain acquis par les entrepreneurs sur base de leur expérience internationale passée. Mais nous montrons en même temps que ces organisations sont soutenues par une intention délibérée d’internationalisation dès l’origine. Notre recherche empirique est basée sur l’analyse d’un échantillon de 466 nouvelles entreprises de hautes technologies anglaises et allemandes. Nous montrons que ce capital humain est un actif qui facilite la pénétration rapide des marchés étrangers, et plus encore quand l’entreprise nouvelle est accompagnée d’une intention stratégique délibérée d’internationalisation. Des conclusions similaires peuvent être étendues au niveau des ressources que l’entrepreneur consacre à la start-up : plus ces ressources sont importantes, plus le processus d’internationalisation tend à se faire à grande échelle ; et là aussi, l’influence de ces ressources est augmenté par l’intention stratégique d’internationalisation. Dans le cadre des études empiriques sur les born-globals (entreprises qui démarrent sur un marché globalisé), cette recherche fournit une des premières études empiriques reliant l’influence des conditions initiales de création aux probabilités de croissance internationale rapide.

Generating rules with predicates, terms and variables from the pruned neural networks

Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in the form of restricted first-order predicate rules. The empirical results demonstrate that an equivalent symbolic interpretation in the form of rules with predicates, terms and variables can be derived describing the overall behaviour of the trained ANN with improved comprehensibility while maintaining the accuracy and fidelity of the propositional rules.

Condition-based prognosis of machine health

Modern machines are complex and often required to operate long hours to achieve production targets. The ability to detect symptoms of failure, hence, forecasting the remaining useful life of the machine is vital to prevent catastrophic failures. This is essential to reducing maintenance cost, operation downtime and safety hazard. Recent advances in condition monitoring technologies have given rise to a number of prognosis models that attempt to forecast machinery health based on either condition data or reliability data. In practice, failure condition trending data are seldom kept by industries and data that ended with a suspension are sometimes treated as failure data. This paper presents a novel approach of incorporating historical failure data and suspended condition trending data in the prognostic model. The proposed model consists of a FFNN whose training targets are asset survival probabilities estimated using a variation of Kaplan-Meier estimator and degradation-based failure PDF estimator. The output survival probabilities collectively form an estimated survival curve. The viability of the model was tested using a set of industry vibration data.

Conveying identity with mobile content

A series of mobile phone prototypes called The Swarm have been developed in response to the user needs identified in a three-year empirical study of young people’s use of mobile phones. The prototypes take cues from user led innovation and provide multiple avatars that allow individuals to define and manage their own virtual identity. This paper briefly maps the evolution of the prototypes and then describes how the pre-defined, color coded avatars in the latest version are being given greater context and personalization through the use of digital images. This not only gives ‘serendipity a nudge’ by allowing groups to come together more easily, it provides contextual information that can reduce gratuitous contact.

Synopolies: The use of cryptographic technologies to impede competition in multiple jurisdictions

Many jurisdictions have developed mature infrastructures, both administratively and legislatively, to promote competition. Substantial funds have been expended to monitor activities that are anticompetitive and many jurisdictions also have adopted a form of "Cartel Leniency Program", first developed by the US Federal Trade Commission, to assist in cartel detection. Further, some jurisdictions are now criminalizing cartel behaviour so that cartel participants can be held criminally liable with substantial custodial penalties imposed. Notwithstanding these multijurisdictional approaches, a new form of possibly anticompetitive behaviour is looming. Synergistic monopolies („synopolies‟) involve not competitors within a horizontal market but complimentors within separate vertical markets. Where two complimentary corporations are monopolists in their own market they can, through various technologies, assist each other to expand their respective monopolies thus creating a barrier to new entrants and/or blocking existing participants from further participation in that market. The nature of the technologies involved means that it is easy for this potentially anti-competitive activity to enter and affect the global marketplace. Competition regulators need to be aware of this potential for abuse and ensure that their respective competition frameworks appropriately address this activity. This paper discusses how new technologies can be used to create a synopoly.

From social butterfly to urban citizen workshop

Web applications such as blogs, wikis, video and photo sharing sites, and social networking systems have been termed ‘Web 2.0’ to highlight an arguably more open, collaborative, personalisable, and therefore more participatory internet experience than what had previously been possible. Giving rise to a culture of participation, an increasing number of these social applications are now available on mobile phones where they take advantage of device-specific features such as sensors, location and context awareness. This workshop made a contribution towards exploring and better understanding the opportunities and challenges provided by tools, interfaces, methods and practices of social and mobile technology that enable participation and engagement. It brought together a group of academics and practitioners from a diverse range of disciplines such as computing and engineering, social sciences, digital media and human-computer interaction to critically examine a range of applications of social and mobile technology, such as social networking, mobile interaction, wikis (eg., futuremelbourne.com.au), twitter, blogging, virtual worlds (eg, hub2.org), and their impact to foster community activism, civic engagement and cultural citizenship.

Hungry 24/7? HCI design for sustainable food culture workshop

This workshop proposes to explore new approaches to cultivate and support sustainable food culture in urban environments via human computer interaction design and ubiquitous technologies. Food is a challenging issue in urban contexts: while food consumption decisions are made many times a day, most food interaction for urbanites occurs based on convenience and habitual practices. This situation is contrasting to the fact that food is at the centre of global environment, health, and social issues that are becoming increasingly immanent and imminent. As such, it is timely and crucial to ask: what are feasible, effective, and innovative ways to improve human-food-interaction through human-computer-interaction in order to contribute to environmental, health, and social sustainability in urban environments? This workshop brings together insights across disciplines to discuss this question, and plan and promote individual, local, and global change for sustainable food culture.

MiLK. Mobile Learning Kit

The Mobile Learning Kit is a new digital learning application that allows students and teachers to compose, publish, discuss and evaluate their own mobile learning games and events. The research field was interaction design in the context of mobile learning. The research methodology was primarily design-based supported by collaboration between participating disciplines of game design, education and information technology. As such, the resulting MiLK application is a synthesis of current pedagogical models and experimental interaction design techniques and technologies. MiLK is a dynamic learning resource for incorporating both formal and informal teaching and learning practices while exploiting mobile phones and contemporary digital social tools in innovative ways. MiLK explicitly addresses other predominant themes in educational scholarship that relate to current education innovation and reform such as personalised learning, life-long learning and new learning spaces. The success of this project is indicated through rigorous trials and actual uptake of MiLK by international participants in Australia, UK, US and South Africa. MiLK was awarded for excellence in the use of emerging technologies for improved learning and teaching as a finalist (top 3) in the Handheld Learning and Innovation Awards in the UK in 2008. MiLK was awarded funding from the Australasian CRC for Interaction Design in 2008 to prepare the MiLK application for development. MiLK has been awarded over $230,000 from ACID since 2006. The resulting application and research materials are now being commercialised by a new company, ‘ACID Services’.