A framework towards a multi-modal fingerprinting scheme for multimedia assets

Fingerprinting is a well known approach for identifying multimedia data without having the original data present but instead what amounts to its essence or 'DNA'. Current approaches show insufficient deployment of various types of knowledge that could be brought to bear in providing a fingerprinting framework that remains effective, efficient and can accommodate both the whole as well as elemental protection at appropriate levels of abstraction to suit various Zones of Interest (ZoI) in an image or cross media artefact. The proposed framework aims to deliver selective composite fingerprinting that is powerfully aided by leveraging both multi-modal information as well as a rich spectrum of collateral context knowledge including both image-level collaterals and also the inevitably needed market intelligence knowledge such as customers' social networks interests profiling which we can deploy as a crucial component of our fingerprinting collateral knowledge.

Nonlinear system identification using particle swarm optimisation tuned radial basis function models

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

Factors mediating the routinisation of e-learning within a traditional university education environment

Technology-enhanced or Computer Aided Learning (e-learning) can be institutionally integrated and supported by learning management systems or Virtual Learning Environments (VLEs) to offer efficiency gains, effectiveness and scalability of the e-leaning paradigm. However this can only be achieved through integration of pedagogically intelligent approaches and lesson preparation tools environment and VLE that is well accepted by both the students and teachers. This paper critically explores some of the issues relevant to scalable routinisation of e-learning at the tertiary level, typically first year university undergraduates, with the teaching of Relational Data Analysis (RDA), as supported by multimedia authoring, as a case study. The paper concludes that blended learning approaches which balance the deployment of e-learning with other modalities of learning delivery such as instructor–mediated group learning etc offer the most flexible and scalable route to e-learning but that this requires the graceful integration of platforms for multimedia production, distribution and delivery through advanced interactive spaces that provoke learner engagement and promote learning autonomy and group learning facilitated by a cooperative-creative learning environment that remains open to personal exploration of constructivist-constructionist pathways to learning.

Finding the point on Bezier Curves with the normal vector passing an external point

Two algorithms for finding the point on non-rational/rational Bezier curves of which the normal vector passes through a given external point are presented. The algorithms are based on Bezier curves generation algorithms of de Casteljau's algorithm for non-rational Bezier curve or Farin's recursion for rational Bezier curve, respectively. Orthogonal projections from the external point are used to guide the directional search used in the proposed iterative algorithms. Using Lyapunov's method, it is shown that each algorithm is able to converge to a local minimum for each case of non-rational/rational Bezier curves. It is also shown that on convergence the distance between the point on curves to the external point reaches a local minimum for both approaches. Illustrative examples are included to demonstrate the effectiveness of the proposed approaches.

Risk accountability in the tender process of contractors in Ghana and UK

The process of how contractors take account of risk when calculating their bids for construction work is investigated based on preliminary investigations and case studies in Ghana and UK. Ghana and UK were chosen, more or less arbitrarily, for the purpose of case studies, and to test the idea that there are systematic differences between the approaches in different places. Clear differences were found in the risk pricing approaches of contractors in the two countries. The difference appeared to emanate from the professional knowledge and competence of the bid team members, company policy, corporate accountability and the business environments in which the contractors operate. Both groups of contractors take account of risk in estimates. However, risk accountability was found to be higher on the agenda in the tender process of UK contractors, documented more systematically, and assessed and managed more rigorously with input from the whole bid team. Risk accountability takes place at three levels of the tender process and is dictated strongly by market forces and company circumstances.

Defining a framework for the evaluation of information

In any enterprise, decisions need be made during the life cycle of information about its management. This requires information evaluation to take place; a little-understood process. For evaluation support to be both effective and resource efficient, some sort of automatic or semi-automatic evaluation method would be invaluable. Such a method would require an understanding of the diversity of the contexts in which evaluation takes place so that evaluation support can have the necessary context-sensitivity. This paper identifies the dimensions influencing the information evaluation process and defines the elements that characterise them, thus providing the foundations for a context-sensitive evaluation framework.

Forecasting UK real estate cycle phases with leading indicators: a probit approach

This paper examines the significance of widely used leading indicators of the UK economy for predicting the cyclical pattern of commercial real estate performance. The analysis uses monthly capital value data for UK industrials, offices and retail from the Investment Property Databank (IPD). Prospective economic indicators are drawn from three sources namely, the series used by the US Conference Board to construct their UK leading indicator and the series deployed by two private organisations, Lombard Street Research and NTC Research, to predict UK economic activity. We first identify turning points in the capital value series adopting techniques employed in the classical business cycle literature. We then estimate probit models using the leading economic indicators as independent variables and forecast the probability of different phases of capital values, that is, periods of declining and rising capital values. The forecast performance of the models is tested and found to be satisfactory. The predictability of lasting directional changes in property performance represents a useful tool for real estate investment decision-making.