Semantic-driven context-aware visual information indexing and retrieval: applied in the film post-production domain

A large volume of visual content is inaccessible until effective and efficient indexing and retrieval of such data is achieved. In this paper, we introduce the DREAM system, which is a knowledge-assisted semantic-driven context-aware visual information retrieval system applied in the film post production domain. We mainly focus on the automatic labelling and topic map related aspects of the framework. The use of the context- related collateral knowledge, represented by a novel probabilistic based visual keyword co-occurrence matrix, had been proven effective via the experiments conducted during system evaluation. The automatically generated semantic labels were fed into the Topic Map Engine which can automatically construct ontological networks using Topic Maps technology, which dramatically enhances the indexing and retrieval performance of the system towards an even higher semantic level.

Information sources and advice used by small business tenants when leasing commercial property

Many small businesses lease commercial premises. The terms of a lease can affect the ability of the business to grow and adapt and have an impact on cashflow. Ensuring that they have the information with which to negotiate terms is part of the UK government policy focus on small businesses. Such information is most effectively disseminated through the sources of advice that small businesses use during the leasing process. Therefore these sources of advice need identifying. An interview survey of small business tenants who have recently taken leases provides initial results that suggest small businesses do not seek out advice during the leasing process or see the need to be better informed. The only formal professional input is from solicitors but this is not until after the main commercial terms have been agreed. The landlords’ letting agents play a key, but ambiguous, role in providing information as well as advice. These results suggest that the most effective way of disseminating information by government could be via the letting agents, the very people with whom the tenants are negotiating.

Selecting from amongst non–nested conditional variance models: information criteria and portfolio determination

We consider the finite sample properties of model selection by information criteria in conditionally heteroscedastic models. Recent theoretical results show that certain popular criteria are consistent in that they will select the true model asymptotically with probability 1. To examine the empirical relevance of this property, Monte Carlo simulations are conducted for a set of non–nested data generating processes (DGPs) with the set of candidate models consisting of all types of model used as DGPs. In addition, not only is the best model considered but also those with similar values of the information criterion, called close competitors, thus forming a portfolio of eligible models. To supplement the simulations, the criteria are applied to a set of economic and financial series. In the simulations, the criteria are largely ineffective at identifying the correct model, either as best or a close competitor, the parsimonious GARCH(1, 1) model being preferred for most DGPs. In contrast, asymmetric models are generally selected to represent actual data. This leads to the conjecture that the properties of parameterizations of processes commonly used to model heteroscedastic data are more similar than may be imagined and that more attention needs to be paid to the behaviour of the standardized disturbances of such models, both in simulation exercises and in empirical modelling.