Abstract:Researchers have proposed intelligent product-brokering applications to help facilitate the m-commerce shopping process. However, most algorithms require explicit, user-provided feedback to learn about user preference. In practical applications, users may not be motivated to provide unrewarded and time-consuming feedback. By adopting a cognitive approach, this paper investigates the possibility of replacing user feedback with user behavioral data analysis during product browsing. By means of evolutionary algorithms, the system is able to derive corresponding models that simulate the user's shopping behavior. User group profiling is also implemented to help identify the user's shopping patterns. Upon simulations of trial cases with consistent and rational shopping patterns, our experimental results confirm this approach being promising. The system shows high accuracy in detecting the preferences of the user. The algorithms are also portable and effective across different products. © Imperial College Press.

Abstract:Cerebral palsy (CP) is generally considered as a non-progressive neuro-developmental condition that occurs in early childhood and is associated with a motor impairment, usually affecting mobility and posture. Automatic accurate identification of cerebral palsy gait has many potential applications, for example, assistance in diagnosis, clinical decision-making and communication among the clinical professionals. In previous studies, support vector machine (SVM) and some other pattern classification methods like neural networks have been applied to classify CP gait patterns. The objective of this study is to first further investigate different classification paradigms in the CP gait analysis, particularly the Kernel Fisher Discriminant Analysis (KFD) which has been successfully applied to many pattern recognition problems and identified as a strong competitor of SVM. The component obtained by KFD maximally separates two classes in the feature space, thus overcoming the limitations of linear discriminant analysis of being unable to extract nonlinear features representing higher-order statistics. Using a publicly available CP gait dataset (68 normal healthy and 88 with spastic diplegia form of CP), a comprehensive performances comparison was presented with different features including the two basic temporal-spatial gait parameters (stride length and cadence). Various cross-validation testing show that the KFD offers better classification accuracies than the support vector machine and is superior to a number of other classification methods such as decision tree, multiple layer perceptron and k nearest neighbor.

Abstract:Content-based image retrieval (CBIR) has certain advantages over those pure keyword-based. CBIR indexes images by visual features that are extracted from the images. This may save the effort spent on the manual annotation. However, because low-level visual features, such as colour and texture, often carry no high-level concepts, images retrieved purely based on content may not match with the intention of the user. The work presented in this paper is an image retrieval system that bases both on text annotations and visual contents. It indexes and retrieves images by both keywords and visual features, with the purpose that the keywords may mend the gap between the semantic meaning an image carries and its visual content. Tests were made on the system that have demonstrated that such a hybrid approach did improve retrieval precisions over those pure content-based.

Abstract:In this paper, model-based approaches for real-time 3-D soccer ball tracking are proposed, using image sequences from multiple fixed cameras as input. The main challenges include filtering false alarms, tracking through missing observations, and estimating 3-D positions from single or multiple cameras. The key innovations are: 1) incorporating motion cues and temporal hysteresis thresholding in ball detection; 2) modeling each ball trajectory as curve segments in successive virtual vertical planes so that the 3-D position of the ball can be determined from a single camera view; and 4) introducing four motion phases (rolling, flying, in possession, and out of play) and employing phase-specific models to estimate ball trajectories which enables high-level semantics applied in low-level tracking. In addition, unreliable or missing ball observations are recovered using spatio-temporal constraints and temporal filtering. The system accuracy and robustness are evaluated by comparing the estimated ball positions and phases with manual ground-truth data of real soccer sequences.

Abstract:Existing architecture description languages mainly support the specification of the structural elements of the system under design with either only a limited support or no support to specify non-functional requirements. In a component-based development of trustworthy systems, the trustworthiness properties must be specified at the architectural level. Analysis techniques should be available to verify the trustworthiness properties early at design time. Towards this goal we present in this paper a meta-architecture and TADL, a new architecture description language suited for describing the architecture of trustworthy component-based systems. The TADL is a uniform language for specifying the structural, functional, and nonfunctional requirements of component-based systems. It also provides a uniform source for analyzing the different trustworthiness properties. © 2008 Springer-Verlag Berlin Heidelberg.

Abstract:In the field of evolutionary computation, an important attribute of a population is diversity. This paper proposes a method for measuring the diversity of a particle swarm optimization population. It involves the measurement of position and velocity attributes of the particles that comprise the population. The proposed method is computationally straightforward and is adaptable to other evolutionary algorithms.