Implementation and Initial Foci

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Distributed agent-based building evacuation simulator

The optimisation of the evacuation of a building plays a fundamental role in emergency situations. The behaviour of individuals, the directions that civilians receive, and the actions of the emergency personnel, will affect the success of the operation. We describe a simulation system that represents the individual, intelligent, and interacting agents that cooperate and compete while evacuating the building. The system also takes into account detailed information about the building and the sensory capabilities that it may contain. Since the level of detail represented in such a simulation can lead to computational needs that grow at least as a polynomial function of the number of the simulated agents, we propose an agent-oriented Distributed Building Evacuation Simulator (DBES). The DBES is integrated with a wireless sensor network which offers a closed loop representation of the evacuation procedure, including the sensed data and the emergency decision making

A case study on graphically modelling and detecting knowledge mobility risks

As the world continues to increasingly depend on a knowledge economy, companies are realising that their most valuable asset is knowledge held by their employees. This asset is hard to track, manage and retain especially in a situation where employees are free to job-hop for better pay after providing a few weeks’ notice to their employers. In previous work we have defined the concept of knowledge risk, and presented a graph-based approach for detecting it. In this paper, we present the results of a case study which employs knowledge graphs in the context of four software development teams.peer-reviewe

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

Exploiting artistic cues to obtain line labels for free-hand sketches

Artistic cues help designers to communicate design intent in sketches. In this paper, we show how these artistic cues may be used to obtain a line labelling interpretation of freehand sketches, using a cue-based genetic algorithm to obtain a labelling solution that matches design intent. In the paper, we show how this can be achieved from off-line or paper based sketches, thereby allowing designers greater flexibility in the choice of sketching medium.peer-reviewe