Semi-automatic annotation and retrieval of visual content using the topic map technology

There are still major challenges in the area of automatic indexing and retrieval of multimedia content data for very large multimedia content corpora. Current indexing and retrieval applications still use keywords to index multimedia content and those keywords usually do not provide any knowledge about the semantic content of the data. With the increasing amount of multimedia content, it is inefficient to continue with this approach. In this paper, we describe the project DREAM, which addresses such challenges by proposing a new framework for semi-automatic annotation and retrieval of multimedia based on the semantic content. The framework uses the Topic Map Technology, as a tool to model the knowledge automatically extracted from the multimedia content using an Automatic Labelling Engine. We describe how we acquire knowledge from the content and represent this knowledge using the support of NLP to automatically generate Topic Maps. The framework is described in the context of film post-production.

Evolving features-algorithms knowledge map to support NIDS data intelligence and learning loop architecting – a generalised approach to NIDS pattern feature space knowledge processing

This paper describes a proposed new approach to the Computer Network Security Intrusion Detection Systems (NIDS) application domain knowledge processing focused on a topic map technology-enabled representation of features of the threat pattern space as well as the knowledge of situated efficacy of alternative candidate algorithms for pattern recognition within the NIDS domain. Thus an integrative knowledge representation framework for virtualisation, data intelligence and learning loop architecting in the NIDS domain is described together with specific aspects of its deployment.

Analysis of the dominant enterprise information assurance mechanisms

The purpose of this study is to analyse current data continuity mechanisms employed by the target group of businesses and to identify any inadequacies in the mechanisms as a whole. The questionnaire responses indicate that 47% of respondents do perceive backup methodologies as important, with a total of 70% of respondents having some backup methodology already in place. Businesses in Moulton Park perceive the loss of data to have a significant effect upon their business’ ability to function. Only 14% of respondents indicated that loss of data on computer systems would not affect their business at all, with 54% of respondents indicating that there would be either a “major effect” (or greater) on their ability to operate. Respondents that have experienced data loss were more likely to have backup methodologies in place (53%) than respondents that had not experienced data loss (18%). Although the number of respondents clearly affected the quality and conclusiveness of the results returned, the level of backup methodologies in place appears to be proportional to the company size. Further investigation is recommended into the subject in order to validate the information gleaned from the small number of respondents.

Evaluation of probabilistic occupancy map people detection for surveillance systems

In this paper, we evaluate the Probabilistic Occupancy Map (POM) pedestrian detection algorithm on the PETS 2009 benchmark dataset. POM is a multi-camera generative detection method, which estimates ground plane occupancy from multiple background subtraction views. Occupancy probabilities are iteratively estimated by fitting a synthetic model of the background subtraction to the binary foreground motion. Furthermore, we test the integration of this algorithm into a larger framework designed for understanding human activities in real environments. We demonstrate accurate detection and localization on the PETS dataset, despite suboptimal calibration and foreground motion segmentation input.

Generalised Dirichelt-to-Neumann map in time dependent domains

We study the heat, linear Schrodinger and linear KdV equations in the domain l(t) < x < ∞, 0 < t < T, with prescribed initial and boundary conditions and with l(t) a given differentiable function. For the first two equations, we show that the unknown Neumann or Dirichlet boundary value can be computed as the solution of a linear Volterra integral equation with an explicit weakly singular kernel. This integral equation can be derived from the formal Fourier integral representation of the solution. For the linear KdV equation we show that the two unknown boundary values can be computed as the solution of a system of linear Volterra integral equations with explicit weakly singular kernels. The derivation in this case makes crucial use of analyticity and certain invariance properties in the complex spectral plane. The above Volterra equations are shown to admit a unique solution.

The plastic self organising map

A novel extension to Kohonen's self-organising map, called the plastic self organising map (PSOM), is presented. PSOM is unlike any other network because it only has one phase of operation. The PSOM does not go through a training cycle before testing, like the SOM does and its variants. Each pattern is thus treated identically for all time. The algorithm uses a graph structure to represent data and can add or remove neurons to learn dynamic nonstationary pattern sets. The network is tested on a real world radar application and an artificial nonstationary problem.

Reduction of invasive tests in chagasic patients with a modified self-organizing map

The applicability of AI methods to the Chagas' disease diagnosis is carried out by the use of Kohonen's self-organizing feature maps. Electrodiagnosis indicators calculated from ECG records are used as features in input vectors to train the network. Cross-validation results are used to modify the maps, providing an outstanding improvement to the interpretation of the resulting output. As a result, the map might be used to reduce the need for invasive explorations in chronic Chagas' disease.