This paper describes a method for unsupervised classification of events in multi-camera indoors surveillance video. This research is a part of the Multiple Sensor Indoor Surveillance (MSIS) project which uses 32 AXIS-2100 webcams that observe an office environment. The research was inspired by the following practical problem: how automatically classify and visualize a 24 hour long video captured by 32 cameras? Raw data are sequences of JPEG images captured by webcams at the rate 2-6 Hz. The following features are extracted from the image data: foreground pixels' spatial distribution and color histogram. The data are integrated by event by averaging motion and color features and creating a "summary" frame which accumulates all foreground pixels of frames of the event into one image. The self-organizing map (SOM) approach is applied to event data for clustering and visualization. One-level and two-level SOM clustering are used. A tool for browsing results allows exploring units of the SOM maps at different levels of hierarchy, clusters of units and distances between units in 3D space. A special technique has been developed to visualize rare events. The results are presented and discussed.

We present virtual training worlds that are relatively low-cost distributed collaborative learning environments suitable for corporate training. A virtual training world allows a facilitator, experts and trainees communicating and acting in the virtual environment for practicing skills during collaborative problem solving. Using these environments is beneficial to both trainees and corporations. The design of a leadership training course is discussed in details.

Challenges in multimedia analysis are calling for the sharing of research efforts, while in practice collaboration is hindered by technical and proprietary issues. The Community of Multimedia Agents project (COMMA) attempts to solve this problem by creating an open environment for developing, testing, and prototyping multimedia content analysis and annotation methods. Each method is represented as an agent (an executable module) that can communicate with the other agents based on descriptors and description schemes in the coming MPEG-7 standard. This allows multimedia-processing agents developed by different organizations to operate and collaborate with each other, regardless of their programming languages and internal architecture. The researchers can compare the performance of agents and combine them to build more powerful and robust system prototypes. It can also serve as a learning environment for researchers and students to acquire and test cutting edge multimedia analysis algorithms. Through sharing of media agents, the Community can increase efficiency of research while protecting the intellectual property of the inventors.

This paper presents an overview of speech analysis, visualization and student response evaluation techniques that can be used for learning a foreign language by an adult learner. The general framework for student response evaluation is described. It is based on collecting experimental data about experts' and novices' performance and applying machine learning and knowledge management techniques for deriving evaluation rules. Application of the proposed approach to language learning tasks is discussed.

The paper describes an experimental study on vocal emotion expression and recognition and the development of a computer agent for emotion recognition. The study deals with a corpus of 700 short utterances expressing five emotions: happiness, anger, sadness, fear, and normal (unemotional) state, which were portrayed by thirty subjects. The utterances were evaluated by twenty three subjects, twenty of whom participated in recording. The accuracy of recognition emotions in speech is the