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The purpose of this paper is to present a procedure for finding unusual patterns in accredited tests using a rapid processing method for analyzing video records. The procedure uses the temporal differencing technique for object tracking and considers only frames not identified as statistically redundant.

An accreditation organization is responsible for accrediting facilities to undertake testing and calibration activities. Periodically, such organizations evaluate accredited testing facilities. These evaluations could use video records and photographs of the tests performed by the facility to judge their conformity to technical requirements. To validate the proposed procedure, a real-world data set with video records from accredited testing facilities in the field of vehicle safety in Brazil was used. The processing time of this proposed procedure was compared with the time needed to process the video records in a traditional fashion.

With an appropriate threshold value, the proposed procedure could successfully identify video records of fraudulent services. Processing time was faster than when a traditional method was employed.

Manually evaluating video records is time consuming and tedious. This paper proposes a procedure to rapidly find unusual patterns in videos of accredited tests with a minimum of manual effort.

Automatic, autonomous browsing has an increasingly important task in information discovery and assisted browsing on the Internet. Where users could once keep up to date with information of interest on the Internet, the recursive growth of the network has made this process increasingly time consuming and less rewarding. Presents two possible solutions to this problem: Data Agents and CollaborAgents which were developed with IBM’s Aglet Workbench – a particular implementation of mobile agents. Also surveys the agent technology and discusses the agent building package used to develop both mentioned applications. Concludes that the future of local interaction, reduced network loading, server flexibility and application autonomy which are supported by mobile agent technology, all help to provide a level agility above distributed problem solving.

Nowadays the Internet offers a large amount of information to a wide range of users, making it difficult to deal with. The present work suggests the use of intelligent agents for the personalized filtering of Web pages. A set of autonomous, non‐mobile and adaptive agents was developed, aiming to satisfy the user’s need for information. The agents learn from the users’ feedback and attempt to produce better results over time. This work presents the system description and the promising results of tests performed in a simulated environment. The proposed system has proven to be a useful tool in reducing the amount of information with which the user has to deal.