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The advancement of modern Information and Communication Technologies (ICT) along with e-governments initiatives and the new digital world, during Covid-19, forced users to look for Free Open Source Software (FOSS) solutions, to serve different normal or specific needs, which is the real drawback of some Commercial software. The digital world is here and it needs modern management using advanced FOSS technology. We will present an overview of the Architecture of the Free Open Source Web GIS platform to demonstrate its easiness, practicality, and ability to perform effectively and better than commercial GIS tools. The developed platform proposed will be explained based on the succession of published applications of a project which is a "Sewage Infrastructure Network". A Web Map application for the project has been developed; starting from loading the software and data collection to publishing the results on the web. The Add-ons and other complementary software tools were used for full GIS creation and functions with database management, Applications, and an easy online updating of the published data. Applications, publishing, and easy online updating at low cost are major issues to future success and the OS is the solution. [ABSTRACT FROM AUTHOR]

This paper presents techniques to verify the identity of computer users using the keystroke dynamics of computer user's login string as characteristic patterns using pattern recognition and neural network techniques. This work is a continuation of our previous work [1]-[3] where only interkey times were used as features for identifying computer users. In this work we used the key hold times for classification and then compared the performance with the former interkey time-based technique. Then we use the combined interkey and hold times for the identification process. We applied several neural network and pattern recognition algorithms for verifying computer users as they type their password phrases. It was found that hold times are more effective than interkey times and the best identification performance was achieved by using both time measurements. An identification accuracy of 100% was achieved when the combined hold and intekey time-based approach were considered as features using the fuzzy ARTMAP, radial basis function networks (RBFN), and learning vector quantization (LVQ) neural network paradigms. Other neural network and classical pattern algorithms such as backpropagation with a sigmoid transfer function (BP, Sigm), hybrid sum-of-products (HSOP), sum-of-products (SOP), potential function and Bayes' rule algorithms gave moderate performance. Using the hold time as a feature for identifying the computer user is novel. Index Terms - Classifications, interkey times, key hold times, neural networks, performance evaluation.