Practical algorithms for precedent-based recognition are considered that are based on the logical or algebraic correction of various heuristic algorithms or m odels of recognition. The recognition problem is solved in two stages. First, algorithms from a certain group are independently applied to recognition of an arbitrary object, and then an appropriate corrector is used to calculate the final collective solution. General concepts of the algebraic approach, descriptions of practical algorithms for logical and algebraic correction, and the results of comparison of these algorithms are presented.

Purpose -The research is aimed at developing an efficient electronic voting platform that would offer distinct advantages over traditional paper ballot voting and the available electronic voting systems. Design/methodology/approach -Based on the analysis of the existing technologies and the authors' prior findings, electronic voting was approached as a public information and communication technologies service. Findings -A new methodology of forming election event outcomes is proposed, which is based on the outcomes of internet transactions between web portals such as "The Guarantor" and millions of remote electors, voting event officials and independent observers (auditors). The paper presents the structure of a state-scale voting system that collects, processes publishes the results of different election events. Originality/value -The system can accept votes cast online by internet or SMS, by mail, via electronic kiosks and by special computer-filled paper ballots at polling stations. The system also provides a number of new possibilities for network verification of voter registration and the individual votes without compromising the voters' privacy. A model implementation of a web portal for remote monitoring of election events and individual outcome verification is presented.

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of one and many objects in video. The following metrics are considered: the quality of detection of tracked objects, the accuracy of determining the location of the object in a frame, the trajectory of movement, the accuracy of tracking multiple objects. Based on the considered generalization, an algorithm for tracking people has been developed that uses the tracking through detection method and convolutional neural networks to detect people and form features. Neural network features are included in a composite descriptor that also contains geometric and color features to describe each detected person in the frame. The results of experiments based on the considered criteria are presented, and it is experimentally confirmed that the improvement of the detector operation makes it possible to increase the accuracy of tracking objects. Examples of frames of processed video sequences with visualization of human movement trajectories are presented.