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stimtion t ffiin f mngmnt in muniil frmtins (fter th ml f th Rubli f shkortostan)

Yangirov A.V. Doctor of Economic Sciences, Dean of Economics and Mathematics Faculty in Neftekamsk branch of Bashkir State University ( jangirovav@list.ru )

Samigullina E. post-graduate student of the department of the theory of economics and economical policy, Bashkir academy of state service and government under the President of the Republic of Bashkortostan auspices ( izabella.78@mail.ru )

Journal: Regional Economics: Theory and Practice, #5, 2011

h approach is dsribd t stimt th ffiin f mngmnt in municil frmtins. h rtiularit f ur rh is t rvl th bjtiv nd subjtiv ftrs of th muniil develment, t nd th strum f th nmic ftrs t ffiin f muniil mangment nd t llt th mount f municil mlos, th level f th muniil budgtr nss with rdutin-nmi nd scil dvloment f a it r rgin. Fur matris f shring th muniil frmations f th rubli f shkrtstan was md dnding n th sifi munt f the muniil mls, th budgetar sul er it nd th level f rduction-nmi nd sil development.


User identification through keystroke dynamics as part of automated proctoring systems

Abzalov A.R. Kazan National Research Technical University named after A.N. Tupolev KAI (KNRTU-KAI), Kazan, Republic of Tatarstan, Russian Federation ( abzalov@land.ru )

Zhiganov A.V. Kazan National Research Technical University named after A.N. Tupolev KAI (KNRTU-KAI), Kazan, Republic of Tatarstan, Russian Federation ( Lesha.159@yandex.ru )

Samigullina R.R. Kazan National Research Technical University named after A.N. Tupolev KAI (KNRTU-KAI), Kazan, Republic of Tatarstan, Russian Federation ( Samigullina0304@mail.ru )

Journal: National Interests: Priorities and Security, #3, 2020

Subject Popular online courses and testing programs integrate into correspondence education systems, which are more often than not based on automated proctoring. What makes the latter vulnerable is user identification.
Objectives We examine user identification methods through keystroke dynamics and devise a more accurate and effective technique for user identification through keystroke dynamics.
Methods The article sets out a three-tiered model for identifying users more accurately not only in automated proctoring environments, but also in critically sensitive locations.
Results We had an experiment, which showed a 97.5 percent accuracy of user identification. We significantly reduced illegitimate users at the statistical level of the three-tiered model.
Conclusions and Relevance Following the study, it is possible to develop a logic comparison method for higher accuracy. It will serve for creating a more refined model, which would accommodate for distinctions of each user and some deviations of users emotions. This would contribute to continuous user identification systems to monitor their emotional condition at critical locations.


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