Annotating training data for sequence tagging of texts is usually very time-consuming. Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget. We are the first to thoroughly investigate this powerful combination for the sequence tagging task. We conduct an extensive empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pretrained models in the active learning framework and find the best combinations for different types of models. Besides, we also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance and reduces obstacles for applying deep active learning in practice.

We present a comparative study of the players' and professional players' (athletes') performance in Counter Strike: Global Offensive (CS:GO) discipline.Our study is based on ubiquitous sensing helping identify the biometric features significantly contributing to the classification of particular skills of the players. The research provides better understanding why the athletes demonstrate superior performance as compared to other players.eSports is an organized and competitive gaming with a specific goal at the end of a game where single players or teams compete against each other. In spite of popularity and official recognition of esports, the debates as to the assessment of esports as an actual sport and comparing it to a sport are still going on 3 . The global esports audience numbered 380.2 million in 2018 and has tended to grow up to 557 million by 2021 (1) . Apart from the rapid increase in the quantity of professional athletes and teams, the number of players has dramatically gone up: 27 million people play League of Legends every day 1 . In this research we define an athlete as a professional player with a work contract with a professional eSports team (we use athlete and professional player interchangeably throughout the article). A player is a person without the eSports contract while having relevant game skills or status.In spite of popularity and official recognition of esports, the debates as to the assessment of esports as an actual sport are still going on. Although professional esports players spend 8-12 hours(1) Newzoo. 2018 Global Esports Market Report. https://asociacionempresarialesports.es/wpcontent/uploads/newzoo_2018_global_esports_market_report_excerpt.pdf

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Using highly interactive systems like computer games requires a lot of visual activity and eye movements. Eye movements are best characterized by visual fixation -periods of time when the eyes stay relatively still over an object. We analyzed the distributions of fixation duration of professional athletes, amateur and newbie players. We show that the analysis of fixation durations can be used to deduce the skill level in computer game players. Highly skilled gaming performance is characterized by more variability in fixation durations and by bimodal fixation duration distributions suggesting the presence of two fixation types in high skill gamers. These fixation types were identified as ambient (automatic spatial processing) and focal (conscious visual processing). The analysis of computer gamers' skill level via the analysis of fixation durations may be used in developing adaptive interfaces and in interface design.

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eSports is a developing multidisciplinary research area. At present, there is a lack of relevant data collected from real eSports athletes and lack of platforms which could be used for the data collection and further analysis. In this paper, we present a sensing system for enabling the data collection from professional athletes. Also, we report on the case study about collecting and analyzing the gaze data from Monolith professional eSports team specializing in Counter-Strike: Global Offensive (CS:GO) discipline. We perform a comparative study on assessing the gaze of amateur players and professional athletes. The results of our work are vital for ensuring eSports data collection and the following analysis in the scope of scouting or assessing the eSports players and athletes.

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