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In the domain of web security, websites strive to prevent themselves from data gathering performed by automatic programs called bots. In that way, crawler traps are an efficient brake against this kind of programs. By creating similar pages or random content dynamically, crawler traps give fake information to the bot and resulting by wasting time and resources. Nowadays, there is no available bots able to detect the presence of a crawler trap. Our aim was to find a generic solution to escape any type of crawler trap. Since the random generation is potentially endless, the only way to perform crawler trap detection is on the fly. Using machine learning, it is possible to compute the comparison between datasets of webpages extracted from regular websites from those generated by crawler traps. Since machine learning requires to use distances, we designed our system using information theory. We considered widely used distances compared to a new one designed to take into account heterogeneous data. Indeed, two pages does not have necessary the same words and it is operationally impossible to know all possible words by advance. To solve our problematic, our new distance compares two webpages and the results showed that our distance is more accurate than other tested distances. By extension, we can say that our distance has a much larger potential range than just crawler traps detection. This opens many new possibilities in the scope of data classification and data mining.
This article explores the features and requirements of intelligent virtual reality tutoring systems – an innovative type of educational software. It also explores the applicability of these systems as educational simulators and classifies them with the help of existing typologies. The authors present a detailed discussion of immersive virtual learning environments, the penetration of artificial intelligence into this domain, and the effects of this penetration. The paper seems to be the first publication to survey intelligent virtual reality tutoring systems (IVRTSs). The results are our own IVRTS that can act as an educational simulator for kinematics as well as a set of recommendations on IVRTS design and implementation.