This paper presents the model of a robot software capable of doingsome functions inside the online role-playing game Tibia automaticallywhile avoiding the Battleye anti-cheating system and showcountermeasures that can help Battleye and Tibia developers, aswell as developers of similar games to prevent this type of bot fromsurpassing their anti-cheating system. Among these functions arethe hunting, the healing, and the looting. Specifically, there is anexplanation of the game’s mechanics and how the robot surpassesthe anti-cheating system by analyzing the game screen and simulatingmouse and keyboard functions, without doing any memoryinjection or memory reading. The evaluation of the bot was doneduring six months on ten accounts with different variables suchas the character’s vocation, daily playing time, account status, andwhether the character is hunting in the same place every day orvariation occurs.
The massive amount of information currently available on the Internetmakes it difficult for teachers to curate quality educationalcontent or to select material for self-regulated study by students.Aiming to facilitate these steps in the teaching and/or learningprocess this article presents an approach to assist the discovery ofeducational content from the hybrid recommendation system andlater classification from the feedback evaluation with sentimentanalysis techniques trained with the Re-Li corpus. This paper describesthe proposed model, the implementation of a prototype andits application in non-formal training involving 13 participants.
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