In this work, we present a dataset which provides information on the scientific program of a set conferences of Machine Learning. Data were extracted from the IEEE Xplore Digital Library and the official web site of the International Conference on Machine Learning Applications (ICMLA). We include data of four different editions (from 2014 to 2017). Web scrapping techniques were used to mine the data contained in these web sites. The dataset covers 448 papers presented in the conference and every paper contains 6 attributes including information about the thematic session in which they were presented in the conference. The dataset is hosted in the Mendeley Dataset Repository.
Traffic psychology covers not only the act of driving but also includes inherent phenomena such as accidents, violations, and the generation of emotions. This article proposes the development of a driving simulator within the video game Grand Theft Auto V, taking advantage of the artificial intelligence of characters that interact with the player, as well as other drivers and pedestrians. The implementation includes the adaptation of steering wheel type controls and the creation of a mod in C# for data collection and export. This implementation seeks to obtain useful data for the analysis of the behavior of the drivers evaluated during the driving tests carried out in three virtual circuits within a city. By performing the tests with drivers of different levels of experience, it was possible to verify the effectiveness of the tool in its use and realism. According to the individuals evaluated, the simulator achieved an average realism of 4.6 on a Likert scale from 1 to 5. Likewise, the results obtained show that the data collected by the simulator is less biased than those obtained through a post-driving survey.
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