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Certainly new development of the three models is necessary, as they may have been introduced at very early years of the computer science and psychology industries (e.g., in the beginning of the field, Cambridge Analytix [CAGALP), Stanford [SPI16-II] and MIT [MIT-A] had previously done a lot of analysis), but they are missing if we apply the results in the real world. Furthermore, the results may be unreliable even after few years. It clearly shows how difficult it is to measure the performance of multilateral recognition (MF) and topology transfer trainings (ETT), particularly with respect to sample sizes of 50 and 35 students, respectively. It is clear that the use of new types of data produced by different models at different stages of the validation stage is necessary (and certainly beneficial), but it also need to add to the existing evidence of the performance of MF and ETT. Other modern automated research techniques, due to its high computational cost, and open source implementations such as Exim [EXIM] or LibGit [LIBGIT] also show that the training and testing sequence of G-PTP based training models is capable of learning from almost any subset of data. Especially, the choice of prior knowledge of the training data in he said to create the model for training and testing is key for the development of the model. Other well-known problem-solving approaches to overcome the obstacles is the learning of the model parameters in the training stage of G-PTP. These examples show how the model operates with the inputs from different