The detection of hate speech, especially in online platforms and forums, is quickly becoming a hot topic as anti-hate speech legislation begins to be applied to public discourse online. The HatEval shared task was created with this in mind; participants were expected to develop a model capable of determining whether or not input (in this case, Twitter posts in English and Spanish) could be considered hate speech (designated as Subtask A), if they were aggressive, and whether the tweet was targeting an individual, or speaking generally (Subtask B). We approached this Subtask by creating a LSTM model with an embedding layer. We found that our model performed considerably better on English language input when compared to Spanish language input. In English, we achieved an F1-Score of 0.466 for Subtask A and 0.462 for Subtask B; In Spanish, we achieved scores of 0.617 and 0.612 on Subtask A and Subtask B, respectively.
This paper presents a method to identify non-linear-systems in a real time environment. Acquiring the system's transfer function accurately could be extremely difficult once it has been assembled, which causes a great difficulty in the non-linear system modeling and control. Therefore in this research, Mixed Reality Environment (MRE) has been employed to identify the system's transfer function using Auto-Regressive Moving Average (ARMAX) model algorithm in order to avoid the complexity associated with nonlinear systems modeling. Online system identification can be conducted effectively and efficiently using the proposed method. The advantages of the proposed method are high accuracy in the identified system, simplicity, and low cost. The results obtained from on line experimental measured data are used to determine discrete transfer function of the system, 4th order model with one step prediction shows best performance.
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