Arabic Poetry of the 12th Century a.k.a. Satwal, Middle-earth and the Three Musket Games

Arabic Poetry of the 12th Century a.k.a. Satwal, Middle-earth and the Three Musket Games – Artificial Intelligence (AI) has achieved outstanding results in the field of natural language. In this paper, we propose a novel language-specific language-specific model developed for the purpose of computer-generated speech. The model is built on a network of artificial neural networks which are able to learn how to predict word-level words. The model is also tested on a real-world database of 5,000 spoken languages. We demonstrate that the learning procedure of the deep neural networks can produce an interesting result (i.e. not a bad word). This result was verified using a language analysis benchmark. The model was the most successful machine learning model we have used for this task. The system proved able to predict an interesting result while learning from only a small set of tokens.

Person recognition is a vital task in many computer-based applications, but human performance is typically too poor to be considered a benchmark. However, it’s very important to consider the role of the human to make the decisions regarding what person to recognize. This paper presents a novel approach for face recognition in action videos, which is based on a deep network. The network is trained for a multi-dimensional space (with both a facial and a visual input), which is capable to capture the human’s face attributes. Experiments show that the proposed model is capable of recognising human expressions (including the facial-expression similarity level) of human. Moreover, it makes it possible to identify people that have been described as being similar to the human. Therefore, the proposed approach may be useful to users of action-based video games.

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Arabic Poetry of the 12th Century a.k.a. Satwal, Middle-earth and the Three Musket Games

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  • Fast, Accurate Metric Learning

    Generating a Robust Multimodal Corpus for Robust Speech RecognitionPerson recognition is a vital task in many computer-based applications, but human performance is typically too poor to be considered a benchmark. However, it’s very important to consider the role of the human to make the decisions regarding what person to recognize. This paper presents a novel approach for face recognition in action videos, which is based on a deep network. The network is trained for a multi-dimensional space (with both a facial and a visual input), which is capable to capture the human’s face attributes. Experiments show that the proposed model is capable of recognising human expressions (including the facial-expression similarity level) of human. Moreover, it makes it possible to identify people that have been described as being similar to the human. Therefore, the proposed approach may be useful to users of action-based video games.


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