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Miss Martha Smons

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Using Language Theory to Improve the translation of ISO into Sindhi: A case study in English
This paper proposes a comprehensive set of tools and techniques for building a bilingual parser for the NLP Parsing Language (PL). The tool is a tool named Language-wise Parsing Machine. It is capable of generating PL sentences using any language, and the parser has been written by M.F.T.A. using a machine translation system. To obtain a PL sentence from this system, we have built a neural network and trained it to parse PL sentences. This work shows that the neural network's performance is better than those of a classical neural model as a result of the use of a language learned from a parser. This paper also shows that the system is able to produce PL sentences as a result of this network using natural language. It shows that the parser produced PL sentences in both languages were able to translate the sentences.
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Deep-Person Recognition: A Benchmark
This paper investigates the effectiveness of a novel method for the automatic detection of human-body interactions (including a facial pose) in action sequences. The method is based on the assumption that the human action sequence is part of an action sequence and is characterized by human actions during the sequence. The model is able to capture the human body poses within a context of the action sequence and can accurately detect and distinguish persons of multiple identities. We present a novel method for automatic human pose estimation from the human-body interaction dataset to date. The proposed method is trained on a well-established human model (using a human subject) and tested on a set of large-scale 3D human pose datasets. The proposed method is able to achieve accuracies comparable to human pose estimation under the same training regime using only human body pose data and a human face data.
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