This paper presents a sequence transcription approach for the automatic diacritization of Arabic text. A recurrent neural network is trained to transcribe undiacritized Arabic text with fully diacritized sentences. We use a deep bidirectional long short-term memory network that builds high-level linguistic abstractions of text and exploits longrange context in both input directions. This approach differs from previous approaches in that no lexical, morphological, or syntactical analysis is performed on the data before being processed by the net. Nonetheless, when the network is postprocessed with our error correction techniques, it achieves state-of-the-art performance, yielding an average diacritic and word error rates of 2.09 and 5.82 %, respectively, on samples from 11 books. For the LDC ATB3 benchmark, this approach reduces the diacritic error rate by 25 %, the word error rate by 20 %, and the last-letter diacritization error rate by 33 % over the best published results.

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Chord is a structured peer-to-peer (P2P) overlay network in which participating peers share resources as equals. To find a specific data item within the network, Chord system provide a lookup mechanism that matches a given key to a network node responsible for the value associated with that key. Chord is recently proposed to become one of the new approaches for building large-scale Internet applications. This paper aims to survey the Chord network, study its main characteristics, and compare its original performance with the performance of the enhancements being deployed over the original Chord. General TermsDistributed systems.