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Based on the naming convention, DCL 2.0 identifies the code artifacts related to the same concept (matching keyword).
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Additionally it readily identifies the coding part of the unigenes, which usually contain untranslated regions.
The objective of the system is to identify the code clones of a target malware from a collection of previously analyzed malware binaries.
This step allows programmers to investigate the power profile of their applications and identify the code areas with higher energy consumption.
So, we present here state-of-the-art techniques to identify the code word length of binary linear block codes.
In this work, we are only interested in blindly identifying the code word length of linear non-binary block codes.
In this work, the aim is to blindly identify the code word length from the only knowledge of received data.
In this part, we present the implementation method which allows us to identify the code word length of a non-binary code in a noisy environment.
The square deviation gain (SDG) between the input and output of the model is used to identify the coding regions.
By using two different designs for the two coding strands, we were able to identify the coding strand of 61 small RNA molecules (95%).
We performed a new round of alignments to identify the coding region (CDS) of each mapped transcript.
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CEO of Professional Science Editing for Scientists @ prosciediting.com