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CONDITIONAL COMPLEXITY OF COMPRESSION FOR AUTHORSHIP ATTRIBUTION M.B. Malyutov1, I.C. Wickramasinghe1 and S. Li2 Mathematics Department, 567 Lake Hall, Northeastern University, Boston, MA 02115 2
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We provide mathematical evidence for the superiority of this alternative to other forms of entropy estimation, such as the entropy-time approach and the Kolmogorov complexity. The results from the experiments used in our paper strongly suggest that for a text consisting of 1000 and 2000 randomly selected symbols, the former is better than the latter. We present a mathematical proof of our findings using an algorithm that yields a near perfect bound for the conditional complexity and show that many other approaches do not yield such a perfect bound. We also consider the conditional complexity of two languages: English and Russian that have been independently compiled in the same language.

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Conditional complexity of compression refers to the measure of how much the size of the compressed file depends on the input data. It indicates how well the compression algorithm can adapt to different types of input data.
The responsibility of filing the conditional complexity of compression lies with the data compression algorithm developers or companies implementing compression algorithms in their software.
Filling out the conditional complexity of compression requires conducting thorough experiments on the compression algorithm by using various input datasets. The data should be compressed, and the resulting sizes should be analyzed to determine the level of dependency on the input data.
The purpose of evaluating the conditional complexity of compression is to understand how well the compression algorithm performs with different types of input data. This information helps in selecting the appropriate compression algorithm for specific usage scenarios.
The conditional complexity of compression report should include details about the compression algorithm, the input datasets used for testing, the resulting compressed file sizes, and any observations or conclusions drawn from the analysis.
The deadline for filing the conditional complexity of compression in 2023 has not been specified. It is recommended to consult the relevant guidelines or regulatory authorities for the specific deadline.
The penalty for the late filing of conditional complexity of compression may vary depending on the legal regulations or requirements in place. It is advisable to check the applicable rules or consult legal experts to determine the exact penalty for late filing.
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