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4) The writing still needs quite some work.  While it does not hinder understanding,
     the writing does provide a distraction.  I'm just going to point out some
     high-level points:
     
     a) Avoid contractions (don't => do not, cannot => cannot)
     b) Avoid "very" (for example, in the first sentence of the abstract; "secure"
        here is already a strong statement, and with "very" you're rather contradicting
        the rest of your paper...)
     c) Section X and Table X (capitalize first letter), but do not capitalize
        section or table if not followed by a number
     d) Use the URL package for URLs (~ missing in [17]); check formatting of
        references [3, 5] as well
     e) Be careful with whitespace (use math-mode in 2.2, add a space before '(SVM)'
        at the end of section 4, etc.)
     f) English:
        - Introduction: which uses precomputed; method called NICETEXT;
          classify given text segments; normal texts. This method can accurately detect;
          detection method for NICETEXT, which; to classify text segments;
          may be translated twice to A1 and three times to A2; ... from different
          translators, a high-frequency word in the cover text thus has a
          reasonable chance of becoming two low-frequency words in the stegotext.
          Then new paragraph.  an SVM classifier; A series of experiments is given;
        - TBS: LiT. ; The encoder then selects the sentence; allows the sender to not
          transmit; bits correspond to the;  a hidden message, he first;
        - SATBS: So in a German cover text which; 1000 differnt types of texts;
          from 1 to 15.  The texts; by the Google, Systran and Prompt machine;
          words for each frequency; given texts into stegotexts; we need to expand;
          A word which is translated; translators and always yields the same result;
          a word is a one-to-one; Words which are translated; and generate different
          results are called one-to-many-words; and the stegotext, if we delete;
          stegotext (after deleting...; We find the word; greater than it is in;
          frequency differences between; also 2-gram (two adjacent words) frequency
          differences. Deleting one-to-one 2-grams ...; one-to-one words) from;
          stegotext also expands the; exists significant n-gram frequency;
        - FG: Our detection schema is an instance of two-class pattern recognition.
          A given text; can be formalized to; text, we delete (space!);
          following word, we; into a collection of; from the collection.  the refined
          collection as:; we extracted are defined; word types that appear in
        - Experiments: translators.  The texts; using the LiT prototype;
          (English) and German; from the Europarl; texts from the Europal; sentence.  Every sentence;
          we collected a total of 9784 one-to-one words and a total of 39638;
          text size increases.  when the text size; represents data; not need to be repeated
          in each subsequent; texts. Translated texts; our method is accurate at classifying;
        - Conclusion: we presented; semantically coherent; Also: "normal texts" (normal =
          untranslated? nobody cares! what about machine-translated texts?);
          First sentence after (4) is also irrelevant for your conclusion.
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