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向细心的2010Information Hiding Conference匿名评审人致敬 已有12人参与
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看看人家的评审意见多么的细心（仅一条），只要对着一条条的改正就可以，人家不是指出来错误，而且是给出来一个更好的表述方式，开始感觉这么多错误，怎么改啊，因为鄙人英语很不好，原来人家给出的是正确的表述方式。 我们很多人也都是论文审稿人，我们什么时候向人家这么仔细。 无论做学问还是做事情，怕就怕在较真上。美国能在科学技术上有今天的成就，我想和做事的态度也很有关系。相反，我们有些时候是不是有些太浮躁了，感觉文章写好了，多检查一遍有时都嫌太耽误时间。多向人家学习学习做事做研究的态度吧！！ 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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