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mpustc銀蟲(chóng) (初入文壇)
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[交流]
向細(xì)心的2010Information Hiding Conference匿名評(píng)審人致敬 已有12人參與
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看看人家的評(píng)審意見(jiàn)多么的細(xì)心（僅一條），只要對(duì)著一條條的改正就可以，人家不是指出來(lái)錯(cuò)誤，而且是給出來(lái)一個(gè)更好的表述方式，開(kāi)始感覺(jué)這么多錯(cuò)誤，怎么改啊，因?yàn)楸扇擞⒄Z(yǔ)很不好，原來(lái)人家給出的是正確的表述方式。 我們很多人也都是論文審稿人，我們什么時(shí)候向人家這么仔細(xì)。 無(wú)論做學(xué)問(wèn)還是做事情，怕就怕在較真上。美國(guó)能在科學(xué)技術(shù)上有今天的成就，我想和做事的態(tài)度也很有關(guān)系。相反，我們有些時(shí)候是不是有些太浮躁了，感覺(jué)文章寫(xiě)好了，多檢查一遍有時(shí)都嫌太耽誤時(shí)間。多向人家學(xué)習(xí)學(xué)習(xí)做事做研究的態(tài)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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小木蟲(chóng)(金幣+0.5):給個(gè)紅包，謝謝回帖交流
小木蟲(chóng)(金幣+0.5):給個(gè)紅包，謝謝回帖交流
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1。發(fā)錯(cuò)地方了，應(yīng)該發(fā)到會(huì)議版。 2。好的會(huì)議都有審稿意見(jiàn)，有的比較多。看看這個(gè)貼，鼓勵(lì)審稿意見(jiàn)的交流，大家也可以學(xué)學(xué)如何審稿， http://www.gaoyang168.com/bbs/viewthread.php?tid=1444722 |
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