The present research reports on the use of data mining techniques for differentiating between translated and non-translated original Chinese based on monolingual comparable corpora. We operationalized seven entropy-based metrics including character, wordform unigram, wordform bigram and wordform trigram, POS (Part-of-speech) unigram, POS bigram and POS trigram entropy from two balanced Chinese comparable corpora (translated vs non-translated) for data mining and analysis. We then applied four data mining techniques including Support Vector Machines (SVMs), Linear discriminant analysis (LDA), Random Forest (RF) and Multilayer Perceptron (MLP) to distinguish translated Chinese from original Chinese based on these seven features. Our results show that SVMs is the most robust and effective classifier, yielding an AUC of 90.5% and an accuracy rate of 84.3%. Our results have affirmed the hypothesis that translational language is categorically different from original language. Our research demonstrates that combining information-theoretic indicator of Shannon’s entropy together with machine learning techniques can provide a novel approach for studying translation as a unique communicative activity. This study has yielded new insights for corpus-based studies on the translationese phenomenon in the field of translation studies.
Two-party collaborative signature scheme is an important cryptographic technology for user authentication and data integrity protection when using mobile devices for financial and securities transactions. However, the two-party collaboration scheme has the following shortcomings: firstly, it is not flexible enough, and it requires the collaborating parties to be secure and trusted; secondly, the two-party collaboration security still needs to be improved. Once a hacker obtains the signature private key and collaborative identity of a mobile device, it can construct a legitimate two-party collaborative signature. Third, the application scenario of two-party co-signature is limited and cannot meet the application scenario of multi-device co-signature. For this reason, this paper designs a multi-party collaborative signature scheme based on SM2 digital signature algorithm in the standard “SM2 Elliptic Curve Public Key Cryptography” of GM/T003-2012. This scheme consists of multiple (more than 2) participants to jointly generate the signature group public key and valid signature in an interactive manner, while ensuring that each user cannot know the signature key other than their own during the signing process. We implement this scheme based on the GMP library. The experimental results show that this scheme is not only flexible but also more secure and trustworthy to meet the application scenario of multi-device collaborative signing. In addition, the time for multiple participants to construct signatures in this scheme is similar, and the time for signature verification is less different from that of the original SM2 signature.
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