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SM9-IBS is an industry standard for identity-based signature (IBS) algorithms issued by China in 2016. Although the IBS algorithms can be reduce the complexity of management of user keys, they have the problem of key revocation. In addition, the existing technologies are not fully applicable to SM9-IBS due to its special algebraic structure of users’ secret keys. Therefore, this paper proposes an efficient revocable SM9 identity-based signature (shorted as CS-SM9-RIBS) algorithm, which can quickly revoke and update the user’s signature authority. The algorithm introduces a complete subtree, which is used by the key generation center (KGC) generates temporary signature keys for each legitimate user, so that only the signature generated by this key can pass the signature verification. In terms of security, the new algorithm is proven to be existentially unforgeable under adaptive chosen message and identity attacks in the random oracle model. In terms of efficiency, when the number of users in the system is large and the number of revoked users is small in the key update stage, the time cost of the KGC to update the user’s signature key is much smaller than Boneh et al. ’s update technology. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the lack of accurate and efficient off-topic detection algorithm for the current English composition teaching system in China, this paper proposed an off-topic detection algorithm based on LDA and word2vec. The algorithm used LDA to model the documents and trained it with word2vec, with obtained semantic relation between document’s topic and words, calculated the probability weighted sum of each topic and its feature words in the document. Finally, by setting reasonable threshold, it selected the off-topic essays. According to the different F values for the different number of topics in the document, it determined the optimum number of topics in the experiment. The experimental results show that, compared to traditional vector space model, the proposed method can detect more off-topic essays with higher accuracy, and the F value is above 89%, which realizes the intelligent processing of off-topic essays detection, and may applies effectively in English essays teaching. [ABSTRACT FROM AUTHOR]