ID 原文 译文
18235 该文从理论和应用角度，评估该类型协议共享密钥建立过程中的部分信息泄漏对安全性的威胁至关重要。 Hence, it is important to evaluate the threat aroused by the partial information leakage during the establishment of shared keys.
18236 基于隐藏数问题和格分析技术，该文讨论了椭圆曲线Diffie-Hellman密钥交换协议的比特安全性，启发式地证明了椭圆曲线Diffie-Hellman共享密钥的x坐标的中间11/12 bit的计算困难性近似于恢复整个密钥。 In this paper,the bit security of elliptic curve Diffie-Hellman with knowledge of partial inner bits based on the combination of hidden number problem and lattice-based cryptanalysis technique is recisited. 11/12 of the inner bits of thex-coordinate of the elliptic curve Diffie-Hellman key are approximately as hard to compute as the entire key.
18237 进一步地，给出了信息泄露量与泄漏位置的显式关系式。 Moreover, the explicit relationship between the leakage fraction and the leakage position is elaborated.
18238 该文的研究结果放松了对泄露比特位置的限制，更加符合应用场景，显著改进了以往工作中得出的结论。 This result which relaxes the restriction on the location of leakage position dramatically improves the trivial one which stemmed from prior work.
18239 针对区间型不确定数据的特点，该文提出一种改进的模糊C均值聚类算法(IU-IFCM)。 An Improved Fuzzy C-Means clustering algorithm (IU-IFCM) is proposed in this study in accordancewith the characteristics of Interval Uncertain data.
18240 首先对区间型数据进行特征变换，由p维特征映射成由2p维特征组成的实数据， First, the interval data is transformed into real datacomposed of 2p dimension feature, which is mapped from that of p dimension feature.
18241 然后考虑区间中值与区间大小关系，设计一种样本距离计算方法，通过模糊C均值实现对区间型样本聚类。 Second, a method forcalculating sample distance, which realizes the interval sample clustering by fuzzy c-mean algorithm, is designedwhile considering the relationship between interval median value and interval size.
18242 理论分析与对比实验表明，该算法的划分系数(PC)及正确等级(CR)值比其它方法平均提高10%以上， Theoretical analysis andcomparison experiments show that the presented algorithm surpaes the compared algorithms by more than 10%on average in terms of the Partition Coefficient (PC) and Correct Rank(CR) value.
18243 表明有更好的聚类精度，对当前大数据环境下不确定数据的分类提供了一种新的解决方案。 These results indicate that the algorithm presents in this study has better clustering accuracy and provides a new solution for the classification of uncertain data in current big data environments.
18244 当前，应用软件面临的重要问题是不法分子通过软件剽窃、重打包等技术，将恶意负载或广告加载到合法应用软件中，并形成新软件进行发布，给用户和应用软件作者的合法权益带来威胁。 Currently, a main problem in software is repackaging or plagiarization, which means attackers canadd malicious payloads or advertisements into legitimate APPs through piggybacking, it greatly threatens theusers and original developers.