ID 原文 译文
1143 以三进制为例，设计实现了 2Bin 三进制图像数字水印算法。 and then, a concret ternary digital watermarking as an example of the M-ary system is further implemented. I
1144 在设计的算法中，根据嵌入的水印信息容量和载体图像的大小调整算法嵌入阈值， n the de-signed algorithm, the embedding threshold is adjusted according to the embedded watermark information capacity and the sizeof the carrier image.
1145 在维持直方图相邻 2Bin 高低形状不变的情况下，确定修改像素数目以及分块策略，实现水印信息的嵌入和提取操作。 Under the condition of maintaining the high or low shape of the adjacent 2Bin histogram unchanged, themodified pixel number and the block strategy are determined to realize the embedding and extraction of watermark informa-tion.
1146 实验结果表明，该方法能够智能均衡载体图像质量和水印容量，同现有算法相比，大大提高了水印嵌入容量和水印嵌入后的图像质量，且能抵抗裁剪、旋转等传统的几何攻击和信号处理攻击。 Experimental results show that this method is able to balance the relationship between image quality and watermark capac-ity intelligently, and that it greatly improves embedding capacity and image quality compared to these existing algorithms, meanwhile equips with the ability to resist the traditional geometric attacks and common image processing operations.
1147 为解决现有算法对社交网络节点影响力计算准确度不高的问题，本文整合节点不同维度信息，综合考虑节点在多个主题社区上的主题分布向量，提出一种新的节点影响力计算模型。 To solve the accuracy problems of the existing algorithms in calculating the influence of social networknodes, by integrating different dimension information of nodes, and considering the topic distribution vector of nodes on mul-tiple communities, a new model is proposed.
1148 模型首先将主题相关性作为先验信息;然后利用混合隶属度随机块( Mixed Membership Stochastic Block) 模型表达节点间的交互关系，用主题模型学习主题内容; It first regards the correlation between topics as the prior information, then uses the mixed membership stochastic block ( MMSB) model to express the interaction among nodes, learns topic contents usingtopic model,
1149 最后结合全局拓扑关系迭代计算节点的全局影响力。 and finally, iteratively calculates the global influence of nodes with global topological relationship.
1150 本文选取社交网络数据，以 P@ N、MAP 等作为评价指标同现有主流算法进行比较。 We select da-ta from social networks, use P@ N, MAP, etc. , as the evaluation indicators, and compare the proposed algorithm with the ex-isting mainstream algorithms.
1151 实验结果显示，本文算法有效提升了影响力节点识别的准确度和排名的有效性。 The experimental results show that our algorithm significantly improves the identification accu-racy of influential nodes and the validity of ranking.
1152 针对能效提升、宏用户干扰减小的问题，该文研究了基于干扰效率最大的异构无线网络顽健资源分配算法。 To improve energy efficiency and reduce the interference to macro users ( MUs) , this paper studies robustresource allocation for interference efficiency ( IE) maximization in heterogeneous wireless networks.