ID	原文	译文
39996	SimRank方法是一种基于图的拓扑结构信息来衡量任意两个对象间相似程度的方法，针对在真实的大规模社交网络中节点与节点之间的迭代计算过程需要消耗大量的时间，	SimRank is a method based on the topological structure information of the graph to measure the similarity between any two objects.However, in real large-scale social networks, the iterative computation between nodes is time-consuming.
39997	提出了一种基于SimRank全局矩阵平滑收敛的网络社区发现方法（SimRank global smooth convergence，SGSC）。	Here we propose a hierarchical community detection algorithm based on global matrix smooth convergence using SimRank, called SGSC.
39998	首先，该算法通过经典度量来识别网络中的初始核心节点；	First, the SGSC algorithm identifies the initial core nodes in a network by classical measurement.
39999	然后利用矩阵平滑收敛来计算SimRank得到最终核心节点；	Then, it smoothly converges a matrix to calculate SimRank to obtain original core nodes.
40000	最后，基于全局收敛矩阵，将社区聚集在核心节点周围，使用Closeness指数合并两个社区，	Based on the global convergence matrix, we cluster the communities around the core nodes and use a closeness index to merge two communities.
40001	通过递归的重复该过程，聚类出最终社区。	By recursively repeating the process, a dendrogram of the communities is eventually constructed.
40002	在3种真实的不同规模的社交网络中将SGSC和其他2种具有代表性的方法进行比较，并验证了提出的算法在不同规模的社交网络中社区划分的准确率和算法运行的时间性能上有所提升。	We validate the performance of SGSC by comparing its results with those of two representative methods for three real-world networks with different scales, and comparison results show that the proposed SGSC algorithm improves the accuracy in community division and reduces running time in social networks of different scales.
40003	三维人体目标检测在智能安防、机器人、自动驾驶等领域具有重要的应用价值。	Three-dimensional(3-D)human target detection has important application value in intelligent security, robot, automatic driving and other fields.
40004	目前基于雷达与图像数据融合的三维人体目标检测方法主要采用两阶段网络结构，分别完成目标概率较高的候选边界框的选取以及对目标候选框进行分类和边界框回归。	At present, the 3-D human target detection method based on radar and image data fusion mainly adopts two-stage network structure, which respectively completes the selection of candidate boundary boxes with high target probability and the target classification/regression of target candidate boxes.
40005	目标候选边界框的预先选取使两阶段网络结构的检测准确率和定位精度得到提高，但相对复杂的网络结构导致运算速度受到限制，难以满足实时性要求较高的应用场景。	Although the preselection of target candidate bounding box enables the two-stage network structure to achieve higher detection accuracy and positioning accuracy, the complexity of the network structure leads to the limitation of the operation speed, which cannot be applied in scenarios with high real-time requirements.