ID 原文 译文
4874 最后，在时变兴趣社区的基础上，设计新的查询消息路由算法。 Next, a dynamic query routing on theconstructed time-variant interest communities was proposed.
4875 理论分析结果表明，所提算法时间复杂度是 ( log ) O n n ； Theoretical analysis shows that the proposed routing can runin  ( log ) O n n time.
4876 仿真实验结果表明，所提算法在查询成功率、平均查询时延、查询跳数及系统开销方面均优于与同类型算法。 The comparisons between the proposed routing and state-of-the-art message delivery algorithmsshow that the proposed routing can keep high query success rate, reduce the average query latency and the hop count of aquery and maintain low system overhead.
4877 路由与波长分配是全光网络重要的资源分配方法。 Routing and wave length assignment is an important resource allocation method of all-optical network.
4878 针对传统方法与新架构结合的问题，提出了一种基于SDN 的自适应多目标路由与波长分配方法，能够通过自我调节的方式实现全光网络的链路资源调配。 Aiming at the problem of traditional method combined with the new architecture, an adaptive multi-objective routing and wave-length assignment method based on SDN was proposed, which could realize the allocation of link resources of all-opticalnetwork through self-regulation.
4879 该方法基于SDN 服务功能链模式，以调度时间和链路质量为调度目标，将路由与波长分配问题构建为 0-1 整数规划问题， Based on the SDN service function chain model, service scheduling time and link ser-vice quality were taken as the scheduling objective, routing and wavelength assignment problem was constructed as the0-1 integer programming problem,
4880 同时采用二进制混合拓扑粒子群算法对该模型求解实现网络资源的优化调度。 meanwhile, binary hybrid topology particle swarm optimization algorithm was used tooptimize the network resources for optimal scheduling.
4881 仿真实验结果表明，所提方法在恢复时间、阻塞率、资源利用率等指标的测试中均优于传统经典算法的性能。 The simulation results show that the proposed method is superiorto the traditional classical algorithms in the test of recovery time, blocking rate and resource utilization.
4882 为解决现有的隐私保护方法不能很好地满足群组推荐中用户的个性化隐私需求的问题，提出了一种面向群组推荐的基于可信客户端的个性化隐私保护框架及基于此框架的群组敏感偏好保护方法。 To address the problem that most of the existing privacy protection methods can not satisfy the user's persona-lized requirements very well in group recommendation, a user personalized privacy protection framework based ontrusted client for group recommendation (UPPPF-TC-GR) followed with a group sensitive preference protection method(GSPPM) was proposed.
4883 所提方法在可信客户端收集群组内用户的历史数据以及隐私偏好需求，利用用户敏感主题相似性发现组内相似用户，通过对前 k 个用户进行随机的协同扰动，实现群组内用户的个性化隐私保护。 In GSPPM, user's historical data and privacy preference demands were collected in the trustedclient, and similar users were selected in the group based on sensitive topic similarity between users. Privacy protectionfor users who had privacy preferences in the group was realized by randomization of cooperative disturbance to top ksimilar users.