ID	原文	译文
45866	仿真结果表明，算法 GBP 能够有效地解决城市环境车联网中的区域覆盖问题。	Simulation results show that GBP can efficiently solve the coverage problem in urban VANET.
45867	针对现有分布式计算环境下随机梯度下降算法存在效率性与私密性矛盾的问题，提出一种 MapReduce框架下满足差分隐私的随机梯度下降算法。	Aiming at the contradiction between the efficiency and privacy of stochastic gradient descent algorithm in dis-tributed computing environment, a stochastic gradient descent algorithm preserving differential privacy based on MapReduce was proposed.
45868	该算法基于 MapReduce 框架，将数据随机分配到各个 Map 节点并启动 Map 分任务独立并行执行随机梯度下降算法；	Based on the computing framework of MapReduce, the data were allocated randomly to each Map node and the Map tasks were started independently to execute the stochastic gradient descent algorithm.
45869	启动 Reduce 分任务合并满足更新要求的分目标更新模型，并加入拉普拉斯随机噪声实现差分隐私保护。	The Reduce tasks were appointed to update the model when the sub-target update models were meeting the update requirements, and to add Laplace random noise to achieve differential privacy protection.
45870	根据差分隐私保护原理，证明了算法满足  -差分隐私保护要求。	Based on the combinatorial features of differential privacy, the results of the algorithm is proved to be able to fulfill ε-differentially private.
45871	实验表明该算法具有明显的效率优势并有较好的数据可用性。	The experimental results show that the algorithm has obvious efficiency advantage and good data availability.
45872	针对多云环境下带截止日期约束的科学工作流调度问题，提出一种基于遗传算法操作的自适应离散粒子群优化算法（ADPSOGA），目的是在尽可能满足工作流截止日期前提下，减少其执行代价。	In view of the deadline-constrained scientific workflow scheduling on multi-cloud, an adaptive discrete particle swarm optimization with genetic algorithm (ADPSOGA) was proposed, which aimed to minimize the execution cost of workflow while meeting its deadline constrains.
45873	该方法考虑多云之间的通信代价、虚拟机的启动和关闭时间以及多云之间不同的带宽通信波动；	Firstly, the data transfer cost, the shutdown and boot time of virtual machines, and the bandwidth fluctuations among different cloud providers were considered by this method.
45874	为了避免传统粒子群优化算法（PSO,particle swarm optimization）存在的过早收敛问题，引入遗传算法的随机两点交叉操作和随机单点变异操作，	Secondly, in order to avoid the premature convergence of traditional particle swarm optimization (PSO), the randomly two-point crossover operator and randomly one-point mutation operator of the genetic algorithm (GA) was introduced.
45875	有效提高种群进化过程中的多样性；	It could effectively improve the diversity of the population in the process of evolution.