ID 原文 译文
23495 最后，基于攻击概貌检测结果，通过构造指示函数排除攻击概貌在推荐过程中产生的影响，并引入矩阵分解技术设计相应的鲁棒协同推荐算法。 Finally, an indicator function is constructed based on the attack detection results to reduce the influence of attack profiles on the recommendation, and it is combined with the matrix factorization technology to devise the corresponding robust collaborative recommendation algorithm.
23496 实验结果表明，与现有的基于矩阵分解模型的推荐算法相比，所提算法不但具有很好的鲁棒性，而且准确性也有提高。 Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.
23497 在采用偏好 NSGAII 算法求解多子区域重点覆盖的短波网络频率优化指配时，针对算法中非支配排序耗时较多的问题，该文提出一种偏好排序淘汰的 NSGAII 算法。 This paper proposes a preference ranking elimination NSGAII algorithm to deal with the time-consuming issue of the preference NSGAII algorithm in optimizing HF network frequency assignment in multi-areas outstanding coverage.
23498 在进行非支配排序前，根据解的偏好评价排序结果淘汰一部分偏好评价较差的解，减少参与非支配排序的解的数量从而减少求解时间，同时降低偏好评价结果较差的个体解被选中进行交叉、变异的概率，提高算法的求解效率和求解效果。 The proposed algorithm sorts and eliminates solutions according to their preference evaluation priori to the non-dominate sorting. By eliminating solutions with low ranking, the number of solutions participates in non-dominate sorting is reduced. The calculation time and the probability of selecting low ranking individuals for crossover or mutation are both decreased.
23499 在进行的 48 组数据测试中，该文算法在其中 38 组决策解偏好评价结果和求解时间同时最优，相同迭代次数时相比偏好 NSGAII 算法节省 27%的求解时间。 The proposed algorithm simultaneously achieves the best performance and least calculation time in 38 of 48 sets experiments. Constrained with the same iteration number, the proposed algorithm saves 27% of computation time against the preference NSGAII algorithm.
23500 结果表明通过偏好排序淘汰机制的引入，更好利用了偏好信息，使算法用较少的时间求得更好的偏好解。 Experimental results show that by adopting preference evaluation sorting, the proposed algorithm takes less time and obtains a better solution.
23501 为了实现网络资源的动态分配，提高网络资源利用率，满足用户业务多样性带来的切片网络差异需求，该文提出一种基于网络效用最大化的虚拟资源分配算法。 To realize the dynamic allocation of network resources, improve the network resources utilization and meet the demand of the diverse networks, this paper proposes a virtual resource allocation algorithm based on network utility maximization.
23502 该算法采用商业化模式将频谱资源作为收益载体，并对不同切片网络进行差异化定价。 The spectrum resource is used as the revenue and the differentiated price is commercialized according to slicing networks.
23503 同时将计算资源和回程链路作为开销，还考虑了切片网络对计算资源和频谱资源的差异性需求，最后以最大化网络收益建立效用模型。 It also takes the computing resources and the backhaul as the cost, and also takes into account the different demands of the slicing network on the computing resources and spectrum resources. Finally, the utility model is established to maximize the network revenue.
23504 并通过拉格朗日对偶分解设计了分布式迭代算法对效用模型进行求解。 A distributed iterative algorithm is designed to solve the utility model by Lagrangian dual decomposition.