ID	原文	译文
3443	利用图着色模型为卸载用户分配信道；	The graph coloring model was used to allocate channels to the offloading users.
3444	采用拉格朗日乘子法对功率与计算资源进行分配。	Lagrange multiplier method wasused to allocate power and computing resources.
3445	仿真结果表明，在不同的系统参数下，所提机制可以有效降低系统开销，减少任务完成时延。	The simulation results show that the proposed mechanism can effec-tively reduce system overhead and reduce task completion delay under different system parameters.
3446	针对出租车盲目寻客导致空载率高的问题，提出了一种出租车载客热点推荐策略，以最大程度优化匹配乘客过程，提高寻客效率。	To solve the problems of unloading rate caused by blind passenger search of taxis, the hotspot recommendation strategy of taxi passengers was proposed. The proposed strategy could optimize the process of matching passengers to the greatest extent to increase the efficiency of passenger search.
3447	基于出租车历史轨迹数据，结合热点乘客信息的时间序列特性，提出基于循环神经网络的分段预测（SPBR）算法，以及基于分时马尔可夫决策过程（TMDP）的载客推荐模型。	Based on the historical trajectory data of taxis and the timeseries characteristics of hotspot passenger information, a segment prediction method was proposed based on recurrentneural network (SPBR) and a passenger recommendation model was proposed based on time-varying Markov decisionprocess (TMDP).
3448	实验表明，SPBR 算法预测结果的 RMSE 比 SVR、CART 和 BPNN 等算法分别降低了 67.6%、71.1%和 64.5%；	Experimental results show that the RMSE predicted by SPBR algorithm is 67.6%, 71.1% and 64.5%lower than the SVR, CART and BPNN algorithms.
3449	TMDP 模型出租车期望回报比历史期望提升了 35.9%。	The expected return of taxis based on the TMDP algorithm is 35.9%higher than historical expectations.
3450	传统的集中式数据交易模式不能很好地适用于当前万物互联、数据实时产生的智能时代，为了使产生的数据发挥更大的价值，设计一种有效的数据交易框架至关重要。	The traditional centralized data trading models are not well applicable to the current intelligent era where eve-rything is interconnected and real-time data is generated, and in order to maximize the use of collected data, it is essentialto design an effective data trading framework.
3451	为此，提出了一种基于联盟区块链的分布式数据交易框架，在不依赖第三方的情况下实现了 P2P 的数据交易。	Therefore, a distributed data trading framework based on consortiumblockchain was proposed, which realized P2P data trading without relying on a third party.
3452	针对已有数据交易模型仅考虑数据本身的因素，而忽略用户任务相关因素的问题，基于数据质量、数据属性、属性的相关性、消费者竞争等多维因素构建了双层多目标优化模型，以优化数据提供者（DP）和数据消费者（DC）的效用。	Aiming at the problem thatexisting data trading models only consider the factors of the data itself and ignore the factors related to user tasks, abi-level multi-objective optimization model was constructed based on multi-dimensional factors, such as data quality, da-ta attributes, attribute relevance and consumer competition, to optimize the utilities of data provider (DP) and data con-sumer (DC).