ID 原文 译文
20485 提出公平议价机制，避免参与人自私决策而导致议价失败。 Then a fairbargaining mechanism is put forward to avoid selfish decisions by players and lead to bargaining failures.
20486 实验表明，MOVNE-NB算法不仅能产生一个帕累托有效解，且实现了请求接受率和节能的公平折中。 Experiments show that the MOVNE-NB algorithm can not only produce a Pareto efficient solution, but also achieve a fair tradeoff between request acceptance rate and energy saving.
20487 针对现有兴趣点推荐的初始化和忽视评论信息语义上下文信息的问题，将深度学习融入推荐系统中已经成为兴趣点推荐研究的热点之一。 When modeling user preferences, the current researches of group recommendation ignore the problem of modeling initialization and the review information accompanied with rating information for recommender models, integrating deep learning into the recommendation system becomes a hotspot of Point-Of-Interest(POI) recommendation.
20488 该文提出一种基于深度学习的混合兴趣点推荐模型(MFM-HNN)。 In this paper, a new POI recommendation model called Matrix Factorization Model integrated with Hybrid Neural Networks (MFM-HNN) is proposed.
20489 该模型基于神经网络融合评论信息与用户签到信息来提高兴趣点推荐的性能。 The model improves the performance ofPOI recommendation by fusing review text and check-in information based on Neural Network (NN).
20490 具体地，利用卷积神经网络学习评论信息的特征表示，利用降噪自动编码对用户签到信息进行初始化。 Specifically, the convolutional neural network is used to learn the feature representation of the review text and the check-in information is initialized by using the stacked denoising autoencoder.
20491 进而，基于扩展的矩阵分解模型融合评论信息特征和用户签到信息的初始值进行兴趣点推荐。 Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the check-in information for POI recommendation.
20492 在真实签到数据集上进行实验，结果表明所提MFM-HNN模型相比其他先进的兴趣点推荐具有更好的推荐性能。 As is shown in the experimental results on real datasets, the proposed MFM-HNN achieves better recommendation performances than the other state-of-the-art POI recommendation algorithms.
20493 针对复杂战场环境下无人机(UAV)蜂群的渗透侦察任务，考虑无人机间信息传输时能量消耗的不均匀性，影响侦察任务的高效实施，该文提出一种基于无线紫外光隐秘通信的侦察UAV蜂群分簇算法。 Focusing on the reconnaissance mission of Unmanned Aerial Vehicle (UAV) swarm under complex battlefield environment, the non-uniform energy consumption during the information transmission between UAVs affects the efficient implementation of the reconnaissance mission, thus a cluster-based algorithm of reconnaissance UAV swarm based on wireless ultraviolet secret communication is proposed.
20494 该算法结合无线紫外光散射通信的优点，通过分簇拓扑管理机制对UAV蜂群进行能耗平衡。 Combined the advantages of wireless ultraviolet scattering communication, this algorithm uses cluster topology management mechanism to balance the energy consumption of UAV swarm.