ID	原文	译文
57238	针对信息中心移动自组网场景中节点间间歇连接和网内泛在缓存的特点，提出一种基于门限秘密共享机制的数据访问控制机制.	Aiming at the characteristics of intermittent connections between nodes and ubiquitous caching in the information-centric mobile Ad hoc networks，a data access control scheme based on threshold se- cret sharing scheme is proposed.
57239	通过构建辅助密钥块，降低了消费者解密的开销和网络节点的存储资源消耗.	By constructing an auxiliary key block，the consumer＇s decryption over- head and the storage resource consumption of network nodes are reduced.
57240	同时，通过引入双变量单向函数，保障了消费者子秘密份额的唯一性，减少了消费者侧秘密份额管理所带来的空间开销.	At the same time，by combi- ning a two-variable one-way function，the uniqueness of the consumer＇s shares is guaranteed，and the cost of key management is decreased.
57241	仿真和理论分析结果表明，该机制显著降低了消费者侧的解密开销，良好地适应了信息中心移动自组织网络场景.	Simulations show that this mechanism dramatically reduces the decryp- tion overhead on the consumer side，which indicates its well suited for information-centric mobile Ad hoc network scenarios.
57242	针对命名数据网待定兴趣转发表中高效的变长名称数据索引、硬件可支持的存储消耗以及兴趣包泛洪攻击检测等问题，提出了基于字符卷积神经网络的认知索引模型( C＆I) ，该模型能够支持路由名称数据的分类、聚合，降低名称数据的存储消耗.	In order to solve the problems of efficient variable-length name lookup，hardware-supportable storage consumption，and detection of interest flooding attack in the pending interest table ( PIT) of named data networking，an cognition and indexing model( C＆I) based on character convolutional neural network is proposed. C＆I can support the classification and aggregation of name data，and reduce the storage consumption of name data.
57243	同时，基于 C＆I 提出了支持兴趣包泛洪攻击检测的待定兴趣转发表( PIT) 存储结构 C＆I- PIT 及其数据检索算法，通过多级存储器部署方式，分别在片上和片下的存储器中部署索引结构及存储空间.	At the same time，a pending interest table storage structure C＆I-PIT based on C＆I and its data retrieval algorithm，which supports the detection of interest flooding attack，is proposed. Through the deployment of multi-level memory，the index structure and storage space are re- spectively deployed on static random access memory and dynamic random access memory.
57244	实验结果表明，C＆I-PIT 在名称数据聚合、存储消耗、泛洪攻击检测等方面具有良好的性能.	Experiments show that C＆I-PIT has good performance in name aggregation，memory consumption and interest flooding attack detection.
57245	为了提高噪音环境下城市音频分类系统的鲁棒性，提出了一种双特征 2 阶密集卷积神经网络( D-2-DenseNet) 噪音鲁棒的城市音频分类模型.	A noise robust urban sound event classification model based on 2-order dense convolutionalnetwork using dual features( D-2-DenseNet) is proposed，which aims at the problems of insufficient ro?bustness of current models.
57246	首先介绍了噪音添加和噪音鲁棒处理，阐述了一种双特征互补偿的算法;	Firstly，the brief introduction of the method of noise adding and robust pro?cessing is presented.
57247	然后结合 2 阶密集卷积神经网络与自适应机制提出了一种噪音鲁棒音频分类模型:	Moreover，a dual feature mutual compensation algorithm and 2-order dense convolu?tional network is presented.