Prisoner’s Dilemma Game with Cooperation-Defection Dominance Strategies on Correlational Multilayer Networks

As multilayer networks are widely applied in modern society, numerous studies have shown the impact of a multilayer network structure and the network nature on the proportion of cooperators in the network. In this paper, we use Barabási–Albert scale-free networks (BA) and Watts and Strogatz networks (WS) to build a multilayer network structure, and we propose a new strategy-updating rule called “cooperation-defection dominance”, which can be likened to dominant and recessive traits in biogenetics. With the newly constructed multilayer network structure and the strategy-updating rules, based on the simulation results, we find that in the BA-BA network, the cooperation dominance strategy can make the networks with different rs show a cooperative trend, while the defection dominance strategy only has an obvious effect on the network cooperation with a larger r. When the BA network is connected to the WS network, we find that the effect of strategy on the proportion of cooperators in the network decreases, and the main influencing factor is the structure of the network. In the three-layer network, the cooperation dominance strategy has a greater impact on the BA network, and the proportion of the cooperators is enhanced more than under the natural evolution strategy, but the promotion effect is still smaller than that of the two-layer BA network because of the WS network. Under the defection dominance strategy, the WS layer appears different from the first two strategies, and we conclude through simulation that when the payoff parameter is at the middle level, its cooperator proportion will be suppressed, and we deduce that the proportion of cooperators and defectors, as well as the payoff, play an important role.     

实测卫星频谱占用状态拟合与预测方法

由于在不同时间、不同空间卫星接收数据底噪是动态起伏的,传统建模固定门限的方法存在缺陷。本文在时间维度上对卫星频谱感知数据的频谱占用模型进行分析,利用自适应阈值法确定噪声门限,对卫星频谱数据进行预处理,得到卫星频谱占用长度序列。为对卫星频谱的态势进行有效的统计分析,利用泊松分布和指数分布方法对频谱占用时间长度序列的概率密度曲线进行拟合,得到了适用于卫星频谱占用时间序列的概率分布模型。基于所得的卫星频谱占用状态模型,通过两状态马尔可夫链计算出卫星信道某一频点的状态转移矩阵,从而预测出信道占用和空闲的概率。利用卫星频谱感知数据构建的数据集进行反向传播(BP)神经网络训练,预测某一频点的占用长度。通过计算BP神经网络与传统的长短期记忆(LSTM)神经网络预测法的均方根误差(RMSE),得到LSTM神经网络的RMSE为2.208 1,BP神经网络的RMSE为0.172 8。评估结果表明,BP神经网络准确度高。     

基于TDD的低轨卫星跳波束资源分配算法

低轨(LEO)卫星跳波束技术可以灵活分配系统资源,适用于业务分布不均匀的场景。时分双工(TDD)方式可以减少星载和地面终端设备的天线数量,有效降低其复杂度,并有利于开展上下行非对称业务。本文提出一种基于TDD的LEO卫星跳波束资源分配算法,在满足业务需求的基础上,以最小化时域资源消耗为目标,建立支持跳波束和多频时分多址接入(MF-TDMA)机制的LEO卫星反向链路资源分配模型;综合考虑星地动态时延补偿,采取一种多层次的跳波束时隙架构设计,以最大化可用时隙为目标,建立上下行时隙切换模型,并提出一种基于TDD的跳波束时隙排布优化方法。仿真结果表明,对比于传统的MF-TDMA资源分配方法或固定多波束均分算法,本文提出的算法能有效提高系统的时隙利用率和吞吐量。     

Adversarial Data Hiding in Digital Images

In recent studies of generative adversarial networks (GAN), researchers have attempted to combine adversarial perturbation with data hiding in order to protect the privacy and authenticity of the host image simultaneously. However, most of the studied approaches can only achieve adversarial perturbation through a visible watermark; the quality of the host image is low, and the concealment of data hiding cannot be achieved. In this work, we propose a true data hiding method with adversarial effect for generating high-quality covers. Based on GAN, the data hiding area is selected precisely by limiting the modification strength in order to preserve the fidelity of the image. We devise a genetic algorithm that can explore decision boundaries in an artificially constrained search space to improve the attack effect as well as construct aggressive covert adversarial samples by detecting “sensitive pixels” in ordinary samples to place discontinuous perturbations. The results reveal that the stego-image has good visual quality and attack effect. To the best of our knowledge, this is the first attempt to use covert data hiding to generate adversarial samples based on GAN.     

Conserved Control Path in Multilayer Networks

The determination of directed control paths in complex networks is important because control paths indicate the structure of the propagation of control signals through edges. A challenging problem is to identify them in complex networked systems characterized by different types of interactions that form multilayer networks. In this study, we describe a graph pattern called the conserved control path, which allows us to model a common control structure among different types of relations. We present a practical conserved control path detection method (CoPath), which is based on a maximum-weighted matching, to determine the paths that play the most consistent roles in controlling signal transmission in multilayer networks. As a pragmatic application, we demonstrate that the control paths detected in a multilayered pan-cancer network are statistically more consistent. Additionally, they lead to the effective identification of drug targets, thereby demonstrating their power in predicting key pathways that influence multiple cancers.     

An Improvement to Conformer-Based Model for High-Accuracy Speech Feature Extraction and Learning

Owing to the loss of effective information and incomplete feature extraction caused by the convolution and pooling operations in a convolution subsampling network, the accuracy and speed of current speech processing architectures based on the conformer model are influenced because the shallow features of speech signals are not completely extracted. To solve these problems, in this study, we researched a method that used a capsule network to improve the accuracy of feature extraction in a conformer-based model, and then, we proposed a new end-to-end model architecture for speech recognition. First, to improve the accuracy of speech feature extraction, a capsule network with a dynamic routing mechanism was introduced into the conformer model; thus, the structural information in speech was preserved, and it was input to the conformer blocks via sequestered vectors; the learning ability of the conformed-based model was significantly enhanced using dynamic weight updating. Second, a residual network was added to the capsule blocks, thus, the mapping ability of our model was improved and the training difficulty was reduced. Furthermore, the bi-transformer model was adopted in the decoding network to promote the consistency of the hypotheses in different directions through bidirectional modeling. Finally, the effectiveness and robustness of the proposed model were verified against different types of recognition models by performing multiple sets of experiments. The experimental results demonstrated that our speech recognition model achieved a lower word error rate without a language model because of the higher accuracy of speech feature extraction and learning using our model architecture with a capsule network. Furthermore, our model architecture benefited from the advantage of the capsule network and the conformer encoder, and also has potential for other speech-related applications.     

出境旅游网络关注度时空演变及影响因素研究——以泰国为例

网络关注度是测量潜在旅游者对目的地旅游关注情况及需求变化的重要手段之一。基于百度指数,以我国31个省（区、市）（不含港、澳、台）的泰国旅游网络关注度为研究对象,运用季节性强度指数、地理集中度指数、赫芬达尔-赫希曼指数、地理探测器等方法,探讨我国居民对泰国旅游网络关注度的时空演化规律及其影响因素。结果表明：从时序演化上看,2011—2019年泰国旅游网络关注度呈波动上升态势,可划分为快速上升期和平稳发展期2个阶段,地区季节性差异显著,3月、7月、12月为泰国旅游网络关注度的高峰时段;从空间分异上看,泰国旅游网络关注度空间分异变化不大,空间集聚趋于分散状态,整体呈“东高-西低”的阶梯状递减特征,高关注度地区主要集中在东部地区及四川省,低关注度地区则主要分布于除四川省外的西部省份;从影响因素上看,经济发展水平（人均可支配收入、GDP）、交通便利程度、贸易开放度以及国际旅游开放度共同影响泰国旅游网络关注度的空间分布格局。     

沈阳机床技改项目弱电智能化设计

沈阳机床（集团）有限责任公司的弱电智能化设计成功地将智能卡应用系统与MES有效的集成,为国内首创。生产物流条码化管理使其物流管理水平走在了国内机械行业的前列。系统采用了优化的制冷机群控、冷却塔群控技术,并在保持冷冻水大温差的条件下采用了变冷冻水温度的节能环保设计。全网冗余设计、弹性架构并融合安全的计算机网络充分保证厂区网络高可靠性、可扩展性和安全性。设计充分体现了信息化融合工业化的数字化工厂理念。     

用单端矢量网络分析仪测试手机RF电路的差分散射参数

本文以声表的测试为例,说明了采用单端矢量网络分析仪测试手机射频(RF)电路的差分散射参数的原理及方法,其中包含物理三端口的散射参数和模式三端口网络散射参数。     

计算机网络工程实验优化探索

实验教学是计算机专业教学活动中的一个重要环节,现如今实验教学环节中存在着实验设备滞后、实验内容单一、学生实验积极性没有充分调动等众多问题。从以上三方面展开《计算机网络工程》课程的实验教学研究,提出将网络仿真工具应用于计算机网络实验教学、强调学生在实验中的主体作用、将实验项目模块化、搭建虚拟网络实验平台等多种优化策略。实践证明这些优化方案在网路工程实验环节中取得了良好效果。