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。     

基于G/G/1排队模型的802.11 DCF延迟分析

针对现有802.11 DCF分析模型在进行退避机制分析时不考虑周围节点状态的不足,提出了一种非饱和情况下IEEE 802.11 DCF协议的性能分析方法,推导出了以802.11 DCF为媒体访问控制(MAC)层协议的单跳无线网络中,MAC层的分组延迟及其抖动表达式,并分析了不同分组到达速率、节点数和退避窗口大小对MAC层分组冲突概率、延迟及其抖动的影响.仿真结果表明:MAC层的分组冲突概率、延迟及其抖动随着分组到达速率、节点数的增加而增加;以冲突概率的增加为代价,减小最小退避窗口可以减小MAC层的分组延迟及其抖动.     

一种新型的多址接入中继协作方案

现有多址中继协作方案的编码增益有限且用户之间不能相互辅助,为此,文中提出了一种结合网络与信道编码的多址中继协作方案:在中继端利用网络编码将不同用户的编码冗余同时转发,目的端通过对某个用户信息的正确译码恢复出其他用户的编码冗余.通过对译码错误事件概率的分析,推导了该方案的中断率与误码率近似值表达式.仿真结果表明,随着信噪比的提升,文中方案可获得比编码协作更好的中断率和误码率性能,同时信噪比较高的用户还可辅助提升信噪比较低的用户的性能.     

网络监督视角下的廉政建设研究

网络监督的发展给中国的廉政建设带来新的机遇和挑战。通过对网络监督的概念、特征的研究,可以看出网络监督对廉政建设的正负效应。这对于加强党的建设具有重要的意义。     

基于时频域融合和ECA-1DCNN的航空串联故障电弧检测

为了快速准确地检测航空交流线路中出现的串联故障电弧,提出了一种基于时频域融合和加入高效注意力机制(efficient channel attention, ECA)的一维卷积神经网络(one-dimensional convolutional neural network, 1DCNN)的故障检测算法。首先,搭建航空交流电弧故障实验平台,负载选择多类型、多参数值进行电流信号的采集;其次,为了保留更多的故障信息,分析其特征频段,经过大量数据验证,航空串联电弧在发生时,1 000～4 000 Hz分量具有一定的占比,因此将原始信号与特征频段进行融合,融合后的一维数据作为模型输入;最后,搭建ECA-1DCNN检测模型,进行训练,并通过K折交叉验证模型的有效性,得到测试集平均准确率为97.96%。该方法网络层数较少,计算快速,避免了复杂时频域计算过程,较为智能,对航空串联电弧检测装置的研究提供了理论参考。     

大规模复杂换热网络联动进化障碍分析及策略改进

针对启发式算法在优化大规模换热网络时出现优化精细度不足的问题,提出一种以小负荷公用工程为导向的换热单元耦合联动强制进化策略。该策略以小负荷公用工程作为导向,构建具有耦合关系的换热单元环路,并对环路内的换热单元热负荷进行联动调整,同时保持环路外换热单元的热负荷和流股匹配不变。该策略旨在最大程度回收流股的能量,以减少小负荷公用工程使用带来的费用增加。将策略应用于强制进化随机游走算法并对两个算例进行测试,算例H8C7和H10C10的优化结果分别为1495292 $/a和1713637 $/a。与改进前相比,分别降低了7466 $/a和7533 $/a;与文献中最优解相比,分别减少了2033 $/a和1450 $/a,证明了该策略的有效性。     

基于SnowNLP的微博网络舆情分析系统

随着微博、抖音、贴吧等新兴网络社交媒体的发展,大量用户开始喜欢使用这些平台进行发布和获取信息,因此累积了大量舆情数据。为了能够及时监测网络舆论动向,更好的维护互联网的安全运营和网络安全,针对实时微博数据,研究设计了一种基于SnowNLP的微博网络舆情分析系统。该系统由舆情数据采集、舆情数据分析和舆情数据可视化组成,能够实现微博数据文本挖掘、网络舆情数据情感分析、舆情数据与关键词匹配结果统计等功能,并能够对微博内容情感分析结果、用户等级、内容分词结果等进行可视化展示。实验测试结果表明：本系统功能运行正常,同时验证了设计方案的可行性和有效性。系统在网络舆情监测领域具有重要的应用价值。