Change of stream network connectivity and its impact on flood control

AbstUrbanization can alter the hydrogeomorphology of streams and rivers,change stream network structures,and reduce stream network connectivity,which leads to a decrease in the storage capacity of stream networks and aggravates flood damage.Therefore,investigation of the ways in which stream network connectivity impacts flood storage capacity and flood control in urbanized watersheds can provide significant benefits.This study developed a framework to assess stream network connectivity and its impact on flood control.First,a few connectivity indices were adopted to assess longitudinal stream network connectivity.Afterward,the static and dynamic storage capacities of stream networks were evaluated using storage capacity indices and a one-dimensional hydrodynamic model.Finally,the impact of stream network connectivity change on flood control was assessed by investigating the changes in stream network connectivity and storage capacity.This framework was applied to the Qinhuai River Basin,China,where intensive urbanization has occurred in the last few decades.The results show that stream network storage capacity is affected by stream network connectivity.Increasing stream network connectivity enhances stream network storage capacity.©2020 Hohai University.Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Modelling Uncertainty of Stream Networks Derived from Elevation Data Using Two Free Softwares: R and SAGA

Stream networks are considered important units in many environmental decision making processes. The extraction of streams using digital elevation models (DEMs) presents many advantages. However it is very sensitive to the uncertainty of the elevation datasets used. The main aim of this paper is to implement geostatistical simulations and assess the propagated uncertainty and map the error of location streams. First, point sampled elevations are used to fit a variogram model. Next two hundred DEM realizations are generated using conditional sequential Gaussian simulation;the stream network map is extracted for each of these realizations, and the collection of stream networks is analyzed to quantify the error propagation. At each grid cell, the probability of the occurrence of a stream and the propagated error are estimated. The more probable stream network are delineated and compared with the digital stream network derived from topographic map. The method is illustrated using a small dataset (8742 sampled elevations) for Anaguid Saharan platform. All computations are run in two free softwares: R and SAGA. R is used to fit variogram and to run sequential Gaussian simulation. SAGA is used to extract streams via RSAGA library.

Two stream skeleton behavior recognition algorithm based on Motif-GCN

Compared with RGB videos and images,human bone data is less vulnerable to external factors and has stronger robustness.Therefore,behavior recognition methods based on skeletons are widely studied.Because graph convolution network(GCN)can deal with the irregular topology data of hu-man skeletons very well,more and more researchers apply GCN to human behavior recognition.Tra-ditional graph convolution methods only consider the joints with physical connectivity or the same type when building the behavior recognition model based on human skeletons structure,which cannot capture higher-order information better.To solve this problem,Motif-GCN is used in this paper to ex-tract spatial features.The relationship between the joints with natural connection in the human body is encoded by the first Motif-GCN,and the possible relationship between the unconnected joints in the human skeleton is encoded by the second Motif-GCN.In this way,the relationship between non-physical joints can be strengthened.Then a two stream framework combining joint and bone informa-tion is used to capture more action information.Finally,experiments are conducted on two subdata-sets X-Sub and X-View of NTU-RGB+D,and the accuracy shown in Top-1 classification results is 89.5%and 95.4%respectively.The experimental results are 1.0%and 0.3%higher than those of the 2S-AGCN model respectively.The superiority of this method is also proved by the experimental results.

Error resilient concurrent video streaming over wireless mesh networks

In this paper, we propose a multi-source multi-path video streaming system for supporting high quality concurrent video-on-demand (VoD) services over wireless mesh networks (WMNs), and leverage forward error correction to enhance the error resilience of the system. By taking wireless interference into consideration, we present a more realistic networking model to capture the characteristics of WMNs and then design a route selection scheme using a joint rate/interference-distortion optimiza- tion framework to help the system optimally select concurrent streaming paths. We mathematically formulate such a route selec- tion problem, and solve it heuristically using genetic algorithm. Simulation results demonstrate the effectiveness of our proposed scheme.

QoS Guaranteed Pre-Pushing Scheme in Peer-Assisted Streaming Network

In peer-assisted streaming network, service provider needs to pre-pushing resources to some users for caching. This scheme can increase the resource availability. In traditional strategy, in order to guarantee the user＇s quality of service （QoS）, the pre-pushing action can only start at the time point that the certain user has not used the computer for more than 20 minutes. If the user comes back, the pre-pushing action will be stopped. We claim that this is not an efficient scheme. In this paper, we propose a novel pre-pushing scheme to improve the pre-pushing efficiency, while meeting the user＇s QoS requirements. The basic idea is using the user＇s available bandwidth as much as possible in the condition of meeting the user＇s QoS requirements. Then we design an available bandwidth calculating strategy. The numerical simulation demonstrates that our proposed scheme outperforms the traditional one.

A Real-Time TCP Stream Reassembly Mechanism in High-Speed Network

With the continual growth of the variety and complexity of network crime means, the traditional packet feature matching cannot detect all kinds of intrusion behaviors completely. It is urgent to reassemble network stream to perform packet processing at a semantic level above the network layer. This paper presents an efficient TCP stream reassembly mechanism for real-time processing of high-speed network traffic. By analyzing the characteristics of network stream in high-speed network and TCP connection establishment process, several polices for designing the reassembly mechanism are built. Then, the reassembly implementation is elaborated in accordance with the policies. Finally, the reassembly mechanism is compared with the traditional reassembly mechanism by the network traffic captured in a typical gigabit gateway. Experiment results illustrate that the reassembly mechanism is efficient and can satisfy the real-time property requirement of traffic analysis system in high-speed network.

Multimedia Streaming for Ad Hoc Wireless Mesh Networks Using Network Coding

Over the past years, we have witnessed an explosive growth in the use of multimedia applications such as audio and video streaming with mobile and static devices. Multimedia streaming applications need new approaches to multimedia transmissions to meet the growing volume demand and quality expectations of multimedia traffic. This paper studies network coding which is a promising paradigm that has the potential to improve the performance of networks for multimedia streaming applications in terms of packet delivery ratio (PDR), latency and jitter. This paper examines several network coding protocols for ad hoc wireless mesh networks and compares their performance on multimedia streaming applications with optimized broadcast protocols, e.g., BCast, Simplified Multicast Forwarding (SMF), and Partial Dominant Pruning (PDP). The results show that the performance increases significantly with the Random Linear Network Coding (RLNC) scheme.

Joint routing and rate allocation for multiple video streams in ad-hoc wireless networks

The support for multiple video streams in an ad-hoc wireless network requires appropriate routing and rate allocation measures ascertaining the set of links for transmitting each stream and the encoding rate of the video to be delivered over the chosen links. The routing and rate allocation procedures impact the sustained quality of each video stream measured as the mean squared error (MSE) distortion at the receiver, and the overall network congestion in terms of queuing delay per link. We study the trade-off between these two competing objectives in a convex optimization formulation, and discuss both centralized and dis- tributed solutions for joint routing and rate allocation for multiple streams. For each stream, the optimal allocated rate strikes a balance between the selfish motive of minimizing video distortion and the global good of minimizing network congestions, while the routes are chosen over the least-congested links in the network. In addition to detailed analysis, network simulation results using ns-2 are presented for studying the optimal choice of parameters and to confirm the effectiveness of the proposed measures.

Author Gender Prediction in an Email Stream Using Neural Networks

With the rapid growth of the Internet in recent years, the ability to analyze and identify its users has become increasingly important. Authorship analysis provides a means to glean information about the author of a document originating from the internet or elsewhere, including but not limited to the author’s gender. There are well-known linguistic differences between the writing of men and women, and these differences can be effectively used to predict the gender of a document’s author. Capitalizing on these linguistic nuances, this study uses a set of stylometric features and a set of word count features to facilitate automatic gender discrimination on emails from the popular Enron email dataset. These features are used in conjunction with the Modified Balanced Winnow Neural Network proposed by Carvalho and Cohen, an improvement on the original Balanced Winnow created by Littlestone. Experiments with the Modified Balanced Winnow show that it is effectively able to discriminate gender using both stylometric and word count features, with the word count features providing superior results.

Data Stream Subspace Clustering for Anomalous Network Packet Detection

As the Internet offers increased connectivity between human beings, it has fallen prey to malicious users who exploit its resources to gain illegal access to critical information. In an effort to protect computer networks from external attacks, two common types of Intrusion Detection Systems (IDSs) are often deployed. The first type is signature-based IDSs which can detect intrusions efficiently by scanning network packets and comparing them with human-generated signatures describing previously-observed attacks. The second type is anomaly-based IDSs able to detect new attacks through modeling normal network traffic without the need for a human expert. Despite this advantage, anomaly-based IDSs are limited by a high false-alarm rate and difficulty detecting network attacks attempting to blend in with normal traffic. In this study, we propose a StreamPreDeCon anomaly-based IDS. StreamPreDeCon is an extension of the preference subspace clustering algorithm PreDeCon designed to resolve some of the challenges associated with anomalous packet detection. Using network packets extracted from the first week of the DARPA '99 intrusion detection evaluation dataset combined with Generic Http, Shellcode and CLET attacks, our IDS achieved 94.4% sensitivity and 0.726% false positives in a best case scenario. To measure the overall effectiveness of the IDS, the average sensitivity and false positive rates were calculated for both the maximum sensitivity and the minimum false positive rate. With the maximum sensitivity, the IDS had 80% sensitivity and 9% false positives on average. The IDS also averaged 63% sensitivity with a 0.4% false positive rate when the minimal number of false positives is needed. These rates are an improvement on results found in a previous study as the sensitivity rate in general increased while the false positive rate decreased.

Dynamic multimedia stream adaptation and rate control for heterogeneous networks

Dynamic adaptation of multimedia content is seen as an important feature of next generation networks and pervasive systems enabling terminals and applications to adapt to changes in e.g. context, access network, and available Quality-of-Service(QoS) due to mobility of users, devices or sessions. We present the architecture of a multimedia stream adaptation service which enables communication between terminals having heterogeneous hardware and software capabilities and served by heterogeneous networks. The service runs on special content adaptation nodes which can be placed at any location within the network. The flexible structure of our architecture allows using a variety of different adaptation engines. A generic transcoding engine is used to change the codec of streams. An MPEG-21 Digital Item Adaptation (DIA) based transformation engine allows adjusting the data rate of scalable media streams. An intelligent decision-taking engine implements adaptive flow control which takes into account current network QoS parameters and congestion information. Measurements demonstrate the quality gains achieved through adaptive congestion control mechanisms under conditions typical for a heterogeneous network.

Sentiment Analysis on the Social Networks Using Stream Algorithms

The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.

基于双流交互学习的长时空换装行人重识别

近年来深度行人重识别已取得快速进展,但长时空换装问题仍颇具挑战.为此构建一种双流交互学习算法模型(interactive dual-stream learning,IDSL):基于现有公开数据集生成无服装辅助模态图像,构建主辅双流分支网络来分别学习原始图像和无服装模态图像的细粒度特征;设计一种多尺度特征级联融合器(multi-scale feature cascade fusion,MSFCF),对细粒度特征进行重组并引进交叉注意力机制来实现全局语义和局部细节间的联合建模,提升模型鲁棒性;提出一种具有软惩罚机制的颈部网络(soft penalty batch normalization neck network,SoftBNNeck)来更好地区分度量学习和分类学习,使模型训练更稳定和可控;最后定义了双流一致性约束损失(dual-stream consistency constraint loss,DCCLoss)并探索了多损失联合训练策略,以更好地衡量换装行人身份的概率分布差异,提升重识别准确度.实验表明,在复杂换装行人公开数据集LTCC和Celeb-reID上,Rank-1/mAP分别达到73.8%/47.9%和66.7%/22.6%,领先于同类研究算法.

基于双流YOLOv4的金属表面缺陷检测方法

目前有许多学者使用深度学习进行表面缺陷检测研究,由于这些研究大都沿用主流目标检测算法的思路,注重高级语义特征,而忽视了低级语义信息(色彩、形状)对表面缺陷检测的重要性,因此导致缺陷检测效果不够理想。为解决上述问题,提出了一种金属表面缺陷检测网络——双流YOLOv4网络,骨干网络分成两个分支,输入分为高分辨率图像和低分辨率图像,浅分支负责从高分辨率图像中提取低级特征,深分支负责从低分辨率图像中提取高级特征,通过削减两分支的层数和通道数来减少模型总参数量;为了强化低级语义特征,提出了一种树形多尺度融合方法(Tree-structured Multi-scale Feature Fusion Me-thod,TMFF),并设计了一个结合极化自注意力机制和空间金字塔池化的特征融合模块(Feature Fusion Module with Polarized Self-Attention Mechanism and Spatial Pyramid Pooling,FFM-PSASPP)应用到TMFF中。在东北大学热轧带表面缺陷数据集NEU-DET、金属表面缺陷数据集GC10-DET和伊莱特电饭煲内胆缺陷数据集Enaiter的测试集上对所提算法进行了测试,测得的map@50结果分别为0.80,0.66和0.57,相比大部分主流的用于缺陷检测的目标检测算法均有提升,且模型参数量仅为原YOLOv4的一半,速度与YOLOv4接近,可满足实际使用需求。

基于双流骨架信息的人体动作识别方法

针对当前基于二维图像的人体动作识别算法鲁棒性差、识别率不高等问题,提出了一种融合卷积神经网络和图卷积神经网络的双流人体动作识别算法,从人体骨架信息提取动作的时间与空间特征进行人体动作识别。首先,构建人体骨架信息时空图,利用引入注意机制的图卷积网络提取骨架信息的时间和空间特征;其次,构建骨架信息运动图,将卷积神经网络网络提取到骨架运动信息的特征作为时空图卷积网络所提取特征的时间和空间特征的补充;最后,将双流网络进行融合,形成基于双流的、注意力机制的人体动作识别算法。算法增强了骨架信息的表征能力,有效提高了人体动作的识别精度,在NTU-RGB+D60数据集上取得了比较好的结果,Cross-Subject和Cross-View的识别率分别为86.5%和93.5%,相比其他同类算法有一定的提高。

基于scSE非局部双流ResNet网络的行为识别

针对双流网络对包含冗余信息的视频帧存在识别率低的问题,在双流网络的基础上引入scSE(Spatial and Channel Squeeze&Excitation Block)和非局部操作,构建SC_NLResNet行为识别框架。该框架将视频划分为等分不重叠的时序段并在每段上稀疏采样,提取RGB帧以及光流图作为scSE模块的输入;将经过scSE处理的特征输入非局部双流ResNet网络中,融合各分段得到最终的预测结果。在UCF101以及Hmdb51数据集上实验准确率分别达到96.9%和76.2%,结果表明,非局部操作与scSE模块结合可以增强特征时空上以及通道间的信息提高准确率,验证了SC_NLResNet网络的有效性。

基于通道注意特征融合的轴承故障诊断方法

针对传统故障诊断方法通常依赖单域信息输入,导致信号中的部分信息丢失或信息不完整使用的问题,提出了一种基于通道注意特征融合的轴承故障诊断方法。首先,将原始信号通过快速傅里叶变换(FFT)和连续小波变换(CWT)处理得到频域和时频图。然后,将来自不同域的2个样本输入双流Ghost Module(GM)神经网络故障诊断模型中提取频域和时频域特征,并结合通道注意力机制有效融合频域和时频域的重要特征,从而获得更丰富的故障诊断信息,实现对故障信号的准确分类。最后,利用美国凯斯西储大学、中国江南大学和加拿大渥太华大学的轴承故障数据集进行实验验证。结果表明,与现行主流模型相比,基于通道注意特征融合的轴承故障诊断方法在3个数据集上的分类故障诊断准确率分别达到99.78%、98.50%和97.65%,证明该方法具有良好的诊断效果。

移动边缘网络中基于QoE的网络媒体流卸载算法

针对移动边缘计算中新兴网络媒体流业务面临的高时延、高能耗、高带宽、低用户体验质量(QoE)等问题,提出一种基于QoE反馈配置卸载(QFCO)算法。首先,联合考虑预处理和优先级划分,从而最大化网络资源利用率,并为计算任务赋予不同的权重建立资源分配关系;然后,综合考虑截止时间、计算资源、功率和带宽等约束,以任务时延、能耗和精确度加权和为优化目标建立QoE模型,利用拉格朗日乘数法求解。仿真结果表明,相比深度增强学习在线卸载(DROO)算法,所提算法可有效实现资源的整体优化配置,更好地提升用户体验质量。

面向AI安全的深度伪造视频检测技术

针对实际应用伪造类型多样化,而检测过程中只检测单一伪造数据类型等问题,提出了一种新的数据预处理方法,解决了视频中运动图像容易连续重叠的问题;针对人脸模糊伪造,侧面人脸伪造和遮挡人脸伪造问题,利用小卷积核代替大卷积核的方法训练出自己的Deeper网络;针对帧插入、帧复制、帧修改、帧内篡改四种常见的深度视频伪造,尤其是在测试领域差距较大的情况下,容易产生泛化性不足的问题,设计了一种改进的基于双流分析的深度伪造视频检测模型。

河流分类分级分段及河流形态学研究进展

河流分类、河流分级及干流河道分段合称为河流三分(分类·分级·分段);基于河流三要素(流域-水系-干流河道)和河流三分-河流形态学-河流学体系,开展了河流三分理论研究及实践应用、河流形态学再构建以及河流形态定性特性与定量因子指标辨识。主要成果结论包括:①优化了独立与非独立河流分类、河流分级Horton法改进、河流5区分段等方法,完善了河流界定拓展、河流平等与分级、干流河道“层次分段”等新理论,总结了世界大江大河及长江河流三分实践应用;②河流形态学研究河流现状及恒常形态,包括以河流三分为基石的河流三要素特性研究和以干流河道平面-剖面-断面形态为核心的河谷-河床-河道形态研究;③定性辨识了河流形态15项殊相特性与共相原则(包括复合流域水系、复杂干流河道等形态特性,干流河道优先、河流湖泊统一、干流河道唯一、侵蚀基面统一等新增原则);④定量辨识了河流形态4类因子指标,双指标3种相关关系(并行-从属-或然)与3类组合模式(物理-化学-生物)以及三因子4类及9种相关模式(均衡线型-瓶颈线型-三角形-金字塔-均分圆-同心圆-同切圆-交叉圆-三角圆)。倡导“河流学-长江学-水科学”学科范式,积极践行“治河·治江·治水”理念。