An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data

Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.

The viscous strip approach to simplify the calculation of the surface acoustic wave generated streaming

In recent decades,the importance of surface acoustic waves,as a biocompatible tool to integrate with microfluidics,has been proven in various medical and biological applications.The numerical modeling of acoustic streaming caused by surface acoustic waves in microchannels requires the effect of viscosity to be considered in the equations which complicates the solution.In this paper,it is shown that the major contribution of viscosity and the horizontal component of actuation is concentrated in a narrow region alongside the actuation boundary.Since the inviscid equations are considerably easier to solve,a division into the viscous and inviscid domains would alleviate the computational load significantly.The particles'traces calculated by this approximation are excellently alongside their counterparts from the completely viscous model.It is also shown that the optimum thickness for the viscous strip is about 9-fold the acoustic boundary layer thickness for various flow patterns and amplitudes of actuation.

Research on Current Situation and Legal Regulation of Cosmetics Live Streaming E-commerce in China

Analyze the compatibility between cosmetics and live streaming e-commerce from its own nature,marketing means and supply chain characteristics.According to the prominent problems,sort out the relationship between all parties in the cosmetics live e-commerce industry chain.Combined with the latest regulatory policies of live streaming e-commerce and cosmetics,the responsibilities of different subjects in cosmetics live streaming e-commerce are summarized,and relevant suggestions and countermeasures are put forward for the standardization and development of live streaming e-commerce.Cosmetics brand owners are the first responsible persons for product quality.Anchors,as a mixed identity between intermediary,advertising spokesperson and operator,should bear stricter joint and several liability when recommending products related to consumers’health.If anchors fail to clearly identify themselves in the recommendation process,thus causing consumers to mistake them for the operator of the cosmetics,they should assume the obligations of the operator.

A Study on the Factors Influencing Consumer Purchase Decision Under the Live-Streaming Sales Model

In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.

Machine Learning Based Classifiers for QoE Prediction Framework in Video Streaming over 5G Wireless Networks

Recently,the combination of video services and 5G networks have been gaining attention in the wireless communication realm.With the brisk advancement in 5G network usage and the massive popularity of threedimensional video streaming,the quality of experience(QoE)of video in 5G systems has been receiving overwhelming significance from both customers and service provider ends.Therefore,effectively categorizing QoE-aware video streaming is imperative for achieving greater client satisfaction.This work makes the following contribution:First,a simulation platform based on NS-3 is introduced to analyze and improve the performance of video services.The simulation is formulated to offer real-time measurements,saving the expensive expenses associated with real-world equipment.Second,A valuable framework for QoE-aware video streaming categorization is introduced in 5G networks based on machine learning(ML)by incorporating the hyperparameter tuning(HPT)principle.It implements an enhanced hyperparameter tuning(EHPT)ensemble and decision tree(DT)classifier for video streaming categorization.The performance of the ML approach is assessed by considering precision,accuracy,recall,and computation time metrics for manifesting the superiority of these classifiers regarding video streaming categorization.This paper demonstrates that our ML classifiers achieve QoE prediction accuracy of 92.59%for(EHPT)ensemble and 87.037%for decision tree(DT)classifiers.

基于Spark Streaming的气象自动站实时流处理与存储系统

在当前大数据技术蓬勃发展的时代,人们对气象数据的实时处理、数据质量、数据存储及大规模查询等要求也越来越高。针对现有气象自动站数据业务落地环节多,任务处理耦合紧但系统部署分散等问题,文中基于Spark Streaming的流式计算框架,研究使用Flume解析收集自动站原始数据,在Spark Streaming中设计融入自动站数据质控算法,最终通过对分布式数据库存储的表设计,使气象自动站数据具备高效率、高质量、高可靠的应用服务能力。性能测试结果表明,基于Spark Streaming的气象自动站数据实时流处理与存储系统,数据从文件采集、解码、流处理至入库的全流程能够在秒级完成,TB级数据查询响应为毫秒级,加权查询为秒级,完全满足自动站数据业务应用需求,从而为进一步提高气象自动站数据质量与服务水平提供基础支撑。

Distribution pattern of acoustic and streaming field during multi-source ultrasonic melt treatment process

The ultrasonic melt treatment(UMT)is widely used in the fields of casting and metallurgy.However,there are certain drawbacks associated with the conventional process of single-source ultrasonic(SSU)treatment,such as the fast attenuation of energy and limited range of effectiveness.In this study,the propagation models of SSU and four-source ultrasonic(FSU)in Al melt were respectively established,and the distribution patterns of acoustic and streaming field during the ultrasonic treatment process were investigated by numerical simulation and physical experiments.The simulated results show that the effective cavitation zone is mainly located in a small spherical region surrounding the end of ultrasonic horn during the SSU treatment process.When the FSU is applied,the effective cavitation zone is obviously expanded in the melt.It increases at first and then decreases with increasing the vibration-source spacing(Lv)from 30 mm to 100 mm.Especially,when the Lv is 80 mm,the area of effective cavitation zone reaches the largest,indicating the best effect of cavitation.Moreover,the acoustic streaming level and flow pattern in the melt also change with the increase of Lv.When the Lv is 80 mm,both the average flow rate and maximum flow rate of the melt reach the highest,and the flow structure is more stable and uniform,with the typical morphological characteristics of angular vortex,thus significantly expanding the range of acoustic streaming.The accuracy of the simulation results was verified by physical experiments of glycerol aqueous solution and tracer particles.

Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience(QoE)and performance objectives.Most researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and performance.However,as network capacity increases,the performance degrades,impacting the live visual experience.Recently,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks.But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time.To address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics.The proposed framework contains modules such as Intelligent Content Extraction Module(ICEM),Channel Status Monitor(CSM),and Adaptive FEC(AFEC).This framework adopts the Cognitive Learning-based Scheduling(CLS)Module,which works on the deep Reinforced Gated Recurrent Networks(RGRN)principle and embeds them along with the FEC to achieve better performances.The complete framework was developed using the Objective Modular Network Testbed in C++(OMNET++),Internet networking(INET),and Python 3.10,with Keras as the front end and Tensorflow 2.10 as the back end.With extensive experimentation,the proposed model outperforms the other existing intelligentmodels in terms of improving the QoE,minimizing the End-to-End Delay(EED),and maintaining the highest accuracy(98%)and a lower Root Mean Square Error(RMSE)value of 0.001.

A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor devices.The investigation of data stream by the use of machine learning(ML)approaches to address regression,prediction,and classification problems have received consid-erable interest.At the same time,the detection of anomalies or outliers and feature selection(FS)processes becomes important.This study develops an outlier detec-tion with feature selection technique for streaming data classification,named ODFST-SDC technique.Initially,streaming data is pre-processed in two ways namely categorical encoding and null value removal.In addition,Local Correla-tion Integral(LOCI)is used which is significant in the detection and removal of outliers.Besides,red deer algorithm(RDA)based FS approach is employed to derive an optimal subset of features.Finally,kernel extreme learning machine(KELM)classifier is used for streaming data classification.The design of LOCI based outlier detection and RDA based FS shows the novelty of the work.In order to assess the classification outcomes of the ODFST-SDC technique,a series of simulations were performed using three benchmark datasets.The experimental results reported the promising outcomes of the ODFST-SDC technique over the recent approaches.

Design and Implementation of a Proxy Caching System for Streaming Media

With the widespread use of streaming media application on the Internet, a significant change in Internet workload will be provoked. Caching is one of the applied techniques for enhancing the scalability of streaming system and reducing the workload of server/network. Aiming at the characteristics of broadband network in community, we propose a popularity-based server-proxy caching strategy for streaming medias, and implement the prototype of streaming proxy caching based on this strategy, using RTSP as control protocol and RTP for content transport. This system can play a role in decreasing server load, reducing the traffic from streaming server to proxy, and improving the start-up latency of the client. Key words streaming server - proxy - cache - streaming media - real time streaming protocol CLC number TP 302 - TP 333 Foundation item: Supported by the National High Technology Development 863 Program of China (2001AA111011).Biography: Tan Jin (1962-), male, Ph. D candidate, research direction: network communications, multimedia technologies, and web caching.

基于Spark Streaming的实时数据分析系统及其应用

为了实现对实时网络数据流的快速分析,设计一种分布式实时数据流分析系统(DRDAS),能有效解决并发访问数据流的收集、存储和实时分析问题,为大数据环境的网络安全检测提供了一种有效的数据分析平台;根据Spark Streaming运行的原理设计一种动态采样的K-Means并行算法,与DRDAS结合能实时有效地检测大数据环境下的各种分布式拒绝服务(DDo S)攻击。实验结果显示:DRDAS具有好的可扩展性、容错性和实时处理能力,与动态采样的K-Means并行算法结合能实时地检测各种DDo S攻击,缩短了攻击的检测时间。

面向直播HTTP Streaming系统的HTTP缓存服务器行为优化

HTTP缓存服务器是提高HTTP Streaming系统客户并发量的关键环节。但当前主流HTTP缓存服务器,如Nginx、Squid、Varnish等,在缓存资源更新期间的行为都存在不足,当被应用在面向直播的HTTP Streaming系统中时,会周期性地把大量客户端请求转发至源服务器,从而制约了HTTP Streaming系统的可伸缩性。提出一种优化的HTTP缓存服务器在缓存更新期间的行为,即缓存服务器仅向源服务器转发一路客户端请求,缓存更新期间,拒绝其他关于该资源的请求。优化策略在使用最为广泛的Nginx服务器的基础上进行了实现。实验证明,优化后系统的伸缩性得到了显著提高。

使用Spark Streaming的自适应实时DDoS检测和防御技术

分布式拒绝服务(distributed denial of service,DDoS)攻击是重要的安全威胁,网络速度的不断提高给传统的检测方法带来了新的挑战。以Spark等为代表的大数据处理技术,给网络安全的高速检测带来了新的契机。提出了一种基于Spark Streaming框架的自适应实时DDoS检测防御技术,通过对滑动窗口内源簇进行分组,并根据与各分组内源簇比例的偏差统计,检测出DDoS攻击流量。通过感知合法的网络流量,实现了对DDoS攻击的自适应快速检测和有效响应。实验结果表明,该技术可极大地提升检测能力,为保障网络服务性能和安全检测的可扩展性提供了一种可行的解决方案。

基于Spark Streaming流回归的煤矿瓦斯浓度实时预测

为了实时分析瓦斯监测流数据并对瓦斯浓度进行准确预测以实现瓦斯灾害实时预警,以实时流数据处理框架Spark Streaming构建基于流回归的瓦斯浓度实时预测系统。系统采用分布式流处理技术,可使基于回归算法的瓦斯浓度预测模型更新周期达到秒级,提高了瓦斯浓度预测精度,满足流式大数据处理的实时性要求。实验表明:应用Spark Streaming流回归预测系统在采样周期为5s的瓦斯监测数据流上进行实时预测时,预测平均均方根误差随模型更新周期的缩短而减小,模型更新周期可达15s,且更新周期为45s时预测总均方根误差最小,既能保证预测精度,又能提高瓦斯灾害预警时效。

基于Spark Streaming的实时能耗分项计量系统

能耗分项计量能够准确、及时、有效地发现能源使用问题,形成和实现最有效的节能措施。能耗分项计量系统需要对各项能源使用量在不同粒度上进行统计,既有实时性的需求,又需要涉及到聚合、去重、连接等较为复杂的统计需求。由于数据产生快、实时性强、数据量大,所以很难统一采集并入库存储后再作处理,这便导致传统的数据处理架构不能满足需求。为此,提出基于Spark Streaming大数据流式技术构建一个实时能耗分项计量系统,对实时能耗分项计量的系统架构和内部结构进行了详细介绍,并通过实验数据分析了系统的实时数据处理能力。与传统架构不同,实时能耗分项计量系统在数据流动的过程中实时地进行捕捉和处理,一方面把捕捉到的异常信息及时报警到前端,同时把分类分项统计处理的结果保存到数据库,以便进行离线分析和数据挖掘,能有效地解决上述数据处理过程中遇到的问题。

基于Hadoop Streaming的Last比对软件并行化的研究与实现

随着下一代测序技术的到来,单机版Last比对软件已经不能满足海量数据的处理需求。使用Hadoop Streaming技术将Last比对软件快速部署到云计算环境中,解决当前单机版Last比对软件处理大数据能力差的问题。通过自定义的基于NFS文件系统的数据集切分方法和基于Partitioner的任务分配方式能够实现均衡高效的数据切分,并保证并行化粒度可控。实验结果表明,在保证与单机运行结果一致的情况下,这种方法能有效缩减软件运行时间,具有较高的加速比。

医学影像系统Streaming框架的设计和实现

提出医学影像Streaming概念,并设计和实现一个基于DICOM动态影像和静态影像的Streaming框架,有效地提高了浏览的实时性,使浏览影像更加方便。

Spark Streaming中参数与资源协同调整策略

Spark Streaming是一种典型的批量流式计算平台,可用于处理持续到达的数据流。流式数据最重要的两个特征是波动性和时效性。利用动态调整系统参数和动态调整资源满足不同数据到达速率的响应延迟,但调整参数的方式具有局限性,其用户成本较大。因此提出一种参数和资源协同调整策略,采用动态邻域粒子群算法找到一种满足SLO目标且使用资源最少的系统方案。实验表明,AdaStreaming与DyBBS相比,延迟性降低了70.1%,在资源使用量上比DRA降低了42.1%。

基于Spark Streaming的高校网站敏感信息监测设计与实现

高校在实现智慧化的同时也面临着网站发布信息或网页内容被黑客篡改成不符合国家或学校规定的信息及内容.通过对已有学术研究发现,现有技术的研究普遍存在着效率低、实时性差的问题,本文提出了一种基于Spark Streaming的高校网站敏感信息监测系统.该系统利用Kafka作为中间存储,系统架构在Spark Streaming框架上可实时消费Kafka中数据进行链接解析处理,将获取到的网页内容存储到Elastic Search中进行倒排索引敏感信息匹配,从而达到数据采集和数据处理同步,提高了网站监测效率.

Coping with handover effects in video streaming over cellular networks

The 3rd generation partnership project (3GPP) has defined the protocols and codecs for implementing media streaming services over packet-switched 3G mobile networks. The specification is based on IETF RFCs on audio/video transport.It also adds new features to achieve better adaptation to the mobile network environment. In this paper, we propose an algorithm for handover detection and fast buffer refill that is based on the existing feedback and signaling mechanisms. The proposed algorithm refills the receiver buffer at a faster pace during a limited time frame after a hard handover is detected in order to achieve higher video quality.