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.

Dynamics of iron and aluminum storages in a subtropical forest headwater stream

The forest headwater streams are important hubs for connecting terrestrial and aquatic ecosystems,with plant litter and sediments as the major carriers for material migrations;however,until now we knew little about the dynamics of trace elements such as iron(Fe)and aluminum(Al)in forest headwater streams.Here,we quantitatively identified the spatiotemporal dynamics of Fe and Al storages in plant litter and sediments and their influencing factors in a subtropical forest headwater stream,and assessed the potential pollution risk.The results showed that:(1)the mean concentrations of Fe and Al in plant litter(sediments)were 5.48 and 8.46(7.39 and 47.47)g·kg^(-1),and the mean storages of Fe and Al in plant litter(sediments)were 0.26 and 0.43(749.04 and 5030.90)g·m^(-2),respectively;(2)the storages of Fe and Al in plant litter and sediments significantly fluctuated from January to December,and showed a decreasing pattern from the source to mouth;and(3)storages of Fe and Al had no significant correlation with riparian forest type and the present of tributary and the Fe and Al storages in plant litter were mainly affected by water temperature and water alkalinity,and their storages in sediments were mainly affected by water temperature and frequency of rainfall;and(4)there were no anthropogenic pollution in Fe and Al in the forest headwater stream.Our study revealed the primary factors of concentrations and storages of Fe and Al in plant litter and sediments in a forest headwater stream,which will improve our understanding of the role of headwater streams in forest nutrient storage and cycling along with hydrological processes.

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.

Features of sampling stream sediments of large river valleys under cryolithogenesis conditions in the Balygychan-Sugoy trough,North-East of Russia

Comprehensive research has been implemented to raise the efficiency of the geochemical survey of stream sediments(SSs)that formed under the cryolithogenesis conditions.The authors analysed the composition,structure and specific features of the formation of exogenous anomalous geochemical fields(AGFs)identified through SSs of large river valleys of IV order.In our case,these were the valleys of Maly Ken,Ken and Tap Rivers.These rivers are located in the central and southern parts of the Balygychan-Sugoy trough enclosed in the Magadan region,North-East of Russia.The authors proposed a new technique to sample loose alluvium of SSs in the large river valleys along the profiles.The profiles were located across the valleys.The AGFs of Au,Ag,Pb,Zn,Sn,Bi,Mo and W were studied.Correlations between elements have been established.These elements are the main indicator elements of Au-Ag,Ag-Pb,Sn-Ag,Mo-W and Sn-W mineralization occurring on the sites under study.The results obtained were compared with the results of geochemical surveys of SSs.It is concluded that the AGFs recognized along the profiles reflect the composition and structure of eroded and drained ore zones,uncover completely and precisely the pattern of element distribution in loose sediments of large water flows.The alluvium fraction<0.25 mm seems to be most significant in a practical sense,as it concentrated numerous ore elements.Sampling of this fraction in the river valleys of IV order does not cause any difficulty,for this kind of material is plentiful.The developed technique of alluvium sampling within large river valleys is efficient in searching for diverse mineralization at all stages of prognostic prospecting.It is applicable for geochemical survey of SSs performed at different scales both in the North-East of Russia,as well as other regions with similar climatic conditions,where the SSs are formed under the cryolithogenesis conditions.

Physical Layer Encryption of OFDM-PON Based on Quantum Noise Stream Cipher with Polar Code

Orthogonal frequency division multiplexing passive optical network(OFDM-PON) has superior anti-dispersion property to operate in the C-band of fiber for increased optical power budget. However,the downlink broadcast exposes the physical layer vulnerable to the threat of illegal eavesdropping. Quantum noise stream cipher(QNSC) is a classic physical layer encryption method and well compatible with the OFDM-PON. Meanwhile, it is indispensable to exploit forward error correction(FEC) to control errors in data transmission. However, when QNSC and FEC are jointly coded, the redundant information becomes heavier and thus the code rate of the transmitted signal will be largely reduced. In this work, we propose a physical layer encryption scheme based on polar-code-assisted QNSC. In order to improve the code rate and security of the transmitted signal, we exploit chaotic sequences to yield the redundant bits and utilize the redundant information of the polar code to generate the higher-order encrypted signal in the QNSC scheme with the operation of the interleaver.We experimentally demonstrate the encrypted 16/64-QAM, 16/256-QAM, 16/1024-QAM, 16/4096-QAM QNSC signals transmitted over 30-km standard single mode fiber. For the transmitted 16/4096-QAM QNSC signal, compared with the conventional QNSC method, the proposed method increases the code rate from 0.1 to 0.32 with enhanced security.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

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的车辆电子围栏技术实现与应用

文章提出一种基于Spark Streaming实时数据流处理框架,使用Kafka作为车辆轨迹数据的消息队列服务,结合拓扑关系判断算法射线法的车辆电子围栏技术。应用表明,该技术能够处理高吞吐率、强实时性的车辆动态数据,满足车辆动态精细化监管需求。

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.

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.

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.

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。

胶东盘子涧金矿床成矿流体特征--来自包裹体、H-O同位素证据

盘子涧金矿床地处华北板块胶辽隆起区,栖霞—蓬莱金成矿带上。金矿的形成主要与区内控矿断裂——盘子涧断裂和中生代岩浆岩有关。为研究该矿床成矿流体性质及演化,并控讨矿床成因,对该矿床不同阶段的包裹体进行岩相学、显微测温、包裹体激光拉曼及H—O同位素分析研究。盘子涧金矿床成矿热液期可划分为4个成矿阶段,从早到晚分别是黄铁矿—石英阶段(Ⅰ阶段)、石英—黄铁矿(绢云母)阶段(Ⅱ阶段)、金—石英—多金属硫化物阶段(Ⅲ阶段)和石英—碳酸盐阶段(Ⅳ阶段)。其中Ⅱ、III阶段为主成矿阶段。不同成矿阶段的流体包裹体有3种类型,分别是富液气液两相盐水包裹体、含CO_(2)三相包裹体和纯液相包裹体。显微测温结果显示,成矿流体的完全均一温度介于142~348℃,主要集中于200~300℃,盐度介于4.44%~10.98%NaCl_(eqv)。石英的δD_(V-SMOW)值为-74.6‰~-68.5‰,δ^(18)O_(V-SMOW)值为+11.65‰~+13.92‰。显示成矿流体为中低温、低盐度的CO_(2)—H_(2)O—NaCl体系,来源于地幔,以岩浆热液为主,并伴有部分大气降水加入。矿床成因类型属石英脉型金矿。

丛枝菌根真菌对褐土玉米氮素吸收和土壤N_(2)O排放的影响

探究不同氮肥水平下丛枝菌根(AM)真菌对褐土玉米土壤N_(2)O排放和氮转化功能基因的影响,为阐明AM真菌在褐土N_(2)O排放中的作用和效应提供理论依据。设置氮肥用量(NⅠ:105 mg/kg;NⅡ:210 mg/kg)、AM真菌(M0:不接种AM真菌;M1:接种根内根孢囊霉(Rhizophagus intraradices);M2:接种摩西斗管囊霉(Funneliformis mosseae);M3:接种Rhizophagus intraradices+Funneliformis mosseae等比例混合)双因素盆栽试验。测定植株地上部全氮含量、土壤铵态氮、硝态氮含量和N_(2)O排放量,采用实时荧光定量聚合酶链式反应(PCR)法分析土壤硝化功能基因(amoA-AOA和amoA-AOB)和反硝化功能基因(nirS、nirK和nosZ)的丰度。结果表明,两种施氮水平下,接种AM真菌均可显著降低土壤N_(2)O排放通量和累积排放量,不同AM真菌处理下N_(2)O累积排放量表现为:M0>M2>M1>M3。相同AM真菌处理的土壤N_(2)O排放通量和累积排放量在NⅡ施氮水平高于NⅠ施氮水平;相同AM真菌处理的玉米菌根侵染率在NⅡ施氮水平低于NⅠ施氮水平。与M0相比,NⅠ条件下M1、M2和M3处理土壤铵态氮含量分别降低24.5%、20.8%和45.3%,硝态氮含量分别降低19.7%、14.9%和30.2%,植株地上部全氮含量分别增加16.3%、35.2%和59.6%;与M0相比,NⅡ条件下M1、M2和M3处理土壤铵态氮含量分别降低20.9%、24.8%和40.0%,硝态氮含量分别降低36.3%、25.6%和45.2%,植株地上部全氮含量分别增加33.2%、43.9%和95.4%。两种施氮水平下,AM真菌可显著降低土壤硝化功能基因(amoA-AOA和amoA-AOB)丰度,增加反硝化功能基因(nirS、nirK和nosZ)丰度。AM真菌与N_(2)O排放通量呈极显著负相关。本盆栽试验条件下,接种AM真菌均可增强两种氮肥用量玉米植株氮素吸收能力,调节硝化、反硝化相关功能基因的丰度,减少土壤N_(2)O气体的排放,且两种AM真菌混合处理的N_(2)O减排效应强于单一AM真菌接种。

QoS Control in Streaming Media

In this paper, the QoS(Quality of Service) control in streaming media is discussed, and more attention has been paid on the implementation of Qos control in the streaming media server system of the IP network architecture.

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。

Z型异质结Cu_(2)O/Bi_(2)MoO_(6)的构建及光催化降解性能

通过水热法制备出一系列Z型异质结Cu_(2)O/Bi_(2)MoO_(6)新型光催化剂。采用扫描电子显微镜、粉末X射线衍射、红外光谱、紫外可见吸收光谱等表征手段研究了催化剂的形貌、结构性质和光电化学性质,并以四环素(TC)为降解目标污染物,进一步探究了其催化效率。实验结果表明,Cu_(2)O的加入提高了复合催化剂的光催化性能,其中20%Cu_(2)O/Bi_(2)MoO_(6)复合催化剂(Cu_(2)O和Bi_(2)MoO_(6)的质量比为20%)降解效果最好,100 min内可降解95%的TC。Cu_(2)O与Bi_(2)MoO_(6)之间的协同作用使其可以吸收更多的可见光,所构建的Z型异质结改变了电子转移途径,提高了电子与空穴的分离效率,光催化活性显著提高。通过自由基捕获实验和能带结构,分析了Z型异质结Cu_(2)O/Bi_(2)MoO_(6)复合催化剂光催化降解TC可能的机理。