Subspace Clustering in High-Dimensional Data Streams:A Systematic Literature Review

Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space.Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams.Data streams are not only high-dimensional,but also unbounded and evolving.This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams.Although many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data streams.Therefore,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments.The review follows a systematic methodological approach and includes 18 articles for the final analysis.The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams.The main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation measures.Most algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering stage.The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters.Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers.Future work can focus on the clustering framework,parameter optimization,subspace search techniques,memory-efficient synopsis structures,explicit cluster change detection,and intrinsic performance metrics.This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams.

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.

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.

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.

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.

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.

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.

Chemical simulation teaching system based on virtual reality and gesture interaction

Background Most existing chemical experiment teaching systems lack solid immersive experiences,making it difficult to engage students.To address these challenges,we propose a chemical simulation teaching system based on virtual reality and gesture interaction.Methods The parameters of the models were obtained through actual investigation,whereby Blender and 3DS MAX were used to model and import these parameters into a physics engine.By establishing an interface for the physics engine,gesture interaction hardware,and virtual reality(VR)helmet,a highly realistic chemical experiment environment was created.Using code script logic,particle systems,as well as other systems,chemical phenomena were simulated.Furthermore,we created an online teaching platform using streaming media and databases to address the problems of distance teaching.Results The proposed system was evaluated against two mainstream products in the market.In the experiments,the proposed system outperformed the other products in terms of fidelity and practicality.Conclusions The proposed system which offers realistic simulations and practicability,can help improve the high school chemistry experimental education.

基于STREAM教育理念的初中物理实验拓展的内涵和价值研究

基于初中物理实验教学现状,结合初中物理实验教学对STREAM教育进行本土化解读,探索基于STREAM教育理念的初中物理实验拓展的内涵和价值。

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.

最新一代Eagle Stream平台服务器的主板工艺技术研究

随着Intel最新一代Eagle Stream平台的推出,该平台服务器将会占据未来的服务器主流市场。本文关于Eagle Stream平台服务器,围绕0.94 mm pitch背钻出双线、正、反面树脂塞孔+POFV和插入损耗需达到Ultra Low Loss要求这三个产品特点,分析了工艺上存在的挑战和难点,并列举介绍了关键工序和管控措施,以及相应的过程数据。

STREAM理念在中学化学核心素养的教学实践

核心素养理念再次激发了教师践行课改意识、创新思路、品质观念的踊跃性与自觉性,教师积极推动化学教学实现方法进阶、提质增效。为了提升化学教学的素养型,教师开始将STREAM理念引入课堂活动,简要解析STREAM理念是核心素养的落实支持,扼要概括STREAM理念下核心素养培养的研究价值,全面总结STREAM理念下核心素养培养的教学方法。因此,STREAM理念开启了核心素养培养活动的新方法、新导向,能科学培养初中生的高阶思维与课程能力。

STREAM教育背景下小学跨学科课程实施路径研究

当前跨学科课程的有效开展面临诸多困难和挑战,将STREAM教育理念融入跨学科主题学习中,为跨学科课程实施提供新思路。文章分别从设计真实驱动问题、深化学生合作探究、促进多门学科整合、优化课程评价体系四个维度对STREAM教育理念指导下小学跨学科课程的实施方式进行归纳总结,旨在为小学跨学科课程的有效开展提供理论指导和实践参考。

脑卒中偏瘫患者步态特征及其与STREAM量表下肢运动部分评分间的相关性分析

脑卒中偏瘫患者的步态异常会严重影响其日常生活活动能力。步态分析作为定量评估方法已广泛应用于步态异常的科学研究,但步态分析数据繁杂,如何高效且规范化进行偏瘫患者的下肢运动功能的临床评估仍有待研究。目的探究脑卒中偏瘫患者步态特征,并通过步态特征及脑卒中康复运动评价(STREAM)量表下肢运动部分评分的相关性分析筛选出一些步态的定量指标为偏瘫患者的下肢运动功能的临床评估和诊断提供规范化数据。方法 选择2021年5月至2022年6月在安徽医科大学第二附属医院康复医学科接受治疗的脑卒中后偏瘫患者21例为卒中组,其中男性12例,女性9例;年龄14~77岁,平均年龄49.29岁;身高153~181 cm,平均身高168.90 cm;体质量53~94 kg,平均体质量69.88岁;其中脑出血恢复期患者9例,脑梗死恢复期患者12例。选取同时间段自愿参与该研究的健康者21例作为健康组,其中男性11例,女性10例;年龄24~71岁,平均年龄49.14岁;身高155~183 cm,平均身高167.67 cm;体质量48~80 kg,平均体质量65.95 kg。应用步态分析系统及运动障碍数字化测试平台分别采集两组受试者步态参数,对采集的数据进行统计分析,并对STREAM量表下肢运动部分评分与卒中组各指标进行相关性分析。结果 卒中组步幅小于健康组,步宽、触地时长百分比大于健康组,步速小于健康组,步相角大于健康组,差异均有统计学意义(P <0.05);两组T区、M1区、M2区足底压力占比差异均无统计学意义(P> 0.05);卒中组MF区压力占比较健康组高,差异有显著统计学意义(P <0.01);卒中组HEEL区压力占比较健康组低,差异有统计学意义(P <0.05);卒中组在COP-X、COP-Y方向的偏移值均高于健康组,差异均有统计学意义(P <0.05);卒中组患侧峰值屈髋角度较健康组减少,差异有统计学意义(P <0.05);卒中组患侧峰值屈膝角度较健康组减少,差异有统计学意义(P <0.05);卒中组患者STREAM量表下肢运动部分评分为(12.81±3.14)分。Person相关性分析显示STREAM量表下肢运动部分评分与步速、步幅、触地时长百分比、峰值屈膝角度、峰值屈髋角度呈正相关(P <0.05),与COP-X、COP-Y呈负相关(P <0.05);STREAM量表下肢运动部分评分与步相角、步宽无相关性(P> 0.05)。结论 与健康人比较,脑卒中偏瘫患者步态特征具有显著改变。主要表现为足跟处负重减少、外侧缘负重增多,平衡差,患侧下肢屈髋屈膝不足,步行能力降低。步态分析的一些指标与STREAM量表下肢运动部分评分间具有显著相关性。故可作为评价脑卒中偏瘫患者步行能力的重要定量分析指标。

UNIX System V中基于STREAMS的伪终端子系统及其应用

UNIX System V提供一种基于STREAMS机制的伪终端子系统。本文将介绍这种伪终端子系统及采用客户机/服务器模式设计的一种远程登录。

Improvement Design for Distributed Real-Time Stream Processing Systems

In the era of Big Data, typical architecture of distributed real-time stream processing systems is the combination of Flume, Kafka, and Storm. As a kind of distributed message system, Kafka has the characteristics of horizontal scalability and high throughput, which is manly deployed in many areas in order to address the problem of speed mismatch between message producers and consumers. When using Kafka, we need to quickly receive data sent by producers. In addition, we need to send data to consumers quickly. Therefore, the performance of Kafka is of critical importance to the performance of the whole stream processing system. In this paper, we propose the improved design of real-time stream processing systems, and focus on improving the Kafka's data loading process.We use Kafka cat to transfer data from the source to Kafka topic directly, which can reduce the network transmission. We also utilize the memory file system to accelerate the process of data loading, which can address the bottleneck and performance problems caused by disk I/O. Extensive experiments are conducted to evaluate the performance, which show the superiority of our improved design.

Effect of Seasonality on Distribution of Macroalgae in a Stream System(Xin'an Spring) in Shanxi Province,North China

A survey of the seasonal distribution of macroalgae in a stream system in Shanxi Province, north China, was undertaken from July 2004 to April 2005. The relative abundance and percentage cover of macroalgae, and several environmental factors were monitored along a 20-m stretch at each of four sites, at intervals of approximately three months （one sampling per season）. Several stream conditions were relatively constant over the sampling period （pH, maximum width and maximum depth）, whereas others exhibited a distinct seasonal pattern （water temperature and specific conductance）, and some fluctuated with no discernable seasonal pattern （current velocity and dissolved oxygen）. Forty-two species of macroalgae were found, with a predominance of Chlorophyta （26 species, 61.9%）. Rhodophyts and Charophyta represented the smallest proportion （1 species each, 2.38%）. Six macroalgae species were the most widespread, occurring in all four sampling sites. Twelve species were found at only one site each. In terms of seasonality, eight species occurred throughout the year, whereas 16 species were found in only one season each. The macroalgal community at Xin＇an Spring was species rich relative to other streams. Species richness per sampling site was negatively correlated with pH. Principal component analysis revealed that no single variable had much influence on the macroalgal seasonal dynamics. We calculated Sorensen similarity indices to compare our study with other continent-wide surveys of stream macroalgae, but the similarity indices were all very low. This study also shows that macroalgae in different locations have significant reproductive isolation.

Spatial and temporal variability of agricultural pollutants in an agricultural headwater stream within a multipond system, southeastern China

The spatial and temporal variability of nutrients and suspended solids were investigated for two years in a 1.8 km agricultural headwater stream, located by Chaohu Lake, southeastern China. The stream form was greatly modified by human activities into channelized, pond and estuary shapes. The stream could be divided into 4 channelized reaches(1.3 km), a pond reach(0.15 km) and 3 estuary reaches(0.36 km). It was found that nutrients and TSS concentrations in the stream showed temporal variability, and higher concentrations occurred in months with high precipitation and intensive agricultural activities. And, retention of total nitrogen(TN), nitrate(NO- 3-N), ammonium(NH+ 4-N) and total suspended solids(TSS) predominantly occurred in the pond reach and estuary reaches with larger width and low current velocity. Pollutants retained in these reaches accounted for more than 50% of those retained in whole stream. The retention mostly happened in the rain-runoff events and it was 7 to 27 times than that in base flow. The results showed that the channelized reach was the most important source for pollutants release under either runoff or base flow, and its release accounted for more than 90% of whole stream release. There was a high spatial variability of nutrients retention in different channelized reaches. The channelized reach directly discharging into the pond did always retain nutrients and TSS under base flow and runoff conditions, whereas the other channelized reaches performed differently in different hydrological conditions. The high spatial and temporal variability of nutrients and TSS in the stream indicated that anthropogenic disturbance of the agricultural headwater stream, such as channelization and excavation, would be expected to decrease the capacity of nutrients retention in the stream.