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E-voting system using cloud-based hybrid blockchain technology

Journal of Safety Science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election processes.During pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of votes.This urges a reliable,flexible,transparent,secure,and cost-effective voting system.The proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting phases.Using smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain network.Finally,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or corrupted.Hence,the overall performance of the proposed system is significantly better than that of the existing system.Further performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting cloud Blockchain Smart contract Consensus mechanism

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Splitting and placement of data-intensive applications with machine learning for power system in cloud computing

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Digital Communications and Networks 2022年第4期8卷 476-484页

作者： Zhanyang Xu Dawei Zhu Jinhui Chen Baohua Yu School of Computer and Software Nanjing University of Information Science and TechnologyNanjingChina College of Information Science and Technology Shihezi UniversityXinjiangChina

Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things(IoT),machine learning technology has been adopted to deal with the data-intensive power electronics applications in IoT.By feeding previous power electronic data into the learning model,accurate information is drawn,and the quality of IoT-based power services is improved.Generally,the data-intensive electronic applications with machine learning are split into numerous data/control constrained tasks by workflow technology.The efficient execution of this data-intensive Power Workflow(PW)needs massive computing resources,which are available in the cloud infrastructure.Nevertheless,the execution efficiency of PW decreases due to inappropriate sub-task and data placement.In addition,the power consumption explodes due to massive data acquisition.To address these challenges,a PW placement method named PWP is devised.Specifically,the Non-dominated Sorting Differential Evolution(NSDE)is used to generate placement strategies.The simulation experiments show that PWP achieves the best trade-off among data acquisition time,power consumption,load distribution and privacy preservation,confirming that PWP is effective for the placement problem.

关键词： IoT-based power system cloud Workflow Machine learning NSDE

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Characteristics of cloud Water Resource and Precipitation Efficiency of Hydrometeors over Northwest China

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Journal of Meteorological Research 2023年第3期37卷 353-369页

作者： Zhanyu YAO Lin AN Pei ZHANG Liangshu GAO Shuo JIA Weijian WANG Wenhui ZHAO China Meteorological Administration cloud–Precipitation Physics and Weather Modification Key Laboratory Weather Modification CentreChina Meteorological AdministrationBeijing 100081

Understanding the characteristics of cloud water resource(CWR)and precipitation efficiency of hydrometeors(PEh)is imperative for the application of CWR in Northwest China.The atmospheric precipitable water(PW)in all four seasons and clouds and PEh in summer were studied with ERA-5 and cloudSat data in this region.The results show that topography,especially in the Tibetan Plateau,exerts significant impacts on the precipitation and PW in summer,since large amounts of clouds are distributed along the mountain ranges.The study region is divided into four typical areas:the monsoon area in eastern Northwest China(NWE),the Qilian Mountains area(QM),the Tianshan Mountains area(TM),and the Source of Three Rivers area(STR).Over the four areas,cloud top height(6.3 km)and cloud base height(3.3 km)over NWE are higher,and precipitating clouds are thicker(7 km)in the single-layer clouds.Liquid water content decreases with increasing altitude,while the ice water content first increases and then decreases.Liquid water path is higher over NWE(0.11 kg m^(−2))than over TM and STR(0.05 kg m^(−2)),and the ice water path is mainly concentrated within the range of 0.025–0.055 kg m^(−2).The PEh values are distributed unevenly and affected evidently by the terrain.Although the PEh values in the four typical areas(0.3–0.6)are higher than those in other regions,the CWR is relatively abundant and has a higher exploitation potential.Therefore,it is well-founded to exploit CWR for alleviating water shortages in these areas of Northwest China in summer.

关键词： cloud water resource(CWR) precipitation efficiency of hydrometeors(PEh) precipitable water(PW) cloud Northwest China

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Light bottle transformer based large scale point cloud classification

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Optoelectronics Letters 2023年第6期19卷 377-384页

作者： XIE En ZHANG Zhiyong ZHANG Guodao CHEN Pingkuo GE Yisu College of Computer Information and Engineering Nanchang Institute of TechnologyNanchang 330044China School of Information Engineering East China University of TechnologyNanchang 330000China Department of Digital Media Technology Hangzhou Dianzi UniversityHangzhou 310023China Great Bay University Dongguan 523000China College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325100China

With the rapid development of computer vision,point clouds technique was widely used in practical applications,such as obstacle detection,roadside detection,smart city construction,etc.However,how to efficiently identify the large scale point clouds is still an open challenge.For relieving the large computation consumption and low accuracy problem in point cloud classification,a large scale point cloud classification framework based on light bottle transformer(light-Bot Net)is proposed.Firstly,the two-dimensional(2D)and three-dimensional(3D)feature values of large scale point cloud were extracted for constructing point cloud feature images,which employed the prior knowledge to normalize the point cloud features.Then,the feature images are input to the classification network,and the light-Bot Net network is applied for point cloud classification.It is an interesting attempt to combine the traditional image features with the transformer network.For proving the performance of the proposed method,the large scale point cloud benchmark Oakland 3D is utilized.In the experiments,the proposed method achieved 98.1%accuracy on the Oakland 3D dataset.Compared with the other methods,it can both reduce the memory consumption and improve the classification accuracy in large scale point cloud classification.

关键词： computer cloud point

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A cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

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Computer Systems Science & Engineering 2023年第4期45卷 857-868页

作者： Mohemmed Sha Department of Software Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The use of Amazon Web Services is growing rapidly as more users are adopting the technology.It has various functionalities that can be used by large corporates and individuals as well.Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions.In this research work,sentiment analysis is performed on the AWS Elastic Compute cloud(EC2)through Twitter data.The data is managed to the EC2 by using elastic load balancing.The collected data is subjected to preprocessing approaches to clean the data,and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative sentiments.High accuracy of 94.17%is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.

关键词： Sentiment analysis AWS cloud data analytics machine learning

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User Centric Block-Level Attribute Based Encryption in cloud Using Blockchains

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Computer Systems Science & Engineering 2022年第8期42卷 605-618页

作者： S.Godfrey Winster A.Siva Kumar R.Ramesh Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyChengalpattu603203India Department of Information Technology Sri Sai Ram Engineering CollegeChennai600044India Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India

cloud computing is a collection of distributed storage Network which can provide various services and store the data in the efficient manner.The advantages of cloud computing is its remote access where data can accessed in real time using Remote Method Innovation(RMI).The problem of data security in cloud environment is a major concern since the data can be accessed by any time by any user.Due to the lack of providing the efficient security the cloud computing they fail to achieve higher performance in providing the efficient service.To improve the performance in data security,the block chains are used for securing the data in the cloud environment.However,the traditional block chain technique are not suitable to provide efficient security to the cloud data stored in the cloud.In this paper,an efficient user centric block level Attribute Based Encryption(UCBL-ABE)scheme is presented to provide the efficient security of cloud data in cloud environment.The proposed approach performs data transaction by employing the block chain.The proposed system provides efficient privacy with access control to the user access according to the behavior of cloud user using Data Level Access Trust(DLAT).Based on DLAT,the user access has been restricted in the cloud environment.The proposed protocol is implemented in real time using Java programming language and uses IBM cloud.The implementation results justifies that the proposed system can able to provide efficient security to the data present in and cloud and also enhances the cloud performance.

关键词： cloud blockchain data security ABE block-level

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Scaling Notebooks as Re-configurable cloud Workflows

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Data Intelligence 2022年第2期4卷 409-425页

作者： Yuandou Wang Spiros Koulouzis Riccardo Bianchi Na Li Yifang Shi Joris Timmermans W.Daniel Kissling Zhiming Zhao Multiscale Networked Systems Informatics InstituteUniversity of Amsterdam1098XH AmsterdamThe Netherlands Life Watch ERIC Virtual Lab&InnovationCenter(VLIC)Science Park 9041098XH AmsterdamThe Netherlands Institute for Biodiversity and Ecosystem Dynamics(IBED) 1098xH AmsterdamThe Netherlands

Literate computing environments,such as the Jupyter(i.e.,Jupyter Notebooks,JupyterLab,and JupyterHub),have been widely used in scientific studies;they allow users to interactively develop scientific code,test algorithms,and describe the scientific narratives of the experiments in an integrated document.To scale up scientific analyses,many implemented Jupyter environment architectures encapsulate the whole Jupyter notebooks as reproducible units and autoscale them on dedicated remote infrastructures(e.g.,highperformance computing and cloud computing environments).The existing solutions are stl limited in many ways,e.g.,1)the workflow(or pipeline)is implicit in a notebook,and some steps can be generically used by different code and executed in parallel,but because of the tight cell structure,all steps in the Jupyter notebook have to be executed sequentially and lack of the flexibility of reusing the core code fragments,and 2)there are performance bottlenecks that need to improve the parallelism and scalability when handling extensive input data and complex computation.In this work,we focus on how to manage the workflow in a notebook seamlessly.We 1)encapsulate the reusable cells as RESTful services and containerize them as portal components,2)provide a composition tool for describing workflow logic of those reusable components,and 3)automate the execution on remote cloud infrastructure.Empirically,we validate the solution's usability via a use case from the Ecology and Earth Science domain,illustrating the processing of massive Light Detection and Ranging(LiDAR)data.The demonstration and analysis show that our method is feasible,but that it needs further improvement,especially on integrating distributed workflow scheduling,automatic deployment,and execution to develop as a mature approach.

关键词： Scientific experiments Jupyter Notebooks Workflow management Ecosystem structure data products cloud Scalability

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Factors Influencing Diurnal Variations of cloud and Precipitation in the Yushu Area of the Tibetan Plateau

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Journal of Meteorological Research 2022年第2期36卷 311-325页

作者： Bangjun CAO Shihua LYU Yu ZHANG Xianyu YANG Boliang LI Liang YUAN Meilin LI Plateau Atmosphere and Environment Key Laboratory of Sichuan Province School of Atmospheric SciencesChengdu University of Information TechnologyChengdu 610225

Using the cloud radar,ground observations,and ECMWF Reanalysis v5(ERA5)data,we investigate the factors influencing nighttime precipitation during summer in the Yushu area of the Tibetan Plateau(TP).The cloud top height(CTH),cloud base height(CBH),and liquid water content(LWC)are compared between non-precipitation and precipitation days.The results show that the average CTH on precipitation days in Yushu is below 10 km above ground level(AGL)in the daytime,whereas it exceeds 10 km AGL at night,with the maximum at 2300 BT(Beijing Time).The CBH is in-phase with the dewpoint spread.The precipitation intensity and CTH are in-phase with the LWC.The hourly averaged precipitation intensity and convective available potential energy in ERA5 reach their maximums at2100 BT,which is 3 h ahead of their observed counterparts.There is descending motion in the mid day on non-precipitation days,whereas there is ascending motion at night on precipitation days.In addition,the horizontal wind direction in the lower level(below 5000 m)shows clockwise rotation from morning to night.Wind shear occurs in the mid level of the atmosphere,accompanied by a subtropical westerly jet in the upper level.The difference in horizontal wind speed between 200 and 500 hPa is positively related to the LWC,thereby contributing to the formation of upper-level cloud.

关键词： diurnal variation cloud precipitation Tibetan Plateau

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Health Data Deduplication Using Window Chunking-Signature Encryption in cloud

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Intelligent Automation & Soft Computing 2023年第4期36卷 1079-1093页

作者： G.Neelamegam P.Marikkannu Department of Computer Science and Engineering Anna University Regional Campus CoimbatoreCoimbatore641046India

Due to the development of technology in medicine,millions of health-related data such as scanning the images are generated.It is a great challenge to store the data and handle a massive volume of data.Healthcare data is stored in the cloud-fog storage environments.This cloud-Fog based health model allows the users to get health-related data from different sources,and duplicated informa-tion is also available in the background.Therefore,it requires an additional sto-rage area,increase in data acquisition time,and insecure data replication in the environment.This paper is proposed to eliminate the de-duplication data using a window size chunking algorithm with a biased sampling-based bloomﬁlter and provide the health data security using the Advanced Signature-Based Encryp-tion(ASE)algorithm in the Fog-cloud Environment(WCA-BF+ASE).This WCA-BF+ASE eliminates the duplicate copy of the data and minimizes its sto-rage space and maintenance cost.The data is also stored in an efﬁcient and in a highly secured manner.The security level in the cloud storage environment Win-dows Chunking Algorithm(WSCA)has got 86.5%,two thresholds two divisors(TTTD)80%,Ordinal in Python(ORD)84.4%,Boom Filter(BF)82%,and the proposed work has got better security storage of 97%.And also,after applying the de-duplication process,the proposed method WCA-BF+ASE has required only less storage space for variousﬁle sizes of 10 KB for 200,400 MB has taken only 22 KB,and 600 MB has required 35 KB,800 MB has consumed only 38 KB,1000 MB has taken 40 KB of storage spaces.

关键词： Health data encryption chunks cloud fog deduplication bloomﬁlter Algorithm 3:Generation of Key

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A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation

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Frontiers of Computer Science 2023年第6期17卷 147-149页

作者： Shaolei LIU Xiaoyuan LUO Kexue FU Manning WANG Zhijian SONG Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai 200032China Digital Medical Research Center School of Basic Medical ScienceFudan UniversityShanghai 200032China

1 Introduction Deep neural networks have exhibited excellent performance in supervised tasks on point clouds,such as classification,segmentation[1]and registration[2].In supervised learning schemes,manual labeling of massive point clouds is needed for model training.However,point clouds captured from different scenarios exist inevitable distribution discrepancy,and model trained from one domain always generalize badly in another domain.To reduce the doamin distribution discrepancy,many studies[3–6]have emerged for point cloud unsupervised domain adaptation(UDA)by learning domain-invariant features,where[5]proposed using adaptive nodes to align the local features between the source and the target domains[3,4],and[6]proposed utilizing self-supervised tasks to help capture highly transferable feature representations.

关键词： point cloud utilizing

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