A New Encryption Mechanism Supporting the Update of Encrypted Data for Secure and Efficient Collaboration in the Cloud Environment

With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.

MatCloud-QE:基于云原生理念的高通量第一性原理计算程序包

国内开展第一性原理计算总是局限于某一两个商业软件,不仅需要软件使用许可,而且还需要下载、安装到本地,再通过人工部署到高性能计算集群才能开展使用,极大地限制了科研人员开展第一性原理计算。为了解决这个问题,作者团队基于Quantum ESPRESSO第一性原理计算内核,进一步开发了高通量第一性原理计算加速算法、参数智能推荐算法、输入结构统一算法、自动后处理引擎、组件化引擎、图形化引擎、更多物性计算功能以及MatCloud+接口等算法程序,形成了具有云原生特点的第一性原理计算程序包:MatCloud-QE;进一步通过接入MatCloud+材料云,使用户仅通过浏览器就可在线开展第一性原理计算和数据自动化采集和管理,极大地方便了用户开展第一性原理计算以及高通量计算筛选,且无需软件使用许可。以面向太阳能电池应用的无机钙钛矿Rb_(1-x)Cs_(x)PbI3的材料特性的第一性原理计算为例,说明通过MatCloud+材料云平台使用MatCloud-QE第一性原理计算程序包可进行能隙、态密度和光学性质的计算,计算结果不仅和文献报道结果相符,且整个计算过程减少人工干预次数可达90%以上,极大地提高了效率。目前,MatCloud-QE第一性原理计算程序包深度集成在MatCloud+材料云平台,用户通过云端才能使用,该模式为用户提供了丰富的计算资源和一站式第一性原理计算服务。

基于Spring Cloud的分布式医疗数据平台建设

为解决互联网时代医疗大数据的可靠存储问题,探索异构数据的有效处理方案,加强医疗数据检索能力。提出一种基于Spring Cloud的分布式医疗数据平台建设方案,该方案通过Spring Cloud技术构建分布式架构,选取MongoDB非关系型数据库作为分布式存储,同时对数据进行标准化、分词等操作,从而实现一个异构数据可存储、系统高可用、规模易扩展、检索高效率的医疗数据平台。

CT-CloudDetect:用于遥感卫星云检测的混合模型

云检测是在遥感卫星云图中检测云的任务。近年来,人们提出了基于深度学习的云检测方法,并取得了良好的性能。然而,现有的基于深度学习的云检测模型大多还是基于卷积神经网络(convolutional neural network,CNN),由于卷积运算的固有局部性,难以捕获长距离依赖关系。针对上述问题,文章提出一个基于CNN和ViT(Vision Transformer)的混合型云检测模型,并提出一种基于CNN和ViT的编码器,使网络具备捕捉局部和全局信息的能力。为了更好地融合语义和尺度不一致的特征,提出了一个双尺度注意力融合模块,通过注意力机制有选择地融合特征。此外,提出了轻量级路由解码器,该解码器通过路由结构降低模型复杂度。在3个公开云检测数据集上对模型进行了评估。大量实验表明,所提出的模型具有比现有模型更好的性能。

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

基于POC CLOUD智能管理云平台的即时检验教学探索

目的探讨使用POC CLOUD智能管理云平台培养住培学员掌握即时检验(point-of-care testing,POCT)各设备的工作原理、操作技能,使其具备一定的质量控制管理能力,保证结果的准确性。方法结合《住培学员规范化培训内容与标准(2022年版)》要求及科室POCT教学开展情况,利用POC CLOUD智能管理云平台,信息化、规范化地培养住培学员。结果通过POC CLOUD智能管理云平台管理住培学员操作POCT设备的资格准入,帮助住培学员快速了解轮转科室POCT设备的使用现状,提高住培学员的学习能力及质量控制能力,帮助住培学员分析与审核检验结果。结论基于POC CLOUD智能管理云平台的即时检验教学,有助于系统化培养住培学员,提升其在POCT操作和质量控制方面的能力,确保检验结果的准确性和可靠性,提高住培学员的综合素质。

A Blockchain and CP-ABE Based Access Control Scheme with Fine-Grained Revocation of Attributes in Cloud Health

The Access control scheme is an effective method to protect user data privacy.The access control scheme based on blockchain and ciphertext policy attribute encryption(CP–ABE)can solve the problems of single—point of failure and lack of trust in the centralized system.However,it also brings new problems to the health information in the cloud storage environment,such as attribute leakage,low consensus efficiency,complex permission updates,and so on.This paper proposes an access control scheme with fine-grained attribute revocation,keyword search,and traceability of the attribute private key distribution process.Blockchain technology tracks the authorization of attribute private keys.The credit scoring method improves the Raft protocol in consensus efficiency.Besides,the interplanetary file system(IPFS)addresses the capacity deficit of blockchain.Under the premise of hiding policy,the research proposes a fine-grained access control method based on users,user attributes,and file structure.It optimizes the data-sharing mode.At the same time,Proxy Re-Encryption(PRE)technology is used to update the access rights.The proposed scheme proved to be secure.Comparative analysis and experimental results show that the proposed scheme has higher efficiency and more functions.It can meet the needs of medical institutions.

Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion strategies.Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload pro-cessing.Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains unexplored.In this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed DC.The primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy.On the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming workload.The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

Quafu-Qcover:Explore combinatorial optimization problems on cloud-based quantum computers

We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

Quafu-RL:The cloud quantum computers based quantum reinforcement learning

With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.

基于Spring Cloud微服务架构的非物质文化遗产展示与学习系统的设计与实现

针对目前人民群众对非物质文化遗产认识不够全面,以及市面上利用系统软件来有效保护和传承非物质文化遗产的软件很缺乏等问题,结合相应软件开发技术和数字可视化技术,设计和实现了一款非物质文化遗产展示与学习系统以实现对非物质文化遗产项目的传承和保护创新。本系统以江西省范围内的主要非物质文化遗产项目为例,设置有移动App端和PC端,采用前后端分离的开发模式,以当前主流的Spring Cloud微服务架构技术为基础,结合SpringBoot框架,在华为云服务器上搭建起弹性可伸缩的后端服务。使用Uni-App开发移动端,以解决无法在Android、IOS及各大小程序平台运行的问题。经测试,本系统具有操作简单,易于使用、维护和扩展,高并发性等优点。

Automatic Extraction Method of 3D Feature Guidelines for Complex Cultural Relic Surfaces Based on Point Cloud

Cultural relics line graphic serves as a crucial form of traditional artifact information documentation,which is a simple and intuitive product with low cost of displaying compared with 3D models.Dimensionality reduction is undoubtedly necessary for line drawings.However,most existing methods for artifact drawing rely on the principles of orthographic projection that always cannot avoid angle occlusion and data overlapping while the surface of cultural relics is complex.Therefore,conformal mapping was introduced as a dimensionality reduction way to compensate for the limitation of orthographic projection.Based on the given criteria for assessing surface complexity,this paper proposed a three-dimensional feature guideline extraction method for complex cultural relic surfaces.A 2D and 3D combined factor that measured the importance of points on describing surface features,vertex weight,was designed.Then the selection threshold for feature guideline extraction was determined based on the differences between vertex weight and shape index distributions.The feasibility and stability were verified through experiments conducted on real cultural relic surface data.Results demonstrated the ability of the method to address the challenges associated with the automatic generation of line drawings for complex surfaces.The extraction method and the obtained results will be useful for line graphic drawing,displaying and propaganda of cultural relics.

基于RuoYi-Cloud的劳动教育平台建设

为帮助学生学习劳动文化、进行劳动实践,辅助教师精准地安排和开展劳动教学活动,设计且实现了一款基于RuoYi-Cloud框架的劳动教育系统,具有劳动教育管理、选课管理、在线学习、学业预警、系统监控、数据分析等功能,使用RuoYi-Cloud框架开发降低了开发和维护成本,提高了开发效率。采用微服务架构,实现对用户友好的界面和高度可扩展的后端功能,优化了系统的响应时间和资源利用率确保系统高效运行,系统的灵活性和扩展性为未来的功能扩展提供良好的基础。系统为劳动教育实施提供可靠解决方案,通过对系统的实际应用和用户反馈,系统对劳动教育领域有一定参考价值。

Security Implications of Edge Computing in Cloud Networks

Security issues in cloud networks and edge computing have become very common. This research focuses on analyzing such issues and developing the best solutions. A detailed literature review has been conducted in this regard. The findings have shown that many challenges are linked to edge computing, such as privacy concerns, security breaches, high costs, low efficiency, etc. Therefore, there is a need to implement proper security measures to overcome these issues. Using emerging trends, like machine learning, encryption, artificial intelligence, real-time monitoring, etc., can help mitigate security issues. They can also develop a secure and safe future in cloud computing. It was concluded that the security implications of edge computing can easily be covered with the help of new technologies and techniques.

Unveiling Cloud Vertical Structures over the Interior Tibetan Plateau through Anomaly Detection in Synergetic Lidar and Radar Observations

Cloud vertical structure(CVS)strongly affects atmospheric circulation and radiative transfer.Yet,long-term,groundbased observations are scarce over the Tibetan Plateau(TP)despite its vital role in global climate.This study utilizes ground-based lidar and Ka-band cloud profiling radar(KaCR)measurements at Yangbajain(YBJ),TP,from October 2021 to September 2022 to characterize cloud properties.A satisfactorily performing novel anomaly detection algorithm(LevelShiftAD)is proposed for lidar and KaCR profiles to identify cloud boundaries.Cloud base heights(CBH)retrieved from KaCR and lidar observations show good consistency,with a correlation coefficient of 0.78 and a mean difference of-0.06 km.Cloud top heights(CTH)derived from KaCR match the FengYun-4A and Himawari-8 products well.Thus,KaCR measurements serve as the primary dataset for investigating CVSs over the TP.Different diurnal cycles occur in summer and winter.The diurnal cycle is characterized by a pronounced increase in cloud occurrence frequency in the afternoon with an early-morning decrease in winter,while cloud amounts remain high all day,with scattered nocturnal increases in summer.Summer features more frequent clouds with larger geometrical thicknesses,a higher multi-layer ratio,and greater inter-cloud spacing.Around 26%of the cloud bases occur below 0.5 km.Winter exhibits a bimodal distribution of cloud base heights with peaks at 0-0.5 km and 2-2.5 km.Single-layer and geometrically thin clouds prevail at YBJ.This study enriches long-term measurements of CVSs over the TP,and the robust anomaly detection method helps quantify cloud macro-physical properties via synergistic lidar and radar observations.

Large Eddy Simulation of Vertical Structure and Size Density of Deep Layer Clouds

In a convective scheme featuring a discretized cloud size density, the assumed lateral mixing rate is inversely proportional to the exponential coefficient of plume size. This follows a typical assumption of-1, but it has unveiled inherent uncertainties, especially for deep layer clouds. Addressing this knowledge gap, we conducted comprehensive large eddy simulations and comparative analyses focused on terrestrial regions. Our investigation revealed that cloud formation adheres to the tenets of Bernoulli trials, illustrating power-law scaling that remains consistent regardless of the inherent deep layer cloud attributes existing between cloud size and the number of clouds. This scaling paradigm encompasses liquid, ice, and mixed phases in deep layer clouds. The exponent characterizing the interplay between cloud scale and number in the deep layer cloud, specifically for liquid, ice, or mixed-phase clouds, resembles that of shallow convection,but converges closely to zero. This convergence signifies a propensity for diminished cloud numbers and sizes within deep layer clouds. Notably, the infusion of abundant moisture and the release of latent heat by condensation within the lower atmospheric strata make substantial contributions. However, this role in ice phase formation is limited. The emergence of liquid and ice phases in deep layer clouds is facilitated by the latent heat and influenced by the wind shear inherent in the middle levels. These interrelationships hold potential applications in formulating parameterizations and post-processing model outcomes.

Intelligent Solution System for Cloud Security Based on Equity Distribution:Model and Algorithms

In the cloud environment,ensuring a high level of data security is in high demand.Data planning storage optimization is part of the whole security process in the cloud environment.It enables data security by avoiding the risk of data loss and data overlapping.The development of data flow scheduling approaches in the cloud environment taking security parameters into account is insufficient.In our work,we propose a data scheduling model for the cloud environment.Themodel is made up of three parts that together help dispatch user data flow to the appropriate cloudVMs.The first component is the Collector Agent whichmust periodically collect information on the state of the network links.The second one is the monitoring agent which must then analyze,classify,and make a decision on the state of the link and finally transmit this information to the scheduler.The third one is the scheduler who must consider previous information to transfer user data,including fair distribution and reliable paths.It should be noted that each part of the proposedmodel requires the development of its algorithms.In this article,we are interested in the development of data transfer algorithms,including fairness distribution with the consideration of a stable link state.These algorithms are based on the grouping of transmitted files and the iterative method.The proposed algorithms showthe performances to obtain an approximate solution to the studied problem which is an NP-hard(Non-Polynomial solution)problem.The experimental results show that the best algorithm is the half-grouped minimum excluding(HME),with a percentage of 91.3%,an average deviation of 0.042,and an execution time of 0.001 s.

Information Security in the Cloud: Emerging Trends and Challenges

This article explores the evolution of cloud computing, its advantages over traditional on-premises infrastructure, and its impact on information security. The study presents a comprehensive literature review covering various cloud infrastructure offerings and security models. Additionally, it deeply analyzes real-life case studies illustrating successful cloud migrations and highlights common information security threats in current cloud computing. The article concludes by offering recommendations to businesses to protect themselves from cloud data breaches and providing insights into selecting a suitable cloud services provider from an information security perspective.

Cloud Datacenter Selection Using Service Broker Policies:A Survey

Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.