云计算翻译

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一云多芯专业英文词汇

"一云多芯"是一个在云计算和半导体领域常用的术语，它主要描述了一种技术策略，即在一个云平台上运行多个不同的处理器或芯片。

这种策略的主要目的是提高系统的灵活性和可扩展性，同时也能降低硬件成本和维护复杂性。

在英文中，"一云多芯"可以翻译为"One Cloud, Multiple Cores"。

这个词汇通常用于描述云计算服务提供商（CSP）的产品和服务，这些服务允许客户在一个统一的云环境中使用多种不同的处理器或芯片。

以下是一些与"一云多芯"相关的专业英文词汇：1. Cloud Computing: 云计算是一种使用网络进行计算的模式，其中共享的软件和信息提供给计算机和其他设备按需提供。

2. Multi-Core Processors: 多核处理器是一种在一个集成电路上集成了两个或更多个处理器核心的微处理器。

3. Chipset: 芯片组是一组集成电路，包括中央处理器（CPU）、内存控制器、输入/输出（I/O）控制器等。

4. Virtualization: 虚拟化是一种技术，它可以使单个物理资源（如服务器、存储设备或网络）看起来像多个逻辑上的独立资源。

5. Scalability: 可扩展性是指系统或服务能够处理增加的工作负载的能力。

6. Cost-Efficiency: 成本效益是指通过比较投入和产出来评估一个项目或决策的价值。

7. Maintenance Complexity: 维护复杂性是指维护一个系统或服务所需的工作量和难度。

8. Hardware Diversity: 硬件多样性是指在一个系统中使用多种不同类型的硬件设备。

9. Performance Optimization: 性能优化是指通过调整系统配置或修改代码来提高系统性能的过程。

10. Flexibility: 灵活性是指系统或服务能够适应变化和应对新挑战的能力。

云计算基础知识

云计算基础知识对“云计算”，有人将它说得云山雾罩、玄乎其玄。

甚至，有人将云计算（Cloud Computing）翻译成“云雾计算”，让人啼笑皆非。

弄得好多人在问什么是云计算？什么是雾计算？这正说明好多人对于云计算还是一头雾水。

最近，大公司如MS、Google、IBM等都在炒作一个概念就是云计算，如IBM跟欧盟合作开展云计算，欧盟拨款1.7亿万欧元；Google与IBM联合力推云计算模式；Yahoo!也把宝押在了云计算上；我国也在无锡跟IBM公司联合建立了一个云计算中心；有人说微软收购Yahoo！一个重要的考虑就是在Yahoo在云计算方面的领先地位，多少有点儿道理。

那么，究竟什么是云计算？简单的说，云就是互联网。

如果没有互联网，什么都做不到。

有了互联网以后，你可以把你的数据，把你的服务，在公开的标准的前提下，把所有的数据存到云端。

“云计算”概念由Google提出，一如其名，这是一个美丽的网络应用模式。

云计算时代，可以抛弃U盘等移动设备，只需要进入Google Docs页面，新建文档，编辑内容，然后，直接将文档的URL分享给你的朋友或者上司，他可以直接打开浏览器访问URL。

我们再也不用担心因PC硬盘的损坏而发生资料丢失事件。

基本概念和特点1 .1 云计算的定义英译：cloud；cloud computing；cloud computer；cloud-based。

“云计算”（Cloud Computing）是分布式处理（Distributed Computing）、并行处理（Parallel Computing）和网格计算（Grid Computing）的发展，或者说是这些计算机科学概念的商业实现。

对于云计算的定义，开发者和信息技术人员与最终用户可能存在些许不同。

对那些开发和管理计算机系统的人来说，云计算意味着服务器能力在水平方向上的可扩展性；技术层面面临的挑战是：开发操作系统和应用程序来管理运行过程中的规模变化，同时保持相应的机制对最终用户不可见。

科技术语在翻译中的隐喻现象

科技术语在翻译中的隐喻现象科技技术是现代社会中一个非常重要的领域，其中包含着大量的专业术语。

在翻译科技技术相关的文字时，经常会遇到一些隐喻现象。

隐喻是一种修辞手法，通过将一个事物或概念比喻为另一个事物或概念来传递某种意义。

在翻译中，隐喻是一种常见的语言转化方式，用于表达某些无法直接表达的含义。

一、科技技术词汇的隐喻现象科技技术词汇中的隐喻现象主要包括以下几种情况：1. 抽象概念的隐喻科技技术中的一些抽象概念常常通过隐喻来表达。

"cloud computing"（云计算）中的"cloud"（云）就是一个隐喻，它指的是将计算资源集中在远程服务器上，用户只需通过互联网访问这些远程服务器即可获取所需的计算能力。

2. 物理概念的隐喻科技技术中的一些物理概念也常常通过隐喻来表达。

"firewall"（防火墙）一词中的"fire"（火）就是一个隐喻，它指的是阻止非法入侵和保护系统免受恶意攻击的网络安全技术。

3. 动物隐喻科技技术词汇中的一些概念也常常通过动物隐喻来表示。

"bug"（错误）一词中的"bug"（虫子）就是一个隐喻，它用来描述软件中的错误或问题。

1. 保留隐喻有时候，原文中的隐喻在目标语言中也不存在，但是又没有合适的翻译方式来替代。

此时，可以选择保留隐喻，以保持原文的风格和语言特点。

"cloud computing"（云计算）一词可以直接保留不翻译。

3. 解释隐喻有时候，原文中的隐喻在目标语言中存在，但读者可能不熟悉这种隐喻的含义。

此时，可以选择解释隐喻，以便读者能够理解其在原文中的意义。

"architecture"（架构）一词可以解释为"软件或系统的设计结构"。

4. 转化隐喻有时候，原文中的隐喻在目标语言中不存在，但是可以通过转化表达成其他形式，以传达相似的意义。

云计算下的翻译模式研究

2．信息技术的发展，使得内容需求的翻译大幅增长随着信息技术，特别是w eb2．o的应用，使内容产业需求迅速增长，同时也带来了翻译需求的增长。

内容需求导致的翻译特点在于内容更新及时，尽管许多对于信息及内容的需求是暂时的，并不构成一个完整的传统意义上的正式的翻译任务，但是内容的快速更新和信息的快速传播，却意味着更快的翻译速度和翻译任务的几何级增长。

因此新的翻译模式必须具备能应对频繁、快速翻译所导致的译员数量增长和水平需要提高的特点。

总之，信息时代新的竞争环境变化，迫使翻译产业朝更低廉、更快速和更高质量方向发展。

二、翻译产业环境下的翻译模式比较在探讨了翻译产业的环境需求后，我们再来审视一下目前翻译产业的主要供应模式。

在W eb2．0的今天，机器翻译和众包翻译可以看成两种主要的信息技术类翻译模式，尽管有人提出了云计算翻译也可视为一种主要的翻译模式，但我们认为云计算翻译仅仅是一种翻译技术和翻译环境，并没有改变翻译产业的主要流程与各方参与者，因此我们仅对机器翻译和众包翻译进行分析。

1．机器翻译机器翻译(M T)使用计算机软件将文本或谈话从一种自然语言(源语言)翻译到另一种语言(目标语言)，根据不同的架构主要包括基于规则的机器翻译(R B M T)，基于统计的机器翻译(SM T)，基于实例的机器翻译(E B M T)。

经过几十年的发展，如今的机器翻译进入到了开放式的翻译阶段，如s Y S．，I R A N，L a nguage w eaver以及A ppTek TraJl Sphere等(A nast asi ou＆G upt a，201l：637)，可以实现在线的同步翻译，是目前翻译产业一个重要的应用模式。

2．众包翻译2007年，著名的社交网站Fac ebook发动双语用户志愿为网站进行翻译，并一举成功(M eer，2010)，开启了众包翻译的时代。

众包翻译模式迅速在社交媒体、非盈利性组织、文化传播、政府机构等方面得到了许多应用，并迅速成为翻译产业的一个新兴的应用模式。

5月短语翻译和百科知识

日积月累.180501英汉短语翻译1.中国品牌日Chinese Brands Day2.云计算Cloud computing3.房屋限购Purchase limits for real estate4.网络强国A country with strong cyber technology5.Mainstream media主流媒体6.Electronic social security card电子社保卡7.High-speed aerotrain高速气动悬浮列车8.Targeted poverty alleviation精准脱贫9.Leap year闰年百科词条1.“希腊三贤”指的是哪三位伟大的哲学家？苏格拉底，柏拉图，亚里士多德2.被视为近代西方哲学的奠基之作是哪本书？《第一哲学沉思录》3.“人是万物的尺度”这句话是哪位哲学家首次提出？普罗泰戈拉4.亚里士多德认为最理想的政府是（）阶级执政。

中产阶级5.被黑格尔称作“现代哲学之父”的哲学家是笛卡尔日积月累.180502英汉短语翻译1.大众创新，万众创业mass entrepreneurship and innovation2.虚拟现实Virtual Reality(VR)3.新时代中国特色社会主义socialism with Chinese characterism in a new era4.孔子学院Confucius Institute5.网红经济Internet celebrity economy6.dynamic equivalence动态对等7.back translation回译8.fight corruption and build a clean government反腐倡廉9.CAT计算机辅助翻译10.to teach fish to swim班门弄斧日积月累.180503英汉短语翻译隐形贫困人口the invisible poverty-stricken population/the invisible poor国家安全意识the national security awareness政府集中采购centralized government procurement比特币Bitcoin人工智能artificial intelligenceharmony without uniformity和而不同zero-sum mentality零和博弈ppp政府和社会资本合作（public-private partnership）bond default债券违约Translationese翻译腔日积月累.180504英汉短语翻译1.三大攻坚战three tough battles2.征税清单tariff list3.人才争夺战talent scramble battle4.全面二胎政策the universal second-child policy5.军事分界线military demarcation line（MDL）6.QR code快速响应矩阵码7.sponge city海绵城市8.IPO首次公开募股9.autonomous driving technology自动驾驶技术10.Onomatopoeia拟声法百科知识1.下列我国哪个古迹被誉为“世界八大奇迹”（C）A.万里长城B.乐山大佛C.秦始皇兵马俑D.敦煌2.石头城是对我国哪座城市的美称（B）A.南昌B.南京C.拉萨D.西安3.请问：火车连续发出两声长鸣，这表示（C）A.前进B.停留C.倒退D.故障4.我国第一座国家森林公园是（C）A.武夷山B.长白山C.张家界D.九寨沟5.我国古代“十恶不赦”中的首恶是（D）A.不义B.不道C.内乱D.谋反日积月累.150505英汉短语翻译新一线城市new first-tier city暂定税率provisional tax rate十九大the19th National Congress of the Communist Party of China(CPC)朝鲜半岛无核化denuclearization of the Korean Peninsula长江考察trip along the Yangtze Riverthe six-party talks六方会谈Island-wide quota policy全域限购政策SOHO小型家居办公literal translation字面翻译；直译over-translation超额翻译日积月累.180507英汉短语翻译1.公积金provident fund2.综合国力comprehensive national strength3.人脸识别facial recognition4.物联网the Internet of Things(IoT)5.移动支付mobile payment6.一带一路the Belt and Road7.电子商务e-commerce8.限购令limited purchasing order9.自主知识产权Independent Intellectual Property Rights10.马克思主义Marxism11.birth defect出生缺陷12.live within their means量入为出13.programmer motivator程序员鼓励师14.rural vitalization strategy乡村振兴战略15.bilateral interpreting双边传译16.covert translation隐形翻译17.double First-Class initiative双一流18.CTO首席技术官19.VAT增值税20.APEC亚太经合组织百科知识：1.第一次世界大战的开战时间是（B）A.1910B.1914C.1939D.19402.下列农民起义哪次是洪秀全领导的（D）A.大泽乡起义B.黄巾起义C.赤眉起义D.金田起义3.下列清朝皇帝中哪位是末代皇帝（A）A.宣统B.光绪C.同治D.道光4.中华人民共和国国旗五星红旗的设计者是（C）A.毛泽东B.周恩来C.曾联松D.梁思成5.我国最早的一部医学理论著作是（B）A.《本草纲目》B.《黄帝内经》C.《千金方》D.《伤寒杂病论》日积月累.180508英汉短语翻译1.软实力soft power2.硕士生导师supervisor of postgraduate3.人才流失brain drain4.不要在错误的道路上越走越远refrain from going further down the wrong path5.21世纪数字丝绸之路a digital silk road of21st century6.down payment首付7.demographic dividend人口红利8.two centenary goals“两个一百年”奋斗目标9.Corpora语料库10.domesticating translation归化翻译日积月累.180509英汉短语翻译1.网络安全cyberspace security2.协同发展develop in a coordinated way3.朝鲜半岛局势the situation on the Korean Peninsula4.前沿领域cutting-edge areas5.谅解备忘录memorandum of understanding6.TPP跨太平洋伙伴关系协定7.independent intellectual property rights自主知识产权8.Silk Road Spirit丝路精神9.Abusive translation滥译10.Exoticism异国情调百科知识1.我国古代诗歌史上被称为“双璧”的一篇是《孔雀东南飞》，另一篇是（A）A.《木兰诗》B.《木兰辞》C.《离骚》D.《格萨尔王》2.著名的《大卫》像是谁的作品（C）A.拉斐尔B.达.芬奇C.米开朗琪罗D.阿古斯特尔3.现在美国国旗星条旗上有多少颗星（C）A.25B.30C.50D.604.有的学校实行学分制，在我国最早提倡学分制的是（B）A.鲁迅B.蔡元培C.吴玉章D.毛泽东5.相传我国古代能作“掌上飞”的是（B）A.杨玉环B.赵飞燕C.西施D.貂蝉日积月累.180510英汉短语翻译词条1.“三步走”战略Three-Step Development Strategy2.全面建成小康社会Build a moderately prosperous society3.积分落户points-based household4.按揭贷款mortgage loans5.朝韩首脑会晤inter-Korean summit6.IMF国际货币基金组织7.public rental housing公租房8.capability of independent innovation自主创新能力9.semantic translation语义翻译10.idiomatic translation语义翻译日积月累.180512英汉短语互译1.自由贸易试验区pilot free trade zone2.近海防御offshore waters defense3.医患纠纷patient-doctor disputes4.量子通信quantum communication5.春联Spring Festival Couplets6.Negative list负面清单7.intelligent vehicles智能汽车8.THAAD末端高空区域防御系统（萨德）9.pay by installment分期支付10.the Renaissance文艺复兴日积月累.180514英汉短语互译1.交流互鉴exchanges and mutual learning2.经济全球化economic globalization3.监测网络monitoring network4.伊朗核协议Iran nuclear deal5.工匠精神craftsmanship spirit6.bubble economy泡沫经济7.special effect特效8.Ferrari法拉利9.F.I.T国际翻译工作者联合会10.arms race军备竞赛日积月累.180515英汉短语翻译1.战略沟通strategic communication2.稳中求进to make progress while ensuring stability3.基础设施建设infrastructure construction4.反倾销措施anti-dumping measures5.轮值主席rotating presidency6.FTA自由贸易协定7.leap year闰年8.health resort疗养胜地，养生度假村9.singe one's wings损害自己的名誉或利益10.cohesion and coherence衔接与连贯日积月累.180516英汉短语翻译国产航母domestically built aircraft carrier退耕还林return cultivated land to forest or pastures传销pyramid selling房子是用来住的，不是用来炒的housing is for accommodation rather than speculation中等收入陷阱middle-income traphome with joint property rights共有产权住房the Doctrine of Mean《中庸》OTC非处方药cross-border e-commerce境外电子商务vape电子烟百科知识1.“月上柳梢头，人约黄昏后。

云计算外文翻译参考文献

云计算外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)原文:Technical Issues of Forensic Investigations in Cloud Computing EnvironmentsDominik BirkRuhr-University BochumHorst Goertz Institute for IT SecurityBochum, GermanyRuhr-University BochumHorst Goertz Institute for IT SecurityBochum, GermanyAbstract—Cloud Computing is arguably one of the most discussedinformation technologies today. It presents many promising technological and economical opportunities. However, many customers remain reluctant to move their business IT infrastructure completely to the cloud. One of their main concerns is Cloud Security and the threat of the unknown. Cloud Service Providers(CSP) encourage this perception by not letting their customers see what is behind their virtual curtain. A seldomly discussed, but in this regard highly relevant open issue is the ability to perform digital investigations. This continues to fuel insecurity on the sides of both providers and customers. Cloud Forensics constitutes a new and disruptive challenge for investigators. Due to the decentralized nature of data processing in the cloud, traditional approaches to evidence collection and recovery are no longer practical. This paper focuses on the technical aspects of digital forensics in distributed cloud environments. We contribute by assessing whether it is possible for the customer of cloud computing services to perform a traditional digital investigation from a technical point of view. Furthermore we discuss possible solutions and possible new methodologies helping customers to perform such investigations.I. INTRODUCTIONAlthough the cloud might appear attractive to small as well as to large companies, it does not come along without its own unique problems. Outsourcing sensitive corporate data into the cloud raises concerns regarding the privacy and security of data. Security policies, companies main pillar concerning security, cannot be easily deployed into distributed, virtualized cloud environments. This situation is further complicated by the unknown physical location of the companie’s assets. Normally,if a security incident occurs, the corporate security team wants to be able to perform their own investigation without dependency on third parties. In the cloud, this is not possible anymore: The CSP obtains all the power over the environmentand thus controls the sources of evidence. In the best case, a trusted third party acts as a trustee and guarantees for the trustworthiness of the CSP. Furthermore, the implementation of the technical architecture and circumstances within cloud computing environments bias the way an investigation may be processed. In detail, evidence data has to be interpreted by an investigator in a We would like to thank the reviewers for the helpful comments and Dennis Heinson (Center for Advanced Security Research Darmstadt - CASED) for the profound discussions regarding the legal aspects of cloud forensics. proper manner which is hardly be possible due to the lackof circumstantial information. For auditors, this situation does not change: Questions who accessed specific data and information cannot be answered by the customers, if no corresponding logs are available. With the increasing demand for using the power of the cloud for processing also sensible information and data, enterprises face the issue of Data and Process Provenance in the cloud [10]. Digital provenance, meaning meta-data that describes the ancestry or history of a digital object, is a crucial feature for forensic investigations. In combination with a suitable authentication scheme, it provides information about who created and who modified what kind of data in the cloud. These are crucial aspects for digital investigations in distributed environments such as the cloud. Unfortunately, the aspects of forensic investigations in distributed environment have so far been mostly neglected by the research community. Current discussion centers mostly around security, privacy and data protection issues [35], [9], [12]. The impact of forensic investigations on cloud environments was little noticed albeit mentioned by the authors of [1] in 2009: ”[...] to our knowledge, no research has been published on how cloud computing environments affect digital artifacts,and on acquisition logistics and legal issues related to cloud computing env ironments.” This statement is also confirmed by other authors [34], [36], [40] stressing that further research on incident handling, evidence tracking and accountability in cloud environments has to be done. At the same time, massive investments are being made in cloud technology. Combined with the fact that information technology increasingly transcendents peoples’ private and professional life, thus mirroring more and more of peoples’actions, it becomes apparent that evidence gathered from cloud environments will be of high significance to litigation or criminal proceedings in the future. Within this work, we focus the notion of cloud forensics by addressing the technical issues of forensics in all three major cloud service models and consider cross-disciplinary aspects. Moreover, we address the usability of various sources of evidence for investigative purposes and propose potential solutions to the issues from a practical standpoint. This work should be considered as a surveying discussion of an almost unexplored research area. The paper is organized as follows: We discuss the related work and the fundamental technical background information of digital forensics, cloud computing and the fault model in section II and III. In section IV, we focus on the technical issues of cloud forensics and discuss the potential sources and nature of digital evidence as well as investigations in XaaS environments including thecross-disciplinary aspects. We conclude in section V.II. RELATED WORKVarious works have been published in the field of cloud security and privacy [9], [35], [30] focussing on aspects for protecting data in multi-tenant, virtualized environments. Desired security characteristics for current cloud infrastructures mainly revolve around isolation of multi-tenant platforms [12], security of hypervisors in order to protect virtualized guest systems and secure network infrastructures [32]. Albeit digital provenance, describing the ancestry of digital objects, still remains a challenging issue for cloud environments, several works have already been published in this field [8], [10] contributing to the issues of cloud forensis. Within this context, cryptographic proofs for verifying data integrity mainly in cloud storage offers have been proposed,yet lacking of practical implementations [24], [37], [23]. Traditional computer forensics has already well researched methods for various fields of application [4], [5], [6], [11], [13]. Also the aspects of forensics in virtual systems have been addressed by several works [2], [3], [20] including the notionof virtual introspection [25]. In addition, the NIST already addressed Web Service Forensics [22] which has a huge impact on investigation processes in cloud computing environments. In contrast, the aspects of forensic investigations in cloud environments have mostly been neglected by both the industry and the research community. One of the first papers focusing on this topic was published by Wolthusen [40] after Bebee et al already introduced problems within cloud environments [1]. Wolthusen stressed that there is an inherent strong need for interdisciplinary work linking the requirements and concepts of evidence arising from the legal field to what can be feasibly reconstructed and inferred algorithmically or in an exploratory manner. In 2010, Grobauer et al [36] published a paper discussing the issues of incident response in cloud environments - unfortunately no specific issues and solutions of cloud forensics have been proposed which will be done within this work.III. TECHNICAL BACKGROUNDA. Traditional Digital ForensicsThe notion of Digital Forensics is widely known as the practice of identifying, extracting and considering evidence from digital media. Unfortunately, digital evidence is both fragile and volatile and therefore requires the attention of special personnel and methods in order to ensure that evidence data can be proper isolated and evaluated. Normally, the process of a digital investigation can be separated into three different steps each having its own specificpurpose:1) In the Securing Phase, the major intention is the preservation of evidence for analysis. The data has to be collected in a manner that maximizes its integrity. This is normally done by a bitwise copy of the original media. As can be imagined, this represents a huge problem in the field of cloud computing where you never know exactly where your data is and additionallydo not have access to any physical hardware. However, the snapshot technology, discussed in section IV-B3, provides a powerful tool to freeze system states and thus makes digital investigations, at least in IaaS scenarios, theoretically possible.2) We refer to the Analyzing Phase as the stage in which the data is sifted and combined. It is in this phase that the data from multiple systems or sources is pulled together to create as complete a picture and event reconstruction as possible. Especially in distributed system infrastructures, this means that bits and pieces of data are pulled together for deciphering the real story of what happened and for providing a deeper look into the data.3) Finally, at the end of the examination and analysis of the data, the results of the previous phases will be reprocessed in the Presentation Phase. The report, created in this phase, is a compilation of all the documentation and evidence from the analysis stage. The main intention of such a report is that it contains all results, it is complete and clear to understand. Apparently, the success of these three steps strongly depends on the first stage. If it is not possible to secure the complete set of evidence data, no exhaustive analysis will be possible. However, in real world scenarios often only a subset of the evidence data can be secured by the investigator. In addition, an important definition in the general context of forensics is the notion of a Chain of Custody. This chain clarifies how and where evidence is stored and who takes possession of it. Especially for cases which are brought to court it is crucial that the chain of custody is preserved.B. Cloud ComputingAccording to the NIST [16], cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal CSP interaction. The new raw definition of cloud computing brought several new characteristics such as multi-tenancy, elasticity, pay-as-you-go and reliability. Within this work, the following three models are used: In the Infrastructure asa Service (IaaS) model, the customer is using the virtual machine provided by the CSP for installing his own system on it. The system can be used like any other physical computer with a few limitations. However, the additive customer power over the system comes along with additional security obligations. Platform as a Service (PaaS) offerings provide the capability to deploy application packages created using the virtual development environment supported by the CSP. For the efficiency of software development process this service model can be propellent. In the Software as a Service (SaaS) model, the customer makes use of a service run by the CSP on a cloud infrastructure. In most of the cases this service can be accessed through an API for a thin client interface such as a web browser. Closed-source public SaaS offers such as Amazon S3 and GoogleMail can only be used in the public deployment model leading to further issues concerning security, privacy and the gathering of suitable evidences. Furthermore, two main deployment models, private and public cloud have to be distinguished. Common public clouds are made available to the general public. The corresponding infrastructure is owned by one organization acting as a CSP and offering services to its customers. In contrast, the private cloud is exclusively operated for an organization but may not provide the scalability and agility of public offers. The additional notions of community and hybrid cloud are not exclusively covered within this work. However, independently from the specific model used, the movement of applications and data to the cloud comes along with limited control for the customer about the application itself, the data pushed into the applications and also about the underlying technical infrastructure.C. Fault ModelBe it an account for a SaaS application, a development environment (PaaS) or a virtual image of an IaaS environment, systems in the cloud can be affected by inconsistencies. Hence, for both customer and CSP it is crucial to have the ability to assign faults to the causing party, even in the presence of Byzantine behavior [33]. Generally, inconsistencies can be caused by the following two reasons:1) Maliciously Intended FaultsInternal or external adversaries with specific malicious intentions can cause faults on cloud instances or applications. Economic rivals as well as former employees can be the reason for these faults and state a constant threat to customers and CSP. In this model, also a malicious CSP is included albeit he isassumed to be rare in real world scenarios. Additionally, from the technical point of view, the movement of computing power to a virtualized, multi-tenant environment can pose further threads and risks to the systems. One reason for this is that if a single system or service in the cloud is compromised, all other guest systems and even the host system are at risk. Hence, besides the need for further security measures, precautions for potential forensic investigations have to be taken into consideration.2) Unintentional FaultsInconsistencies in technical systems or processes in the cloud do not have implicitly to be caused by malicious intent. Internal communication errors or human failures can lead to issues in the services offered to the costumer(i.e. loss or modification of data). Although these failures are not caused intentionally, both the CSP and the customer have a strong intention to discover the reasons and deploy corresponding fixes.IV. TECHNICAL ISSUESDigital investigations are about control of forensic evidence data. From the technical standpoint, this data can be available in three different states: at rest, in motion or in execution. Data at rest is represented by allocated disk space. Whether the data is stored in a database or in a specific file format, it allocates disk space. Furthermore, if a file is deleted, the disk space is de-allocated for the operating system but the data is still accessible since the disk space has not been re-allocated and overwritten. This fact is often exploited by investigators which explore these de-allocated disk space on harddisks. In case the data is in motion, data is transferred from one entity to another e.g. a typical file transfer over a network can be seen as a data in motion scenario. Several encapsulated protocols contain the data each leaving specific traces on systems and network devices which can in return be used by investigators. Data can be loaded into memory and executed as a process. In this case, the data is neither at rest or in motion but in execution. On the executing system, process information, machine instruction and allocated/de-allocated data can be analyzed by creating a snapshot of the current system state. In the following sections, we point out the potential sources for evidential data in cloud environments and discuss the technical issues of digital investigations in XaaS environmentsas well as suggest several solutions to these problems.A. Sources and Nature of EvidenceConcerning the technical aspects of forensic investigations, the amount of potential evidence available to the investigator strongly diverges between thedifferent cloud service and deployment models. The virtual machine (VM), hosting in most of the cases the server application, provides several pieces of information that could be used by investigators. On the network level, network components can provide information about possible communication channels between different parties involved. The browser on the client, acting often as the user agent for communicating with the cloud, also contains a lot of information that could be used as evidence in a forensic investigation. Independently from the used model, the following three components could act as sources for potential evidential data.1) Virtual Cloud Instance: The VM within the cloud, where i.e. data is stored or processes are handled, contains potential evidence [2], [3]. In most of the cases, it is the place where an incident happened and hence provides a good starting point for a forensic investigation. The VM instance can be accessed by both, the CSP and the customer who is running the instance. Furthermore, virtual introspection techniques [25] provide access to the runtime state of the VM via the hypervisor and snapshot technology supplies a powerful technique for the customer to freeze specific states of the VM. Therefore, virtual instances can be still running during analysis which leads to the case of live investigations [41] or can be turned off leading to static image analysis. In SaaS and PaaS scenarios, the ability to access the virtual instance for gathering evidential information is highly limited or simply not possible.2) Network Layer: Traditional network forensics is knownas the analysis of network traffic logs for tracing events that have occurred in the past. Since the different ISO/OSI network layers provide several information on protocols and communication between instances within as well as with instances outside the cloud [4], [5], [6], network forensics is theoretically also feasible in cloud environments. However in practice, ordinary CSP currently do not provide any log data from the network components used by the customer’s instances or applications. For instance, in case of a malware infection of an IaaS VM, it will be difficult for the investigator to get any form of routing information and network log datain general which is crucial for further investigative steps. This situation gets even more complicated in case of PaaS or SaaS. So again, the situation of gathering forensic evidence is strongly affected by the support the investigator receives from the customer and the CSP.3) Client System: On the system layer of the client, it completely depends on the used model (IaaS, PaaS, SaaS) if and where potential evidence could beextracted. In most of the scenarios, the user agent (e.g. the web browser) on the client system is the only application that communicates with the service in the cloud. This especially holds for SaaS applications which are used and controlled by the web browser. But also in IaaS scenarios, the administration interface is often controlled via the browser. Hence, in an exhaustive forensic investigation, the evidence data gathered from the browser environment [7] should not be omitted.a) Browser Forensics: Generally, the circumstances leading to an investigation have to be differentiated: In ordinary scenarios, the main goal of an investigation of the web browser is to determine if a user has been victim of a crime. In complex SaaS scenarios with high client-server interaction, this constitutes a difficult task. Additionally, customers strongly make use of third-party extensions [17] which can be abused for malicious purposes. Hence, the investigator might want to look for malicious extensions, searches performed, websites visited, files downloaded, information entered in forms or stored in local HTML5 stores, web-based email contents and persistent browser cookies for gathering potential evidence data. Within this context, it is inevitable to investigate the appearance of malicious JavaScript [18] leading to e.g. unintended AJAX requests and hence modified usage of administration interfaces. Generally, the web browser contains a lot of electronic evidence data that could be used to give an answer to both of the above questions - even if the private mode is switched on [19].B. Investigations in XaaS EnvironmentsTraditional digital forensic methodologies permit investigators to seize equipment and perform detailed analysis on the media and data recovered [11]. In a distributed infrastructure organization like the cloud computing environment, investigators are confronted with an entirely different situation. They have no longer the option of seizing physical data storage. Data and processes of the customer are dispensed over an undisclosed amount of virtual instances, applications and network elements. Hence, it is in question whether preliminary findings of the computer forensic community in the field of digital forensics apparently have to be revised and adapted to the new environment. Within this section, specific issues of investigations in SaaS, PaaS and IaaS environments will be discussed. In addition, cross-disciplinary issues which affect several environments uniformly, will be taken into consideration. We also suggest potential solutions to the mentioned problems.1) SaaS Environments: Especially in the SaaS model, the customer does notobtain any control of the underlying operating infrastructure such as network, servers, operating systems or the application that is used. This means that no deeper view into the system and its underlying infrastructure is provided to the customer. Only limited userspecific application configuration settings can be controlled contributing to the evidences which can be extracted fromthe client (see section IV-A3). In a lot of cases this urges the investigator to rely on high-level logs which are eventually provided by the CSP. Given the case that the CSP does not run any logging application, the customer has no opportunity to create any useful evidence through the installation of any toolkit or logging tool. These circumstances do not allow a valid forensic investigation and lead to the assumption that customers of SaaS offers do not have any chance to analyze potential incidences.a) Data Provenance: The notion of Digital Provenance is known as meta-data that describes the ancestry or history of digital objects. Secure provenance that records ownership and process history of data objects is vital to the success of data forensics in cloud environments, yet it is still a challenging issue today [8]. Albeit data provenance is of high significance also for IaaS and PaaS, it states a huge problem specifically for SaaS-based applications: Current global acting public SaaS CSP offer Single Sign-On (SSO) access control to the set of their services. Unfortunately in case of an account compromise, most of the CSP do not offer any possibility for the customer to figure out which data and information has been accessed by the adversary. For the victim, this situation can have tremendous impact: If sensitive data has been compromised, it is unclear which data has been leaked and which has not been accessed by the adversary. Additionally, data could be modified or deleted by an external adversary or even by the CSP e.g. due to storage reasons. The customer has no ability to proof otherwise. Secure provenance mechanisms for distributed environments can improve this situation but have not been practically implemented by CSP [10]. Suggested Solution: In private SaaS scenarios this situation is improved by the fact that the customer and the CSP are probably under the same authority. Hence, logging and provenance mechanisms could be implemented which contribute to potential investigations. Additionally, the exact location of the servers and the data is known at any time. Public SaaS CSP should offer additional interfaces for the purpose of compliance, forensics, operations and security matters to their customers. Through an API, the customers should have the ability to receive specific information suchas access, error and event logs that could improve their situation in case of aninvestigation. Furthermore, due to the limited ability of receiving forensic information from the server and proofing integrity of stored data in SaaS scenarios, the client has to contribute to this process. This could be achieved by implementing Proofs of Retrievability (POR) in which a verifier (client) is enabled to determine that a prover (server) possesses a file or data object and it can be retrieved unmodified [24]. Provable Data Possession (PDP) techniques [37] could be used to verify that an untrusted server possesses the original data without the need for the client to retrieve it. Although these cryptographic proofs have not been implemented by any CSP, the authors of [23] introduced a new data integrity verification mechanism for SaaS scenarios which could also be used for forensic purposes.2) PaaS Environments: One of the main advantages of the PaaS model is that the developed software application is under the control of the customer and except for some CSP, the source code of the application does not have to leave the local development environment. Given these circumstances, the customer obtains theoretically the power to dictate how the application interacts with other dependencies such as databases, storage entities etc. CSP normally claim this transfer is encrypted but this statement can hardly be verified by the customer. Since the customer has the ability to interact with the platform over a prepared API, system states and specific application logs can be extracted. However potential adversaries, which can compromise the application during runtime, should not be able to alter these log files afterwards. Suggested Solution:Depending on the runtime environment, logging mechanisms could be implemented which automatically sign and encrypt the log information before its transfer to a central logging server under the control of the customer. Additional signing and encrypting could prevent potential eavesdroppers from being able to view and alter log data information on the way to the logging server. Runtime compromise of an PaaS application by adversaries could be monitored by push-only mechanisms for log data presupposing that the needed information to detect such an attack are logged. Increasingly, CSP offering PaaS solutions give developers the ability to collect and store a variety of diagnostics data in a highly configurable way with the help of runtime feature sets [38].3) IaaS Environments: As expected, even virtual instances in the cloud get compromised by adversaries. Hence, the ability to determine how defenses in the virtual environment failed and to what extent the affected systems havebeen compromised is crucial not only for recovering from an incident. Also forensic investigations gain leverage from such information and contribute to resilience against future attacks on the systems. From the forensic point of view, IaaS instances do provide much more evidence data usable for potential forensics than PaaS and SaaS models do. This fact is caused throughthe ability of the customer to install and set up the image for forensic purposes before an incident occurs. Hence, as proposed for PaaS environments, log data and other forensic evidence information could be signed and encrypted before itis transferred to third-party hosts mitigating the chance that a maliciously motivated shutdown process destroys the volatile data. Although, IaaS environments provide plenty of potential evidence, it has to be emphasized that the customer VM is in the end still under the control of the CSP. He controls the hypervisor which is e.g. responsible for enforcing hardware boundaries and routing hardware requests among different VM. Hence, besides the security responsibilities of the hypervisor, he exerts tremendous control over how customer’s VM communicate with the hardware and theoretically can intervene executed processes on the hosted virtual instance through virtual introspection [25]. This could also affect encryption or signing processes executed on the VM and therefore leading to the leakage of the secret key. Although this risk can be disregarded in most of the cases, the impact on the security of high security environments is tremendous.a) Snapshot Analysis: Traditional forensics expect target machines to be powered down to collect an image (dead virtual instance). This situation completely changed with the advent of the snapshot technology which is supported by all popular hypervisors such as Xen, VMware ESX and Hyper-V.A snapshot, also referred to as the forensic image of a VM, providesa powerful tool with which a virtual instance can be clonedby one click including also the running system’s mem ory. Due to the invention of the snapshot technology, systems hosting crucial business processes do not have to be powered down for forensic investigation purposes. The investigator simply creates and loads a snapshot of the target VM for analysis(live virtual instance). This behavior is especially important for scenarios in which a downtime of a system is not feasible or practical due to existing SLA. However the information whether the machine is running or has been properly powered down is crucial [3] for the investigation. Live investigations of running virtual instances become more common providing evidence data that。

云计算Cloud-Computing-外文翻译

毕业设计说明书英文文献及中文翻译学生姓名：学号：计算机与控制工程学院：专指导教师：2017 年 6 月英文文献Cloud Computing1。

Cloud Computing at a Higher LevelIn many ways，cloud computing is simply a metaphor for the Internet, the increasing movement of compute and data resources onto the Web. But there's a difference: cloud computing represents a new tipping point for the value of network computing. It delivers higher efficiency, massive scalability, and faster，easier software development. It's about new programming models，new IT infrastructure, and the enabling of new business models。

For those developers and enterprises who want to embrace cloud computing, Sun is developing critical technologies to deliver enterprise scale and systemic qualities to this new paradigm:（1) Interoperability —while most current clouds offer closed platforms and vendor lock—in, developers clamor for interoperability。

科技英语中一词多义及其翻译技巧

科技英语中一词多义及其翻译技巧在科技领域中，有许多词汇具有多义性。

以下是一些常见的一词多义词汇及其翻译技巧：1. Chip:-义1：芯片（指电子器件上的集成电路）-义2：碎片（形容薄片状或小块状的物体）翻译技巧：根据上下文确定词义，如果是指电子器件上的集成电路，可翻译为"芯片"；如果指碎片，则可使用"碎片"或者具体描述其形状的词汇。

2. Cloud:-义1：云（指云计算和云存储技术）-义2：云彩（指大气中形成的可见水汽）翻译技巧：根据上下文确定词义，如果是指云计算或云存储技术，可翻译为"云"；如果指云彩，则可使用"云彩"或其他与天气相关的词汇。

3. Firewall:-义1：防火墙（计算机系统中用于保护网络安全的硬件或软件）-义2：防火墙（建筑物中用于阻止火势蔓延的隔离墙）翻译技巧：根据上下文确定词义，如果是指计算机系统中的防火墙，可翻译为"防火墙"；如果指建筑物中的防火墙，则也可使用"防火墙"，但需要通过上下文解释使读者明确其意义。

4. Terminal:-义1：终端（计算机中用于输入和显示数据的设备）-义2：终点（指旅行或运输中的最后一站或目的地）翻译技巧：根据上下文确定词义，如果是指计算机设备中的终端，可翻译为"终端"；如果指旅行或运输中的终点，则可使用"终点"或其他类似的词汇。

5. Patch:-义1：补丁（指修复软件中的漏洞或错误的程序代码）-义2：补片（指用于修复或加固物体的一小块材料）翻译技巧：根据上下文确定词义，如果是指修复软件中的漏洞或错误的程序代码，可翻译为"补丁"；如果指修复或加固物体的补片，则可使用"补片"或者具体描述其材料或用途的词汇。

以上是一些常见的科技英语一词多义的例子及其翻译技巧。

科技英语新词的构词方式和翻译方法

科技英语新词的构词方式和翻译方法科技英语新词的构词方式和翻译方法随着科技的不断进步和发展，越来越多的英语新词诞生出来，尤其是涉及到先进的科学技术、信息技术等领域。

如果我们要更好地理解这些新词的含义，就需要了解这些新词的构词方式和翻译方法。

一、构词方式1.缩略语（Acronyms）缩略语是通过缩写组合词语的方式来构造新的词语，例如：AIDS （Acquired Immune Deficiency Syndrome），NASA（National Aeronautics and Space Administration）。

这种方式在科技领域中特别常见。

2.组合词（Compound words）组合词是将两个或多个词语组合在一起构成新的词语，例如：AI （Artificial Intelligence），biotechnology（生物技术）。

组合词的特点在于，它们可以更好地描述科技领域中新的概念、新的事物和新的技术。

3.借词（Borrowings）借词是将其他语言中的单词直接引用到英语中，例如：karaoke（日语卡拉OK），feng shui（中文风水）。

这种方式也很常见，特别是在涉及到跨国企业、国际贸易、文化交流等方面。

4.衍生词（Derivatives）衍生词是通过对已有词语的修改、添加、删除等方式来构造新的词语，例如：download（从网络上下载），cyberspace（网络空间）。

这种方式在信息技术领域中尤为常见。

二、翻译方法1.直接翻译对于某些新词来说，直接翻译是最简单也最直接的方法。

例如：selfie（自拍），blog（博客）等。

2.意译对于某些新词来说，直接翻译可能并不能很好地表达其含义，这时需要进行意译。

例如：cloud computing（云计算），这里的“云”并没有实质性的含义，而只是一种比喻。

3.加注释对于某些新词来说，直接翻译和意译都不够准确，这时需要加注释来进行解释。

例如：virtual reality（虚拟现实），通过注释我们可以更好地理解这个概念。

科技新词的词语

科技新词的词语
科技新词的词语是指那些近年来随着科技进步和发展而出现的新词汇。

这些新词汇大多是由英文或其他语言的词汇翻译或创造而来的，主要是为了描述或指代一些新兴的科技概念、产品或服务。

以下是一些常见的科技新词：
1. 云计算（Cloud Computing）- 一种将计算资源通过网络交付为服务的技术。

2. 人工智能（Artificial Intelligence）- 一种通过计算机模拟人类智慧和思维的技术。

3. 区块链（Blockchain）- 一种分布式数据库技术，用于存储和传输数据。

4. 物联网（Internet of Things）- 一种将各种设备和物品通过互联网连接起来的技术。

5. 虚拟现实（Virtual Reality）- 一种通过计算机生成的虚拟环境，使用户可以感受到身临其境的感觉。

6. 增强现实（Augmented Reality）- 一种将电子信息与现实世界相结合的技术，使用户可以看到虚拟对象和现实对象的结合。

7. 人脸识别（Facial Recognition）- 一种通过计算机识别人脸的技术，通常用于安全系统和身份验证。

8. 智能家居（Smart Home）- 一种将家庭设备和家庭自动化系统连接到互联网的技术，使用户可以通过智能手机或其他设备控制家庭设备。

9. 自动驾驶（Autonomous Driving）- 一种利用传感器和计算机技术实现车辆自主行驶的技术。

10. 无人机（Drone）- 一种无人机器人，可以通过遥控器或自动控制系统进行控制。

通常用于航拍和物流配送。

随着科技的不断发展，这些科技新词将不断增加和演变，为我们的生活和工作带来更多的便利和创新。

大数据名词多语翻译

大数据名词多语翻译学习大数据相关名词的多语言翻译是一个很好的方式来扩展你的词汇量并提高你的语言能力。

下面是一些常见的大数据名词及其中英文对照：1. 大数据(Big Data)2. 数据分析(Data Analysis)3. 数据挖掘(Data Mining)4. 数据可视化(Data Visualization)5. 数据仓库(Data Warehouse)6. 数据模型(Data Model)7. 数据集(Dataset)8. 数据处理(Data Processing)9. 数据清洗(Data Cleansing)10. 数据科学家(Data Scientist)11. 机器学习(Machine Learning)12. 人工智能(Artificial Intelligence)13. 云计算(Cloud Computing)14. 预测分析(Predictive Analytics)15. 实时分析(Real-time Analytics)当学习这些名词时，你可以采取以下学习技巧来记忆和理解它们：1. 制作词汇卡片：将中英文对照的名词写在一张卡片的一面，另一面写上对应的释义。

每天复习一些卡片，直到你记住所有的名词和它们的意思。

2. 应用名词：尽量将这些名词应用到你的写作、口语练习或者与他人的交流中。

这样能帮助你更好地理解和记忆这些词汇。

3. 创造相关的例句：为每个名词创造一些例句，这样可以帮助你更好地理解其用法和上下文。

4. 多媒体学习：寻找相关的视频、音频或文章来帮助你更好地理解和记忆这些名词。

你可以通过观看教学视频、听听流行歌曲或者阅读相关的新闻文章来扩展你对这些名词的理解。

通过不断地练习和应用这些学习技巧，你将能够更轻松地掌握大数据领域的词汇，并提高你的语言能力。

记住，持之以恒是成功的关键，所以要坚持学习并保持积极的学习态度！。

改革开放40年以来翻译

这些翻译表达涵盖了改革开放40年以来中国在经济、贸易、金融以及社会发展，科技创新、教育文化、农业农村发展，业劳动力、城市发展与建设，交通与基础设施、环境保护与可持续发展、文化与教育，医疗与健康、法律与司法、体育与奥运、军事与国防，外交与国际关系、党风廉政与反腐败，城市化与城市发展等领域的进展和改革。

它们在相关的政策文件、新闻报道和学术文献中被广泛使用。

Reform and Opening Up: 改革开放Economic restructuring: 经济重组Market-oriented reforms: 市场化改革Opening up to the outside world: 对外开放Foreign investment: 外资投资Trade liberalization: 贸易自由化Foreign trade: 外贸Foreign direct investment (FDI): 外商直接投资Export-oriented economy: 出口导向型经济Special Economic Zones (SEZs): 经济特区Opening up of the financial sector: 金融业开放State-owned enterprise (SOE) reform: 国有企业改革Private enterprise development: 民营企业发展Market economy: 市场经济Socialism with Chinese characteristics: 中国特色社会主义Poverty alleviation: 扶贫Rural revitalization: 乡村振兴Belt and Road Initiative: 一带一路倡议Technological innovation: 科技创新Sustainable development: 可持续发展经济与金融：State-owned enterprise (SOE) reform: 国有企业改革Mixed ownership reform: 混合所有制改革Financial liberalization: 金融自由化Stock market: 股票市场Bond market: 债券市场Foreign exchange market: 外汇市场Free trade zone: 自由贸易区Capital market: 资本市场Intellectual property rights protection: 知识产权保护Financial deregulation: 金融放松管制贸易与对外关系：31. Free trade: 自由贸易Trade surplus/deficit: 贸易顺差/逆差Bilateral trade: 双边贸易Multilateral trade: 多边贸易Trade barriers: 贸易壁垒Trade imbalance: 贸易不平衡Foreign direct investment (FDI): 外商直接投资Trade cooperation: 贸易合作Export-oriented industries: 出口导向产业Cross-border e-commerce: 跨境电子商务社会发展与改革：Urbanization: 城市化Poverty alleviation: 扶贫Social welfare: 社会福利Healthcare reform: 医疗改革Education reform: 教育改革Environmental protection: 环境保护Rural revitalization: 农村振兴Housing reform: 住房改革Pension system reform: 养老保险制度改革Social security system: 社会保障体系51. Technological advancement: 科技进步Innovation-driven development: 创新驱动发展Research and development (R&D): 研发High-tech industry: 高科技产业Artificial intelligence (AI): 人工智能Internet of Things (IoT): 物联网Big data: 大数据Cloud computing: 云计算Cybersecurity: 网络安全教育与文化：Cultural exchange: 文化交流Cultural diversity: 文化多样性Intangible cultural heritage: 非物质文化遗产Cultural preservation: 文化保护Art education: 艺术教育Cultural tourism: 文化旅游Education reform: 教育改革Lifelong learning: 终身学习Cultural heritage conservation: 文化遗产保护Quality-oriented education: 素质教育Vocational education: 职业教育Cultural exchange: 文化交流Cultural heritage protection: 文化遗产保护Film and television industry: 影视产业Performing arts: 表演艺术Literary works: 文学作品农业与农村发展：Agricultural modernization: 农业现代化Rural revitalization: 乡村振兴Agricultural subsidies: 农业补贴Crop diversification: 作物多样化Organic farming: 有机农业Rural infrastructure: 农村基础设施Farmer cooperatives: 农民合作社Agricultural technology transfer: 农业技术转让Rural poverty alleviation: 农村扶贫Agricultural reforms: 农业改革Agricultural modernization: 农业现代化Agricultural reform: 农村改革Agricultural technology: 农业技术Agricultural sustainability: 农业可持续发展Rural entrepreneurship: 农村创业Farmer's income: 农民收入Agricultural cooperation: 农业合作就业与劳动力：Employment opportunities: 就业机会Labor market: 劳动力市场Job creation: 就业创造Labor rights: 劳工权益Wage reform: 工资改革Labor mobility: 劳动力流动性Unemployment rate: 失业率Labor union: 工会Labor protection: 劳动保护Work-life balance: 工作与生活平衡城市发展与建设：Urban renewal: 城市更新Urban planning: 城市规划Sustainable urbanization: 可持续城市化Smart cities: 智慧城市Urban infrastructure: 城市基础设施Urban transportation: 城市交通Urban pollution control: 城市污染控制Urbanization rate: 城镇化率Urban-rural integration: 城乡一体化Livable cities: 宜居城市交通与基础设施：High-speed rail: 高速铁路Expressways: 高速公路Airport expansion: 机场扩建Port development: 港口发展Infrastructure investment: 基础设施投资Public transportation: 公共交通Logistics network: 物流网络Rail transit: 轨道交通Bridge construction: 桥梁建设Urban transport planning: 城市交通规划环境保护与可持续发展：Green development: 绿色发展Renewable energy: 可再生能源Carbon emissions reduction: 减排Eco-friendly practices: 环保实践Biodiversity conservation: 生物多样性保护Waste management: 废物管理Clean energy technology: 清洁能源技术Sustainable agriculture: 可持续农业Environmental regulations: 环境法规Climate change mitigation: 气候变化缓解社会治理与法律改革：Rule of law: 法治Judicial reform: 司法改革Anti-corruption campaign: 反腐败运动Legal system reform: 法制改革Social stability: 社会稳定Public security: 公共安全Community governance: 社区治理Crime prevention: 犯罪预防Human rights protection: 人权保护Legal education: 法律教育社会福利与保障：Social insurance: 社会保险Healthcare coverage: 医疗保障Pension system: 养老保险制度Poverty alleviation program: 扶贫计划Social assistance: 社会救助Disability support: 残疾人扶助Childcare services: 儿童保育服务Elderly care: 老年人护理Housing subsidies: 住房补贴Family planning policy: 计划生育政策科学与教育：Scientific research: 科学研究Higher education: 高等教育Scientific achievements: 科研成果Science and technology parks: 科技园区STEM education: 科学、技术、工程、数学教育School curriculum: 学校课程Talent cultivation: 人才培养Academic exchange: 学术交流Digital learning: 数字化学习Education equity: 教育公平性文化与娱乐：Cultural industry: 文化产业Artistic expression: 艺术表达Cultural tourism: 文化旅游Film festival: 电影节Music concert: 音乐会Literary award: 文学奖项Cultural heritage sites: 文化遗产地Performing arts center: 表演艺术中心Art exhibition: 艺术展览Online streaming platform: 在线流媒体平台经济与金融：Market-oriented reforms: 市场化改革Foreign direct investment (FDI): 外商直接投资Economic liberalization: 经济自由化Free trade zone: 自由贸易区Financial reform: 金融改革Stock market: 股票市场Monetary policy: 货币政策Economic globalization: 经济全球化Supply-side reforms: 供给侧改革Consumer market: 消费市场国际合作与外交：Belt and Road Initiative: 一带一路倡议Multilateral cooperation: 多边合作Diplomatic relations: 外交关系International trade: 国际贸易Foreign aid: 外援Cultural diplomacy: 文化外交Global governance: 全球治理Regional integration: 区域一体化International organizations: 国际组织Peacekeeping missions: 维和任务社会变革与社会价值观：Gender equality: 性别平等Family values: 家庭价值观Social equality: 社会平等Human dignity: 人的尊严Social justice: 社会公正Civil society: 公民社会Youth empowerment: 青年赋权Volunteerism: 志愿服务Social inclusion: 社会包容Cultural diversity: 文化多样性人民生活与社会福利：Living standards: 生活水平Social security system: 社会保障体系Health insurance: 医疗保险Education system: 教育体系Housing affordability: 住房可负担性Consumer rights: 消费者权益Social welfare: 社会福利Elderly care services: 老年人护理服务Maternity leave: 产假Cultural activities: 文化活动科技与创新：Technological advancements: 科技进步Innovation-driven development: 创新驱动发展Research and development (R&D): 研发Intellectual property rights: 知识产权Science parks: 科技园区Start-up ecosystem: 创业生态系统High-tech industries: 高科技产业Digital transformation: 数字化转型Artificial intelligence (AI): 人工智能Big data analytics: 大数据分析环境保护与可持续发展：Environmental conservation: 环境保护Sustainable development: 可持续发展Clean energy: 清洁能源Carbon emissions: 碳排放Renewable resources: 可再生资源Ecological restoration: 生态恢复Green technology: 绿色技术Waste management: 废物管理Biodiversity conservation: 生物多样性保护Climate change mitigation: 气候变化缓解城市化与城市发展：Urbanization: 城市化Urban planning: 城市规划Smart cities: 智慧城市Urban infrastructure: 城市基础设施Public transportation: 公共交通Urban renewal: 城市更新Urban design: 城市设计Urban governance: 城市治理Urban development: 城市发展Housing construction: 住房建设医疗与健康：Healthcare reform: 医疗改革Universal health coverage: 全民医保Primary healthcare: 基层医疗Medical research: 医学研究Disease prevention and control: 疾病预防和控制Telemedicine: 远程医疗Health promotion: 健康促进Traditional Chinese medicine: 中医药Public health emergency response: 公共卫生应急响应Health education: 健康教育法律与司法：Rule of law: 法治Judicial reform: 司法改革Legal system: 法律体系Legal rights: 法律权益Legal aid: 法律援助Anti-corruption campaign: 反腐败运动Judicial independence: 司法独立Criminal justice: 刑事司法Civil litigation: 民事诉讼Legal awareness: 法律意识体育与奥运：Sports development: 体育发展Olympic Games: 奥运会Sportsmanship: 体育精神Athlete training: 运动员培训Sports facilities: 体育设施Sports culture: 体育文化Sports diplomacy: 体育外交Fair play: 公平竞争Paralympic Games: 残奥会Sports nutrition: 运动营养军事与国防：National defense: 国防Military modernization: 军事现代化Defense technology: 防务技术Peacekeeping operations: 维和行动National security: 国家安全Defense budget: 国防预算Military strategy: 军事战略Defense industry: 国防工业Cybersecurity: 网络安全Military cooperation: 军事合作经济与贸易：Economic reform: 经济改革Market-oriented economy: 市场经济Foreign direct investment (FDI): 外商直接投资Free trade: 自由贸易Economic globalization: 经济全球化Export-oriented manufacturing: 出口导向型制造业Financial market: 金融市场E-commerce: 电子商务Economic growth: 经济增长Sustainable development: 可持续发展科学与教育：Scientific research: 科学研究Technological innovation: 技术创新STEM education: 科学、技术、工程和数学教育Knowledge-based economy: 知识经济Research institute: 研究所Academic exchange: 学术交流Science and technology policy: 科技政策Intellectual property protection: 知识产权保护Educational reform: 教育改革Lifelong learning: 终身学习社会与人文：Social harmony: 社会和谐Poverty alleviation: 扶贫Social equality: 社会平等Gender equality: 性别平等Social welfare: 社会福利Volunteer work: 志愿者工作Community development: 社区发展Cultural tolerance: 文化包容Ethical values: 道德价值观Social responsibility: 社会责任农业与农村发展：Agricultural modernization: 农业现代化Rural revitalization: 乡村振兴Agricultural productivity: 农业生产力Agricultural subsidies: 农业补贴Rural infrastructure: 农村基础设施Agricultural technology: 农业技术Rural entrepreneurship: 农村创业Agricultural sustainability: 农业可持续性Farmer's income: 农民收入Crop diversification: 农作物多样化交通与基础设施：Transportation network: 交通网络High-speed rail: 高铁Expressway: 高速公路Urban transportation: 城市交通Airport construction: 机场建设Port development: 港口发展Bridge and tunnel: 桥梁和隧道Public transportation system: 公共交通系统Infrastructure investment: 基础设施投资Smart transportation: 智能交通外交与国际关系：Diplomatic relations: 外交关系Bilateral cooperation: 双边合作Multilateral cooperation: 多边合作International trade: 国际贸易Global governance: 全球治理International cooperation: 国际合作Foreign aid: 外援Peacekeeping mission: 维和任务Non-interference policy: 不干涉政策Soft power: 软实力党风廉政与反腐败：Party discipline: 党纪Anti-corruption campaign: 反腐败运动Party building: 党建设Clean governance: 清廉政府Anti-graft measures: 反贪措施Integrity education: 廉政教育Corruption investigation: 腐败调查Asset recovery: 资产追回Whistleblower protection: 举报保护Anti-corruption legislation: 反腐败立法城市化与城市发展：Urbanization: 城市化Urban planning: 城市规划Urban renewal: 城市更新Sustainable urban development: 可持续城市发展Smart city: 智慧城市Urban infrastructure: 城市基础设施Urban transport system: 城市交通系统Urban design: 城市设计Urban environmental protection: 城市环境保护Urban governance: 城市治理环境保护与可持续发展：Environmental protection: 环境保护Pollution control: 污染治理Carbon emissions reduction: 碳排放减少Renewable energy: 可再生能源Ecological conservation: 生态保护Biodiversity preservation: 生物多样性保护Clean energy development: 清洁能源开发Waste management: 废物处理Green technology: 绿色技术Sustainable development goals: 可持续发展目标科技与创新：Technological advancement: 科技进步Innovation-driven development: 创新驱动发展Research and development: 研发Artificial intelligence: 人工智能Big data: 大数据Internet of Things: 物联网Cloud computing: 云计算Robotics: 机器人技术Quantum computing: 量子计算5G technology: 5G技术社会保障与福利：Social security: 社会保障Pension system: 养老金制度Healthcare reform: 医疗改革Social insurance: 社会保险Poverty alleviation program: 扶贫计划Social assistance: 社会救助Disability rights: 残疾人权益Maternity benefits: 生育津贴Unemployment insurance: 失业保险Housing welfare: 住房福利人民生活水平提高：Standard of living: 生活水平Income inequality: 收入不平等Consumerism: 消费主义Quality of life: 生活质量Education equality: 教育平等Affordable housing: 负担得起的住房Employment opportunities: 就业机会Social mobility: 社会流动性Leisure and entertainment: 休闲与娱乐Consumer rights: 消费者权益法律与知识产权保护：Intellectual property rights: 知识产权Patent protection: 专利保护Copyright infringement: 侵权Trademark registration: 商标注册Anti-counterfeiting measures: 打击假冒行动Cybersecurity: 网络安全Data protection: 数据保护Legal enforcement: 法律执法Anti-corruption campaign: 反腐败斗争Anti-money laundering: 反洗钱。

专业术语翻译

专业术语翻译以下是一些常见的专业术语翻译：
1. Algorithm - 算法
2. Database - 数据库
3. Artificial intelligence -
4. Machine learning - 机器学习
5. Robotics - 机器人学
6. Software engineering - 软件工程
7. Data analysis - 数据分析
8. Big data - 大数据
9. Cloud computing - 云计算
10. Cryptocurrency - 加密货币
11. Internet of Things (IoT) - 物联网
12. Virtual reality - 虚拟现实
13. Augmented reality - 增强现实
14. User interface - 用户界面
15. Graphics processing unit (GPU) - 图形处理器
16. Content management system (CMS) - 内容管理系统
17. Customer relationship management (CRM) - 客户关系管理
18. User experience (UX) - 用户体验
19. Search engine optimization (SEO) - 搜索引擎优化
20. Web development - 网站开发
备注：专业术语的翻译可能因行业和语境而有所不同，以上翻译为一般情况下的常见翻译，具体应根据具体情况调整。

信息技术新名词的含义及用途和领域

信息技术新名词的含义及用途和领域【深度解读】信息技术新名词的含义及用途和领域【引言】互联网与信息技术的快速发展，不断为人们带来新的机遇和挑战。

随之而来的是一系列新的名词和概念的涌现，这些名词往往代表着新技术和新思维的突破。

本文将对信息技术新名词的含义、用途和应用领域进行全面评估，旨在为读者提供深刻和灵活的理解。

【正文】一、云计算（Cloud Computing）1. 含义：云计算是一种通过互联网来提供计算服务的模式，用户可以通过云计算平台获取到包括计算能力、存储空间和应用程序等资源。

云计算通过将这些资源集中管理，实现了高效、灵活和可扩展的计算环境。

2. 用途：云计算广泛应用于各个领域，包括企业的数据存储与处理、移动应用的开发与部署、科学计算与模拟等。

云计算可以降低企业的IT成本，提高数据安全性，并提供可靠的计算能力和存储空间。

3. 应用领域：云计算在金融、医疗、教育、物流等行业都有广泛应用。

金融行业可以通过云计算实现高效的数据分析和交易处理；医疗行业可以利用云计算来实现电子病历的共享和远程医疗服务。

二、大数据（Big Data）1. 含义：大数据是指规模庞大且难以用传统方法进行处理和管理的数据集合。

这些数据通常具有多样性、高速度和大容量等特点，需要借助先进的技术和算法进行分析和挖掘。

2. 用途：大数据的应用可以帮助企业和组织发现潜在的商业机会、提升生产效能和服务质量，并在决策过程中提供更准确的支持。

借助大数据分析，企业可以更好地了解用户需求，提高产品的竞争力。

3. 应用领域：大数据在电商、金融、健康医疗等领域都有广泛应用。

电商企业通过分析用户行为和购买数据，提供个性化的推荐服务；金融行业通过大数据分析，可以进行风险评估和欺诈检测。

三、人工智能（Artificial Intelligence）1. 含义：人工智能是指通过模仿和模拟人类智能思维过程的方法，使机器具备类人的智能和学习能力。

人工智能可以处理复杂的问题、提供智能化的服务，并在各个领域展现出巨大的潜力。

云计算的概念

云计算的概念：指服务的提供和使用模式。

云计算的核心思想，是将大量用网络连接的计算资源统一管理和调度，构成一个计算资源池向用户按需服务。

提供资源的网络被称为“云”。

“云”中的资源在使用者看来是可以无限扩展的，并且可以随时获取，按需使用，随时扩展，按使用付费。

云计算的产业三级分层：云软件、云平台、云设备云计算由英文Cloud Computing直接翻译而来。

云计算将计算任务分布在大量计算机构成的资源之上，使各种应用系统能够根据需要获取计算力、存储空间和各种软件服务。

2006年谷歌推出了“Google 101计划”，并正式提出“云”的概念和理论。

随后亚马逊微软、惠普、雅虎、英特尔、IBM等公司都宣布了自己的“云计划”。

1．云计算的产生传统模式下，企业建立一套IT系统不仅仅需要购买硬件等基础设施，还有买软件的许可证，需要专门的人员维护。

当企业的规模扩大时还要继续升级各种软硬件设施以满足需要。

对于企业来说，计算机等硬件和软件本身并非他们真正需要的，它们仅仅是完成工作、提供效率的工具而已。

对个人来说，我们想正常使用电脑需要安装许多软件，而许多软件是收费的，对不经常使用该软件的用户来说购买是非常不划算的。

可不可以有这样的服务，能够提供我们需要的所有软件供我们租用？这样我们只需要在用时付少量“租金”即可“租用”到这些软件服务，为我们节省许多购买软硬件的资金。

好比我们每天都要用电，但我们不是每家自备发电机，它由电厂集中提供；我们每天都要用自来水，但我们不是每家都有井，它由自来水厂集中提供。

这种模式极大得节约了资源，方便了我们的生活。

面对计算机给我们带来的困扰，我们可不可以像使用水和电一样使用计算机资源？这些想法最终导致了云计算的产生。

2．技术概念云计算是由分布式计算、并行处理、网格计算发展来的，是一种新兴的商业计算模型。

云计算的IT资源包括服务器、存储、宽带、网络及安全等资源组件，而数据中心是云计算资源和能力的主要支持和供应核心。

科技英语中一词多义及其翻译技巧

科技英语中一词多义及其翻译技巧在科技英语中，由于技术和科学的发展，一词多义的情况很常见。

以下是一些常见的一词多义词汇及其翻译技巧：1. Chip-芯片：用于电子设备的集成电路芯片。

-片状：用于食品或者物体的薄片状。

翻译技巧：根据上下文选择合适的翻译。

如果是与电子设备相关的话题，选择“芯片”；如果是与食品或物体相关的话题，选择“片状”。

2. Cloud-云：指云计算、云存储等基于互联网的数据处理和存储服务。

-云状物：指天空中的云或者物体形状类似云的东西。

翻译技巧：根据上下文选择合适的翻译。

如果是与互联网和数据相关的话题，选择“云”；如果是与天气或物体形状相关的话题，选择“云状物”。

3. Protocol-协议：指计算机网络通信中用于控制数据传输的规则和约定。

-外交协议：指国际间表示友好、开展合作的文件或约定。

翻译技巧：根据上下文选择合适的翻译。

如果是与计算机网络通信相关的话题，选择“协议”；如果是与国际外交相关的话题，选择“外交协议”。

4. Kernel-内核：指操作系统中控制计算机硬件和其他软件之间交互的核心部分。

-核心部分：指任何事物中最基本、最核心的部分。

翻译技巧：根据上下文选择合适的翻译。

如果是与操作系统和计算机硬件相关的话题，选择“内核”；如果是与其他事物的核心部分相关的话题，选择“核心部分”。

5. Stream-流：指在计算机中持续传输数据的一系列连续的字节。

-溪流：指自然界中流动的水流。

翻译技巧：根据上下文选择合适的翻译。

如果是与计算机数据传输相关的话题，选择“流”；如果是与自然界的水流相关的话题，选择“溪流”。

6. Interface-接口：指不同系统、设备或者软件之间进行交互和通信的界面。

-界面：指人与电子设备或计算机软件之间的交互界面。

翻译技巧：根据上下文选择合适的翻译。

如果是与系统、设备或者软件之间的交互通信相关的话题，选择“接口”；如果是与人与电子设备或计算机软件交互界面相关的话题，选择“界面”。

时政短语翻译汇总(中英)

1.Food crop production 粮食产量2.Energy consumption 能耗3.The strategy of innovation-driven development 创新驱动发展战略4.Business startups and innovations by the general public 大众创业、万众创新5.Disposable income 可支配收入6.Savings deposit 储蓄存款7.Off the assembly line 下线bine long-term and short-term considerations 远近结合9.Seek benefit and avoid harm 趋利避害10.Maintain stable growth, make structural adjustment, guard against risks 稳增长，调结构，防风险11.Downward pressure on economy 经济下行压力12.Range-based regulation 区间调控13.Targeted regulation 定向调控14.Well-timed regulation 相机调控15.Prudent monetary policy 稳健的货币政策16.Real economy 实体经济17.Special-purpose funds 专项基金18.Water conservancy, rundown urban areas, dilapidated rural housing 水利、城镇棚户区、农村危房改造19.Streamline administration, delegate more powers, improve regulation 简政放权、放管结合20.The requirement for government review 行政审批21.Requirement for verification or approval for professional qualifications 职业资格许可和认定22.Non-administrative review 非行政许可23.Operational and post-operational oversight 事中事后监管24.Transfer payments 转移支付25.Cross-border payment system 跨境支付系统ernment set prices 政府定价27.Tax rebates 出口退税28.Overseas-funded projects 外商投资项目29.Special drawing rights basket 特别提款权货币篮子30.Silk road economic belt & 21st century maritime silk road (initiative) (one belt and one road) 一带一路31.Industrial-capacity cooperation with other countries (国际产能合作)32.High-speed railway 高铁33.Internet plus 互联网+34.Maker 创客35.Made in China 2025 initiative 中国制造202536.Small and medium-sized enterprises 中小企业37.Cut overcapacity 化解过剩产能38.Business acquisitions and restructuring 企业兼并重组39.Production- and consumer-oriented service industries 生产性、生活性服务业40.Conserve energy, reduce emissions 节能减排41.Self-imposed emissions reduction 自主减排ernment-subsidized housing unit 保障性安居工程住房43.Badly built and poorly operated schools薄弱学校44.Subsistence allowance 低保subsistence level 生存水平45.Party and government offices and public institutions机关事业单位46.Workplace safety 安全生产监管47.Chinese People’s War of Resistance against Japanese Aggression 中国人民抗日战争48.Global War against Fascism世界反法西斯战争49.Fresh progress 新进展50.Elderly care 养老51.Inbound and outbound tourism 出入境旅游outbound investor 对外投资X52.The South-to-North Water Diversion Project 南水北调工程53.Advanced manufacturing, modern services and strategic emerging industries 先进制造业、现代服务业、战略性新兴产业54.Aggregate economic output 经济总量55.Pilot reform zones 改革试验区56.Cloud computing 云计算57.Innovative and talent-rich country 创新型国家、人才强国58.Water conservancy, farming machinery and the modern seed industry水利、农机及现代种业59.North-south and east-west intersecting economic belts 纵向横向经济轴带60.Growth pole and city clusters 增长极和城市群61.China’s transformation from a trader of quantity to a trader of quality从贸易大国迈向贸易强国her in a new phase 引领、进入、迎来一个新阶段新格局mon prosperity 共同富裕64.First-class universities and first-class fields of discipline 一流大学、一流学科65.Working-age population 劳动年龄人口66.A healthy China 健康中国67.Boost civic morality and keep Chinese culture thriving实施公民道德建设、中华文化传承68.Keep to the following three guidelines 把握好三点69.Middle-income trap 中等收入陷阱70.… is like sailing against the current: you either forge ahead or drift downstream. 如逆水行舟，不进则退。

c开通的单词 -回复

c开通的单词-回复题目：C开通的单词：从C语言到Covid-19，一个字母的变迁之路引言：C是英语字母表中的第三个字母，它可以代表很多意义，从编程语言到全球疫情。

本文将从C语言开始，详细探索C开通的单词背后的故事，展示它们在不同领域的应用与影响。

一、C语言C语言是一种通用的高级程序设计语言，由贝尔实验室的Dennis Ritchie 于1972年在DEC PDP-11上开发。

C语言简洁、高效，广泛应用于系统编程、嵌入式系统和应用软件开发等领域。

随着计算机技术的发展，C语言在软件工程中发挥了巨大的作用。

它的出现让程序员能够更加高效地书写代码，大大提升了软件开发的效率和质量。

二、C++C++是在C语言基础上发展起来的一种编程语言，由贝尔实验室的Bjarne Stroustrup于1980年代初开发。

C++在C语言的基础上增加了面向对象编程的特性，成为一种更加强大和灵活的编程语言。

C++在游戏开发、图形界面设计和底层系统开发等领域有着广泛的应用。

通过C++语言编写的程序可以实现更复杂的功能，并且具备较高的运行效率。

三、Cryptography（密码学）Cryptography是密码学的英文翻译，它是研究如何通过密码和密码学算法保护信息的学科。

密码学在现代通信和网络安全领域起着重要作用，它涉及到信息加密、解密和破解等技术。

Cryptography的发展与人类保护信息隐私的需求密切相关，它为信息安全提供了有效的保障和支持。

四、Cloud computing（云计算）Cloud computing是云计算的英文翻译，它是一种基于互联网的计算模式。

通过云计算，用户可以根据自身需求获得快捷、安全、可靠的计算资源和服务，而无需管理底层的硬件和软件资源。

云计算的兴起极大地改变了人们的计算方式，为各行各业带来了巨大的效益和机遇。

目前，云计算已经成为国际信息技术发展的重要方向和热点。

五、Covid-19Covid-19是新冠肺炎的简称，该病毒引发了全球范围内的公共卫生危机。

云计算翻译

一云多芯 专业英文词汇

云计算基础知识

科技术语在翻译中的隐喻现象

云计算下的翻译模式研究

5月短语翻译和百科知识

云计算外文翻译参考文献

云计算Cloud-Computing-外文翻译

科技英语中一词多义及其翻译技巧

科技英语新词的构词方式和翻译方法

科技新词的词语

大数据名词多语翻译

改革开放40年以来翻译

专业术语翻译

信息技术新名词的含义及用途和领域

云计算的概念

科技英语中一词多义及其翻译技巧

时政短语翻译汇总(中英)

c开通的单词 -回复

一云多芯专业英文词汇