博客园终于解封了，28号复试，复试时间已经很晚了，只能背水一战了，加油！

为准备考研复试中的专业英语翻译，这里每天整理精翻一篇前沿技术的论文摘要。

1、基于区块链的数据透明化：问题与挑战

Abstract: With the high-speed development of Internet of things, wearable devices and mobile communication technology, large-scale data continuously generate and converge to multiple data collectors, which influences people’s life in many ways. Meanwhile, it also causes more and more severe privacy leaks. Traditional privacy aware mechanisms such as differential privacy, encryption and anonymization are not enough to deal with the serious situation. What is more, the data convergence leads to data monopoly which hinders the realization of the big data value seriously. Besides, tampered data, single point failure in data quality management and so on may cause untrustworthy data-driven decision-making. How to use big data correctly has become an important issue. For those reasons, we propose the data transparency, aiming to provide solution for the correct use of big data. Blockchain originated from digital currency has the characteristics of decentralization, transparency and immutability, and it provides an accountable and secure solution for data transparency. In this paper, we first propose the definition and research dimension of the data transparency from the perspective of big data life cycle, and we also analyze and summary the methods to realize data transparency. Then, we summary the research progress of blockchain-based data transparency. Finally, we analyze the challenges that may arise in the process of blockchain-based data transparency.

Key words: blockchain, accountability, privacy protection, data monopoly, data-driven decision-making

摘要： 物联网、穿戴设备和移动通信等技术的高速发展促使数据源源不断地产生并汇聚至多方数据收集者，影响着人们生活的方方面面。由此带来更严峻的隐私泄露问题, 然而传统的隐私感知机制,如差分隐私、加密和匿名等隐私保护技术还不足以应对。更进一步，数据的自主汇聚导致数据垄断问题，严重影响了大数据价值实现.此外，大数据决策过程中，数据非真实产生、被篡改和质量管理过程中的单点失败等问题导致数据决策不可信。如何正确使用大数据已经成为一个重要的问题。因此，我们提出了数据透明度，旨在为大数据的正确使用提供解决方案。区块链源于数字货币，具有去中心化、透明性和不可变性的特点，为数据透明化提供了一个可靠的、安全的解决方案。首先，从大数据生命周期的角度提出数据透明化的定义和研究维度，并分析总结了实现数据透明化的方法。然后，总结了基于区块链的数据透明化的研究进展。最后，对基于区块链的数据透明化可能面临的挑战进行分析.

关键词: 区块链, 问责, 隐私保护, 数据垄断, 数据驱动的决策

2、大数据管理：概念、技术与挑战

关键词: 大数据, 数据分析, 云计算

Abstract: Data type and amount in human society is growing in amazing speed which is caused by emerging new services such as cloud computing, internet of things and social network, the era of big data has come. Data has been fundamental resource from simple dealing object, and how to manage and utilize big data better has attracted much attention. Evolution or revolution on database research for big data is a problem. This paper discusses the concept of big data, and surveys its state of the art. The framework of big data is described and key techniques are studied. Finally some new challenges in the future are summarized.

摘要： 云计算、物联网、社交网络等新兴服务促使人类社会的数据种类和规模正以前所未有的速度增长，大数据时代正式到来。数据从简单的处理对象开始转变为一种基础性资源，如何更好地管理和利用大数据已经成为普遍关注的话题。大数据数据库研究的演变或革命是一个挑战。本文对大数据的基本概念进行剖析，并对其现状进行了综述。在此基础上，阐述大数据处理的基本框架，研究了大数据的关键技术，最后归纳总结大数据时代所面临的新挑战。

3、深度学习的昨天、今天和明天

Abstract: Machine learning is an important area of artificial intelligence. Since 1980s, huge success has been achieved in terms of algorithms, theory, and applications. From 2006, a new machine learning paradigm, named deep learning, has been popular in the research community, and has become a huge wave of technology trend for big data and artificial intelligence. Deep learning simulates the hierarchical structure of human brain, processing data from lower level to higher level, and gradually composing more and more semantic（语义的） concepts. In recent years, Google, Microsoft, IBM, and Baidu have invested a lot of resources into the R&D（Research and Development 研发） of deep learning, making significant progresses on speech recognition, image understanding, natural language processing, and online advertising. In terms of the contribution to real-world applications, deep learning is perhaps the most successful progress made by the machine learning community in the last 10 years. In this article, we will give a high-level overview about the past and current stage of deep learning, discuss the main challenges, and share our views on the future development of deep learning.

Key words: machine learning, deep learning, speech recognition, image recognition, natural language processing, online advertising

摘要： 机器学习是人工智能领域的一个重要学科。自从20世纪80年代以来，机器学习在算法、理论和应用等方面都获得巨大成功.2006年以来，机器学习领域中一个叫“深度学习”的课题开始受到学术界广泛关注，到今天已经成为互联网大数据和人工智能的一个热潮。深度学习通过建立类似于人脑的分层模型结构，对输入数据逐级提取从底层到高层的特征，从而能很好地建立从底层信号到高层语义的映射关系.近年来，谷歌、微软、IBM、百度等拥有大数据的高科技公司相继投入大量资源进行深度学习技术研发，在语音、图像、自然语言、在线广告等领域取得显著进展。从对实际应用的贡献来说，深度学习可能是机器学习领域最近这十年来最成功的研究方向。本文将对深度学习发展的过去和现在做一个全景式的介绍，并讨论深度学习所面临的挑战，以及将来的可能方向.

关键词: 机器学习, 深度学习, 语音识别, 图像识别, 自然语言处理, 在线广告

4、物联网安全综述

Abstract: With the development of smart home, intelligent care and smart car, the application fields of IoT（Internet of Things 物联网） are becoming more and more widespread, and its security and privacy receive more attention by researchers. Currently, the related research on the security of the IoT is still in its initial stage, and most of the research results cannot solve the major security problem in the development of the IoT well. In this paper, we firstly introduce the three-layer logic architecture of the IoT, and outline（概述） the security problems and research priorities of each level. Then we discuss the security issues such as privacy preserving and intrusion detection, which need special attention in the IoT main application scenarios (smart home, intelligent healthcare, car networking, smart grid, and other industrial infrastructure). Though synthesizing（综合） and analyzing the deficiency of existing research and the causes of security problem, we point out five major technical challenges in IoT security. They are privacy protection in data sharing, the equipment security protection under limited resources, more effective intrusion detection and defense systems and method, access control of equipment automation operations and cross-domain authentication of motive device. We finally detail every technical challenge and point out the IoT security research hotspots in future.

Key words: Internet of things, security, privacy, intelligent, survey, challenge

摘要： 随着智能家居、数字医疗、车联网等技术的发展，物联网应用越发普及，其安全问题也受到越来越多研究者的关注.目前，物联网安全的相关研究尚在起步阶段，大部分研究成果还不能完善地解决物联网发展中的安全问题。本文首先对物联网3层逻辑架构进行了介绍，阐述了每个层次的安全问题与研究现状重点;然后分析并讨论了物联网的主要应用场景(智能家居、智能医疗、车联网、智能电网、工业与公共基础设施)中需要特别关注的隐私保护、入侵检测等安全问题;再次，归纳分析了现有研究工作中的不足与安全问题产生的主要原因，指出物联网安全存在的五大技术挑战：数据共享的隐私保护方法、有限资源的设备安全保护方法、更加有效的入侵检测防御系统与设备测试方法、针对自动化操作的访问控制策略、移动设备的跨域认证方法；最后，通过详尽分析这五大技术挑战，指出了物联网安全未来的研究热点.

关键词: 物联网, 安全, 隐私, 智能, 综述, 挑战

5、大数据时代的个人隐私保护

Abstract: With the development of information technology, emerging services based on Web2.0 technologies such as blog, microblog, social networks, and the Internet of things produce various types of data at an unprecedented rate, while cloud computing provides a basic storage infrastructure for big data. All of these lead to the arrival of the big data era. Big data contains great value. Data become the most valuable wealth of the enterprise, but big data also brings grand challenges. Personal privacy protection is one of the major challenges of big data. People on the Internet leave many data footprint with cumulativity（累积性） and relevance. Personal privacy information can be found by gathering data footprint in together. Malicious（恶意的） people use this information for fraud. It brings many trouble or economic loss to personal life. Therefore, the issue of personal privacy has caused extensive concern of the industry and academia. However, there is little work on the protection of personal privacy at present. Firstly, the basic concepts of big data privacy protection are introduced, and the challenges and research on personal privacy concern are discussed. Secondly, the related technology of privacy protection is described from the data layer, application layer and data display layer. Thirdly, several important aspects of the personal privacy laws and industry standards are probed（探测 探究） in the era of big data. Finally, the further research direction of personal privacy protection is put forward.

Key words: personal privacy protection, personal privacy concern, privacy protection technology, big data privacy, big data

摘要： 随着信息技术的发展，以Web2.0技术为基础的博客、微博、社交网络等新兴服务和物联网以前所未有的发展速度产生了类型繁多的数据，而云计算为数据的存储提供了基础平台，这一切造就了大数据时代的正式到来。大数据中蕴藏着巨大的价值，是企业的宝贵财富.但大数据同时也带来了巨大的挑战，个人隐私保护问题就是其中之一。人们在网络上留下了许多数据足迹，这些数据足迹具有累积性和关联性，将多处数据足迹聚集在一起，就可以发现个人的隐私信息.恶意分子利用这些信息进行欺诈等行为，给个人的生活带来了许多麻烦或经济损失，因此大数据的个人隐私问题引起了工业界和学术界的广泛关注。然而，目前关于个人隐私权保护的研究却很少。本文首先介绍了大数据时代个人隐私保护的相关概念，讨论了个人隐私保护面临的挑战和研究问题；然后从数据层、应用层以及数据展示层叙述了个人隐私保护所使用的技术，探讨了个人隐私保护的相关法律以及行业规范的几个重要方面；最后提出了大数据个人隐私保护的进一步研究方向.

关键词: 个人隐私保护, 个人隐私问题, 隐私保护技术, 大数据隐私, 大数据

6、知识表示学习研究进展

Abstract: Knowledge bases are usually represented as networks with entities as nodes and relations as edges. With network representation of knowledge bases, specific algorithms have to be designed to store and utilize knowledge bases, which are usually time consuming and suffer from data sparsity（稀疏度 ）issue. Recently, representation learning, delegated by deep learning, has attracted many attentions in natural language processing, computer vision and speech analysis. Representation learning aims to project the interested objects into a dense, real-valued and low-dimensional semantic（语义的） space, whereas knowledge representation learning focuses on representation learning of entities and relations in knowledge bases. Representation learning can efficiently measure semantic correlations of entities and relations, alleviate（缓解 解决） sparsity issues, and significantly improve the performance of knowledge acquisition, fusion（融合） and inference. In this paper, we will introduce the recent advances of representation learning, summarize the key challenges and possible solutions, and further give a future outlook on the research and application directions.

Key words: knowledge representation, representation learning, knowledge graph, deep learning, distributed representation

摘要： 人们构建的知识库通常被表示为网络形式，节点代表实体，连边代表实体间的关系。在网络表示形式下，人们需要设计专门的图算法存储和利用知识库，存在费时费力的缺点，并受到数据稀疏问题的困扰.最近，以深度学习为代表的表示学习技术在自然语言处理、计算机视觉和语音分析领域受到广泛关注。表示学习旨在将研究对象的语义信息表示为稠密低维实值向量，知识表示学习则面向知识库中的实体和关系进行表示学习.该技术可以在低维空间中高效计算实体和关系的语义联系，有效解决数据稀疏问题，使知识获取、融合和推理的性能得到显著提升.介绍知识表示学习的最新进展，总结该技术面临的主要挑战和可能解决方案，并展望该技术的未来发展方向与前景.

关键词: 知识表示, 表示学习, 知识图谱, 深度学习, 分布式表示

7、贝叶斯机器学习前沿进展综述

Abstract: With the fast growth of big data, statistical machine learning has attracted tremendous attention from both industry and academia, with many successful applications in vision, speech, natural language, and biology. In particular, the last decades have seen the fast development of Bayesian machine learning, which is now representing a very important class of techniques. In this article, we provide an overview（概括，概述） of the recent advances in Bayesian machine learning, including the basics of Bayesian machine learning theory and methods, nonparametric Bayesian methods and inference algorithms, and regularized Bayesian inference. Finally, we also highlight（突出，强调） the challenges and recent progress on large-scale Bayesian learning for big data, and discuss on some future directions.

Key words: Bayesian machine learning, nonparametric methods, regularized methods, learning with big data, big Bayesian learning

摘要： 随着大数据的快速发展，以概率统计为基础的机器学习在近年来受到工业界和学术界的极大关注，并在视觉、语音、自然语言、生物等领域获得很多重要的成功应用，其中贝叶斯方法在过去20多年也得到了快速发展,成为非常重要的一类机器学习方法.本文总结了贝叶斯方法在机器学习中的最新进展，具体内容包括贝叶斯机器学习的基础理论与方法、非参数贝叶斯方法及常用的推理方法、正则化贝叶斯方法等. 最后，还针对大规模贝叶斯学习问题进行了简要的介绍和展望，对其发展趋势作了总结和展望.

关键词: 贝叶斯机器学习, 非参数方法, 正则化方法, 大数据学习, 大数据贝叶斯学习

8、面向深度学习的公平性研究综述

Abstract: Deep learning is an important field of machine learning research, which is widely used in industry for its powerful feature extraction capabilities and advanced performance in many applications. However, due to the bias in training data labeling and model design, research shows that deep learning may aggravate (强化 加剧)human bias and discrimination in some applications, which results in unfairness during the decision-making process, thereby will cause negative impact to both individuals and socials. To improve the reliability of deep learning and promote its development in the field of fairness, we review（评审 审查） the sources of bias in deep learning, debiasing methods for different types biases, fairness measure metrics for measuring the effect of debiasing, and current popular debiasing platforms, based on the existing research work. In the end we explore the open issues in existing fairness research field and future development trends.

Key words: deep learning, algorithm fairness, debiasing method, fairness metric, machine learning

摘要： 深度学习是机器学习研究中的一个重要领域，它具有强大的特征提取能力，且在许多应用中表现出先进的性能，因此在工业界中被广泛应用.然而，由于训练数据标注和模型设计存在偏见，现有的研究表明深度学习在某些应用中可能会强化人类的偏见和歧视，导致决策过程中的不公平现象产生，从而对个人和社会产生潜在的负面影响。为提高深度学习的应用可靠性、推动其在公平领域的发展，针对已有的研究工作，从数据和模型2方面出发，综述了深度学习应用中的偏见来源、针对不同类型偏见的去偏方法、评估去偏效果的公平性评价指标、以及目前主流的去偏平台，最后总结现有公平性研究领域存在的开放问题以及未来的发展趋势.

关键词: 深度学习, 算法公平性, 去偏方法, 公平性指标, 机器学习基于区块链的智能合约技术与应用综述

9、基于区块链的智能合约技术与应用综述

Abstract: With the flourishing development of blockchain technology represented by bitcoin, the blockchain technology has moved from the era of programmable currency into the era of smart contract. The smart contract is an event-driven, state-based code contract and algorithm contract, which has been widely concerned and studied with the deep development of blockchain technology. The protocol（协议，规程） and user interface are applied to complete all steps of the smart contract process. Smart contract enables users to implement personalized logic on the blockchain. The blockchain-based smart contract technology has the characteristics of de-centralization, autonomy, observability, verifiability and information sharing. It can also be effectively applied to build programmable finance and programmable society, which has been widely used in digital payment, financial asset disposal（ 处理; (企业、财产等的)变卖，让与 ）, multi-signature contract, cloud computing, Internet of things, sharing economy and other fields. The survey describes the basic concepts of smart contract technology, its whole life cycle, basic classification and structure, key technology, the art of the state, as well as its application scenarios and the main technology platforms. Its problems encountered（遭遇 遇到） at present are also discussed. Finally, based on the theoretical knowledge of the smart contract, we set up the Ethereum experimental environment and develop a system of crowdsale contract（众筹合约）. The survey is aimed at providing helpful guidance and reference for future research of smart contract based on blockchain technology.

Key words: smart contract, blockchain, ethereum, distributed application, formal method, crowdsale contract

摘要： 随着以比特币为代表的区块链技术的蓬勃发展，区块链技术已经开始逐步超越可编程货币时代而进入智能合约时代.。智能合约(smart contract)是一种由事件驱动的、具有状态的代码合约和算法合同，随着区块链技术的深入发展而受到广泛关注和研究。智能合约利用协议和用户接口完成合约过程的所有步骤，允许用户在区块链上实现个性化的代码逻辑。基于区块链的智能合约技术具有去中心化、自治化、可观察、可验证、可信息共享等特点，可以有效构建可编程金融和可编程社会，广泛应用于数字支付、金融资产处置、多重签名合约、云计算、物联网、共享经济等多个领域。首先阐述了智能合约技术的基本概念、全生命周期、基本分类、基本架构、关键技术、发展现状以及智能合约的主要技术平台；然后探讨了智能合约技术的应用场景以及发展中所存在的问题；最后，基于智能合约的理论知识，搭建了以太坊实验环境并开发了一个众筹智能合约系统，旨在为基于区块链的智能合约技术的研究与发展提供参考与借鉴.

关键词: 智能合约, 区块链, 以太坊, 分布式应用, 形式化方法, 众筹合约

10、云计算系统可靠性研究综述

Abstract: As a new computing paradigm（典范; 范例; 样式）, cloud computing has attracts extensive concerns from both academic and industrial fields. Based on resource virtualization technology, cloud computing provides users with services in the forms of infrastructure, platform and software in a “pay-as-you-go” manner. In the meanwhile, since cloud computing provides highly scalable computing resources, more and more enterprises and organizations choose cloud computing platforms to deploy their scientific or commercial applications. However, with the increasing number of cloud users, cloud data centers continuously expand and the architecture becomes increasingly complex, leading to growing runtime failures in cloud computing systems. Therefore, how to ensure the system reliability in cloud computing systems with large scale and complex architecture has become a huge challenge. This paper first summarizes various failures in cloud systems, introduces several methods to evaluate the reliability of cloud computing, and describes some key fault management mechanisms. Since fault management techniques inevitably increase energy consumption of cloud systems, this paper reviews current researches on the trade-off between reliability and energy efficiency in cloud computing. In the end, we propose some major challenges in current research of cloud computing reliability and concludes our paper.

Key words: cloud computing, virtualization, reliability, fault management, energy consumption

摘要： 云计算作为一种新型计算模式，已经受到了学术界和工业界的广泛关注.基于资源虚拟化技术，云计算能够以按需使用、按使用量付费的方式为用户提供基础设施、平台、软件等服务.因此，越来越多的企业和组织选择云计算来部署他们的科学或商业应用.然而，随着用户数量的不断增加，数据中心的规模在迅速扩大、架构变得日益复杂，导致云计算系统的运行故障频繁发生，造成了巨大的损失.因此在规模巨大、架构复杂的云计算系统中，如何保障系统的可靠性已经成为一个极具挑战性的问题.针对云计算可靠性问题，概述了云计算系统中常见的各种故障，并详细描述了目前云计算中提高可靠性关键的故障管理技术；由于故障管理技术的应用会不可避免地增加系统的能耗，因此介绍了云计算中可靠性与能耗权衡问题的研究现状；最后列举了当前云计算可靠性研究中存在的主要挑战.

关键词: 云计算, 虚拟化, 可靠性, 故障管理, 能耗