Abstract Guardians in a Generation-Based Garbage Collector

理论与方法 / Theoretical and Method86国际非物质文化遗产研究知识图谱可视化分析吴晓晨，王春燕（塔里木大学经济与管理学院，新疆 阿拉尔 843300）摘 要: 非遗是以非物质形态存在于民间、世代相承的传统文化表现形式。

文章通过梳理国际非遗研究文献，客观掌握国际非遗研究方向，以“Web of science核心集合”为数据库，利用Cite Space软件对索引出来的1 304篇文献进行国际、地区、机构、作者网路共现、文献共被引以及关键词聚类等系统化分析，得出国际有关于“intangible cultural heritage”的研究知识图谱。

研究发现，非遗的研究内容方向可以分为3类：民族非遗文化产业管理研究（主要包括区域历史文化、专业科目以及原生态识别）、传承创新与保护政策研究（包括传播表达以及可持续发展保护）、文化旅游的研究（包括标志地区、地方性艺术、空间市场分析）。

研究涉及的学科领域广、分布不均衡、科研力量地域空间上不平衡。

研究的热点随着时间变化，主要有3个研究热点：有非遗概念界定、非遗保存、非遗开发管理。

通过得到的知识图谱可以掌握非遗研究的现状与发展趋势，为今后的研究方向提供数据支持。

关键词：非物质文化遗产；Cite Space软件；文化旅游；可视化中图分类号：G122 文献标志码：AVisualization Analysis of Knowledge Map of International Intangible Cultural HeritageWU Xiaochen, WANG Chunyan（College of Economics and Management, Tarim University, Alar Xinjiang 843300, China）Abstract: Intangible cultural heritage is a form of traditional culture, which exists in non-material form in folk and generation by generation. Through combing the research literature of international non-material cultural heritage, the research direction of international intangible cultural heritage is grasps objectively, and some objective data are provided for future research.This paper uses "Web of science core collection" as the database, and uses Cite Space software to study the 1304 documents which are indexed by international, regional, organization, author network co-occurrence, literature co citation, and keyword cluster analysis.It is concluded that there is an international knowledge map of "intangible cultural heritage". The research found that the research content of intangible cultural heritage can be divided into three categories: the study of national cultural heritage, the research of heritage innovation and protection policy, and the study of cultural tourism. The research covers a wide range of disciplines, uneven distribution, and uneven geographical distribution of research forces. With the change of time, there are three main research hot spots: the definition of intangible cultural heritage, the protection and preservation of intangible cultural heritage, and the development and management of intangible cultural heritage. Through the knowledge map, we can grasp the current situation and development trend of intangible cultural heritage research, so as to provide data support for future research directions.Keywords: intangible cultural heritage; Cite Space software; culture travel; visualization作者简介：吴晓晨（1998-），女，本科，主要研究方向为旅游管理。

由于我国经济的高速发展,计算机科学技术在当前各个科技领域中迅速发展,成为了应用最广泛的技术之一.其中数据库又是计算机科学技术中发展最快,应用最广泛的重要分支之一.它已成为计算机信息系统和计算机应用系统的重要技术基础和支柱。

下面是数据库英文参考文献的分享，希望对你有所帮助。

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第47卷第4期Vol.47No.4计算机工程Computer Engineering2021年4月April 2021图神经网络综述王健宗，孔令炜，黄章成，肖京（平安科技（深圳）有限公司联邦学习技术部，广东深圳518063）摘要：随着互联网和计算机信息技术的不断发展，图神经网络已成为人工智能和大数据处理领域的重要研究方向。

图神经网络可对相邻节点间的信息进行有效传播和聚合，并将深度学习理念应用于非欧几里德空间的数据处理中。

简述图计算、图数据库、知识图谱、图神经网络等图结构的相关研究进展，从频域和空间域角度分析与比较基于不同信息聚合方式的图神经网络结构，重点讨论图神经网络与深度学习技术相结合的研究领域，总结归纳图神经网络在动作检测、图系统、文本和图像处理任务中的具体应用，并对图神经网络未来的发展方向进行展望。

关键词：图神经网络；图结构；图计算；深度学习；频域；空间域开放科学（资源服务）标志码（OSID ）：中文引用格式：王健宗，孔令炜，黄章成，等.图神经网络综述［J ］.计算机工程，2021，47（4）：1-12.英文引用格式：WANG Jianzong ，KONG Lingwei ，HUANG Zhangcheng ，et al.Survey of graph neural network ［J ］.Computer Engineering ，2021，47（4）：1-12.Survey of Graph Neural NetworkWANG Jianzong ，KONG Lingwei ，HUANG Zhangcheng ，XIAO Jing（Federated Learning Technology Department ，Ping An Technology （Shenzhen ）Co.，Ltd.，Shenzhen ，Guangdong 518063，China ）【Abstract 】With the continuous development of the computer and Internet technologies ，graph neural network has become an important research area in artificial intelligence and big data.Graph neural network can effectively transmit and aggregate information between neighboring nodes ，and applies the concept of deep learning to the data processing of non-Euclidean space.This paper briefly introduces the research progress of graph computing ，graph database ，knowledge graph ，graph neural network and other graph-based techniques.It also analyses and compares graph neural network structures based on different information aggregation modes in the spectral and spatial domain.Then the paper discusses research fields that combine graph neural network with deep learning ，and summarizes the specific applications of graph neural networks in action detection ，graph systems ，text and image processing tasks.Finally ，it prospects the future development research directions of graph neural networks.【Key words 】graph neural network ；graph structure ；graph computing ；deep learning ；spectral domain ；spatial domain DOI ：10.19678/j.issn.1000-3428.00583820概述近年来，深度学习技术逐渐成为人工智能领域的研究热点和主流发展方向，主要应用于高维特征规则分布的非欧几里德数据处理中，并且在图像处理、语音识别和语义理解［1］等领域取得了显著成果。

收稿日期:2014-09-30 修回日期:2014-12-02基金项目:教育部人文社会科学研究青年基金项目 专利流氓型态与特征及其影响研究”(编号:13YJC630222);国家自然科学基金 基于专利布局战略与社会网络分析观点探讨影响专利价值的因素研究”(编号:71403191)作者简介:张克群(1980-),男,博士,副教授,研究方向:技术创新管理和技术策略管理;夏伟伟(1991-),男,硕士研究生,研究方向:技术创新管理;郝　娟(1990-),女,硕士研究生,研究方向:技术创新管理;张　曦(1981-),男,硕士,高级工程师,研究方向:专利竞争情报分析㊂专利价值的影响因素分析 专利布局战略观点*张克群1　夏伟伟1　郝　娟1　张　曦2(1.武汉大学经济与管理学院　武汉　430072;2.工业和信息化部电子知识产权咨询服务中心　北京　100040)摘　要　以专利诉讼作为专利价值的代理变量,以专利权利要求数㊁先前技艺数作为自变量,同时引入专利家族深度和专利家族强度作为反映企业专利布局战略的代理指标,结合7146件Thomson Innovation 数据库收录的截至2011年5月31日美国授权的LED 专利文献信息,采用Logistic 回归方法探索专利价值的影响因素㊂实证结果显示:专利权利要求数㊁先前技艺数㊁专利家族深度和专利家族强度对专利价值有显著的正向影响㊂关键词　专利价值　专利布局　企业战略　专利诉讼　专利家族中图分类号　F273.1 文献标识码　A 文章编号　1002-1965(2015)01-0072-05DOI 10.3969/j.issn.1002-1965.2015.01.014The Influencing Factors of Patent Value :A Patent Deployment Strategy ViewZhang Kequn 1　Xia Weiwei 1　Hao Juan 1　Zhang Xi 2(1.School of Economics and Management ,Wuhan University ,Wuhan 430072;2.Intellectual Property Center ,Ministry of Industry and Information Technology ,Beijing 100040)Abstract Patent litigation is utilized as the proxy variable of patent value ,and number of patent claims and number of backward citations are defined as the independent variables ,combining with patent family depth and earn plan ration representative for company 's patent de⁃ployment strategy.With 7146pieces of USA -authorized LED patent literature information collected from Thomson Innovation database up to May 31st ,2011,a logistic regression model is built to explore the influencing factors of patent value.The empirical results demonstrate that patent value has significant positive relationships with number of patent claims ,number of backward citations ,patent family depth and earn plan ratio.Key words patent value patent deployment enterprise strategy patent litigation patent family0　引　言LED 是一种能将电能转换为光能的半导体,其技术领域广泛,涉及衬底㊁外延㊁芯片㊁封装㊁应用等部分㊂LED 企业的主要竞争力体现在技术水平上,因此申请专利是各企业保护自身产品的有效机制㊂专利作为企业的一项无形资产,不仅是一种能将竞争对手排除在自身所属技术领域之外的隔绝机制,更是一种能够增强企业竞争力㊁提升企业价值的战略武器㊂专利诉讼作为企业重要的战略工具,其实施与否在很大程度上受到专利价值的影响[1]㊂当侵权行为发生时,有两条途径可供专利权人选择:一是达成庭外和解,二是通过专利诉讼解决侵权问题㊂与达成庭外和解相比,提起诉讼的成本很高,其中不仅包括巨额的法律费用,而且一般来说,法院审判历时较长,这无疑会分散企业的人力㊁物力㊁财力,并且带来一系列不确定性,从而对企业的经营产生一定程度的负面影响㊂所以,如果专利不能为企业带来足够价值,企业不会选择诉讼,专利诉讼就可以成为评判专利是否具有价值的标准[2]㊂专利权利人和侵权者在诉讼前会对结果进行预期,二者对胜诉概率有不同的期望㊂在专利权人获胜第34卷　第1期2015年1月 情　报　杂　志JOURNAL OF INTELLIGENCE Vol.34　No.1Jan.　2015的情况下,其从该专利中获得利润和损害赔偿费;若专利权人败诉,该专利发明就不再只有一个企业使用,因此企业利润会降低,且专利权利人要支付自身和侵权人的法律费用㊂所以,企业选择诉讼的原因可能是诉讼的收益大于诉讼的成本㊂对于侵权者来说,其也会进行类似的考量㊂由此可见,专利诉讼行为的发生取决于双方当事人通过该专利所获得的利润和双方当事人对判决结果的不同预期㊂当双方当事人对判决结果的预期越趋于一致时,他们越倾向于达成和解[3]㊂利润与专利价值密切相关,一个具有价值的专利能给企业带来更高利润㊂因此,专利价值对于实施专利诉讼行为的动机具有正向影响[1,4]㊂专利价值越高,专利侵权的概率越高,同时双方当事人选择通过诉讼而不是达成和解的概率也越高㊂另外,专利诉讼对专利价值也有反向作用关系,专利诉讼的结果影响了专利价值,专利诉讼败诉会使得该专利前景最小化专利价值,而当专利可以维护企业市场份额并胜诉时,其价值就会大幅上升[5]㊂因此,Allison et al.认为有足够强烈的相信涉讼专利能够作为专利价值的代理变量[2]㊂另外在实务上,凡是能够被专利权人挑选出来参与诉讼的专利,通常在其心目中一定具有相当的分量,同时能够经过司法考验的专利,相较于一般的专利必定更具备在专利有效性方面的稳定性,当然也就是具有更高的为专利权人创造价值的潜力㊂随着商业活动全球化的发展,企业试图利用专利法律价值优势,通过基于法律的商业策略来防止竞争对手进入市场,所以从法律视角上,专利诉讼就成为一种评估专利价值的方式[6],先前学者也通常将专利是否涉讼视为专利是否具有价值的代理变量㊂在以往的文献中,专利权利要求数和先前技艺数通常被当作衡量专利价值的标准,专利权利要求数和先前技艺数越高,专利价值也就越大[2,7]㊂但是除此之外,专利的价值还体现在企业战略上㊂专利家族是一件专利在不同国家的申请集合,也是其后续衍生专利的申请集合,反映了企业对某市场区域和专利的重视程度㊂专利被引证数代表了该专利对后续创新者和竞争者的吸引程度,在一定意义上反映了专利的市场价值㊂如果一项专利的被引证次数越多,则说明该专利所蕴含的技术知识是该技术领域的基础知识或核心知识,能够对后续创新产生积极影响[8]㊂专利被引证数越高意味着该专利的吸引力越大,市场价值越高㊂由此,本文根据专利家族和专利被引证数引申出了两个变量 专利家族深度和专利家族强度,分别反映企业对市场的重视程度和企业战略的正确程度㊂首先,本文对已有文献进行回顾并提出相关假设;其次,本文对所选取的样本进行了描述性统计分析,使用T检验比较诉讼专利和未诉讼专利的区别,初步验证假设㊂最后,本文对样本的诉讼概率进行了回归分析以进一步验证假设㊂最后提出建议以提供企业进行有效的专利管理㊂1　文献探讨与研究假说　1.1　价值专利与涉讼专利　在价值专利与涉讼专利的关联方面,过去已有许多学者发现这两者之间的确具有密切的相关性㊂Allison et al.进一步主张价值专利与涉讼专利之间的关系是相当强固且具有双向性的[2]㊂这是因为一般而言,涉讼专利往往比其他非涉讼专利要来得更有价值;反之,价值专利也要比其他专利更容易被用来进行诉讼㊂为什么涉讼专利往往比其他非涉讼专利要来得更有价值?从实务的角度而言,诉讼的成本绝对远远高于仅仅获得专利的成本,如果涉讼并不能够表明该专利是有价值的,那么这意味着专利权人在使用专利主张权利的机制上是不合理的㊂再者,也正是因为专利诉讼是极其昂贵,对于一个理性的专利权人而言,除非他预期能够从中获得的回报率至少有几百万元美金,否则并不会轻易提起诉讼㊂此外,Lanjouw and Schankerman也认为大部份价值专利都很有可能成为涉讼专利[1]㊂当然,价值的专利并不一定必须非要藉由诉讼的手段才能够为专利权人创造私有价值,还可以透过其他授权或者让与等方式来产生获利,甚至这些方式有可能会比藉由诉讼手段获得更高的利益,因此必定也存在一些非涉讼专利仍然能够为专利权人创造私有价值㊂也就是说,涉讼专利是价值专利的子集合㊂然而专利价值并不是正态分布,而是呈现高度不对称分布,只有少数约10%的专利能够产生80%~ 90%以上的价值㊂大多数专利为专利权人带来的价值是非常小的,甚至不足以支付其专利本身的维护费用㊂因此,本研究也认为涉讼专利是价值专利中最具代表的子集合㊂　1.2　专利价值决定因素　为了识别出专利价值的决定因素并找出最具价值的专利,先前学者已经开发了多种模型,主要是将自变量划分为四大类别,包括专利特征(向前引证数㊁向后引证数㊁IPC分类数和发明者人数等)㊁专利所有权(联合申请㊁跨界申请㊁专利组合规模㊁市场规模等)㊁内部信息(专利申请动机和发明背景)㊁申请策略(专利优先权数和权利要求数等)[9]㊂从这些变量中,可以提取诸多有价值的专利信息,比如技术重要性10㊁现存技术背景㊁创新与基础研究间的联系[11]㊁技术范围[12]㊁研究努力程度[13]㊁法律保护范围等[14]㊂㊃37㊃　第1期 张克群,等:专利价值的影响因素分析 专利布局战略观点同样,专利价值的指标可以分为两类:基于市场的指标和基于专利的指标[9]㊂最著名的基于市场的专利价值指标Tobin’s q和股票市值(企业层面),以及赔偿款㊁发明者和管理者对专利价值的评估和收购活动(专利层面)㊂相比而言,基于专利的专利价值指标则更加多元化,可以分为五类:技术重要性(专利向前引证)㊁地理重要性(专利家族)㊁长度(续期)㊁授权决定(授权专利)和法律争端(诉讼概率㊁反对概率)㊂经过检验,这五类专利价值指标与专利价值正相关[9]㊂基于对专利价值指标和专利价值决定因素的不同选择,学者们做了各种实证研究来预测专利的潜在价值㊂先前研究都一致地发现专利向前引证数㊁专利家族㊁续期㊁法律争端和专利申请策略指标与专利价值正相关,然而,其他的专利价值决定因素与专利价值的关系是模糊的[9]㊂这种现象需要我们从新的视角进一步研究专利价值的决定因素㊂　1.3　专利权利要求数与专利价值　在知识经济时代,专利法律问题显得尤为重要㊂为了衡量专利价值,可以从专利文件中获取相应指标以进行分析㊂其中,专利权利要求㊁先前技艺数㊁专利被引证数以及专利家族数是最基本的专利指标㊂Allison et al.发现有价值的专利包含了更多的权利要求㊁被引证数和先前技艺[2]㊂Nerkar et al.认为专利包含的权利要求数越多,知识产权的价值也越大[15]㊂专利权利要求是以专利申请书为依据,说明专利技术特征,清楚并简要地写出要求专利保护范围,并在一定条件下提出的一项或几项专利权项㊂其作用在于确定申请人请求的专利保护范围和判定他人是否侵权㊂对于专利权人来说,越多的专利权利要求意味着越广泛的专利保护范围,专利保护范围最大化既有利于对专利权人的保护又有利于鼓励发明创造,但可能会妨碍技术的传播和应用,因而会引发更多的侵权案件㊂Lanjouw and Schankerman(2001)发现权利要求数上升10%,意味着样本中专利诉讼数量会增加1. 4%[1]㊂因此,专利权利要求数越多,专利保护范围越广,专利也就越有价值㊂因此,本研究提出假说1:假说1:专利权利要求数对专利价值有正向影响㊂　1.4　先前技艺数与专利价值　先前技艺是对已存在的相关专利文献和非专利文献的引用,体现了该专利的技术基础[16]㊂Lanjouw and Schankerman研究发现,先前技艺数与权利要求数之比每增加1,专利诉讼概率就提高22%[1]㊂Allison et al.的研究结论显示,诉讼专利比未诉讼专利引用了更多的先前技艺,而且也更可能被其他人引用[2]㊂一般来说,专利引证方式越复杂,揭示的背景技术越充分㊂因此,先前技艺越多,代表该专利是在那些已有技术的基础上进一步研发或改进的专利,也表明该专利的技术实在,针对性强,其专利价值越大,专利诉讼的概率越大㊂同时,先前技艺显示了相关企业在该技术领域的活动积极性,技术领域的拥挤也可能导致侵权和诉讼行为㊂另外,通过研究该专利提供的现有技术,其他企业可能重新组合现有技术而模仿该发明,从而可能导致侵权和诉讼行为㊂因此,本研究提出假说2:假说2:先前技艺数对专利价值有正向影响㊂　1.5　专利布局战略与专利价值　除了专利本身的特征外,企业在专利上的战略部署也能反映专利价值,而专利家族是反映企业专利战略的基本指标[17]㊂专利家族是一件专利在不同国家申请的集合和其后续衍生专利不同申请的集合㊂专利家族意味着企业对技术和市场的重视程度,企业可以在几个重要的国家申请专利来获得足够保护,以防止竞争对手通过模仿其技术削弱竞争优势和市场份额㊂当企业申请更多的专利家族时,也要相应地支付更多费用㊂所以,除非该项专利技术能够为企业带来更多的盈利,否则企业不会投资去扩大专利家族[9]㊂故可以从专利家族布局中预测企业的潜在销售市场,因此专利家族数量代表了该技术和未来市场的重要性[18]㊂专利被引证数代表了该专利对后续创新者和竞争者的吸引程度,其不仅是衡量知识溢出效应的指标,也是衡量企业市场价值的指标㊂如果一项专利具有更多的被引证数,则说明该专利的独特技术更有能力影响后续创新并吸引更多竞争者,在企业外部享有更高的声誉㊂而竞争源于销售市场,所以专利的被引证数越多,意味着该专利越有市场价值㊂因此专利被引证数提供了检验该项技术是否重要以及是否能够影响和吸引竞争者的重要指标[18]㊂从专利家族和专利被引证数中,我们可以引申出两个指标来衡量企业的战略部署㊂一是专利家族深度,其是专利家族数与专利家族申请国数之比,代表每个专利家族国家平均所拥有的专利家族数量,反映了企业对该区域市场的重视程度㊂专利家族深度越深,代表企业越重视该区域的市场,说明企业在该区域的投资能够获得高额回报,该专利是有很高价值的㊂因此,本研究提出假说3:假说3:专利家族深度对专利价值有正向影响㊂专利家族强度,表示企业基于专利家族申请战略而使得专利能够被后续创新者和竞争者引证的程度㊂专利家族强度越大,意味着企业将资源合理投入到了具有较高竞争力的专利中,因而企业的战略越正确,该专利越有价值㊂因此,本研究提出假说4:假说4:专利家族强度对专利价值有正向影响㊂㊃47㊃ 情　报　杂　志 第34卷2　研究方法　2.1　样本选取与数据收集　本文的专利数据来源于Thomson Innovation数据库收录的截至2011年5月31日美国授权专利文献信息㊂检索的技术领域包括外延生长㊁LED芯片制作以及LED芯片封装技术,不包括终端产品应用技术㊂经过初步检索共检得40330件专利,人工筛选后属于本次分析范围内技术主题专利共计7164件㊂为了找出符合LED行业的技术领域,本研究组织了深度访谈,我们分别访谈了十位在LED行业参与过项目研发且具有超过十年研发经验的高级专家㊂通过专家访谈,我们确定了专利检索的关键词(见表1),得到了本次研究所需的数据㊂本研究采用了Westlaw专利诉讼数据库根据样本中的专利编号来识别每笔专利是否涉讼㊂最终纳入分析的样本包含未诉讼专利7100笔,诉讼专利64笔㊂　2.2　变量操作型定义2.2.1　因变量　诉讼/未诉讼专利:因变量是分类变量,当专利发生过诉讼时,该变量取值为1;当专利未曾发生诉讼时,该变量取值为0㊂根据先前文献,本研究使用 诉讼专利”作为 价值专利”的代理变量㊂数据来源于Westlaw,该数据库可以提供各国的法律资料,通过输入样本的专利编号可以确保本研究所取样本的专利是否涉讼㊂2.2.2　自变量　专利权利要求数:独立权利要求数和非独立权利要求数之和㊂该变量是离散变量,为大于或等于1的整数㊂数据来源于美国专利和商标局USPTO提供的Patent Full-Text and Image数据库,该数据库包括了所有美国授权专利的申请书文案,专利权利要求数可通过在该数据库中搜索专利编号获得㊂先前技艺数:专利文献数和非专利文献数之和,其中专利文献数包括美国专利文献数和外国专利文献数㊂该变量是离散变量,为大于或等于0的整数㊂数据来源于USPTO提供的Patent Full-Text and Image 数据库,该数据可以通过提供样本的专利编号搜索获得㊂专利家族深度:专利家族数与专利家族申请国数之比㊂该变量是大于或等于1的连续变量,数据来源于欧洲专利局网站esp@cenet提供的国际专利文献中心INPADOC专利数据库,该数据库可以提供世界范围的专利家族信息㊂专利家族数和专利家族申请国数可以通过在该数据库中提供样本的专利编号搜索获得㊂专利家族强度:专利被引证数与专利家族数之比㊂该变量是大于或等于0的连续变量,数据来源于USP⁃TO提供的Patent Full-Text and Image数据库(通过搜索专利编号以获得专利被引证数)和欧洲专利局网站esp@cenet提供的INPADOC专利数据库(通过搜索专利编号以获得专利家族数)㊂表1　专利检索关键词技术领域检索关键词外延LED㊁light-emitting diode㊁light emitting diode㊁Solid statelight㊁epitaxy㊁substrate㊁GaN㊁Nitride㊁Group III㊁MBE㊁LPE㊁HVPE㊁MOCVD㊁MOVPE㊁OMVPE㊁Metal OrganicChemical Vapor Deposition芯片LED㊁light-emitting diode㊁light emitting diode㊁Solid statelight㊁chip㊁electrode㊁Flip chip㊁Flip-chip㊁vertical electrode㊁vertical structure surface rough㊁Lithography㊁passivate㊁bond⁃ing㊁ITO㊁transparent conductive oxide layer㊁light reflectinglayer㊁reflector㊁annealing㊁sputtering㊁LED array㊁PBG㊁Photon⁃ic Band-Gap㊁photo crystal㊁Imprint Nano㊁wet etching㊁RIE㊁Reactive Ion Etching㊁IBE㊁Ion beam etch㊁ICP㊁inductivelycoupled plasma㊁laser scribe㊁diamond scribe㊁diamond dicing 封装LED㊁light-emitting diode㊁light emitting diode㊁Solid statelight㊁package㊁encapsulate㊁piranha㊁SMD㊁SMT㊁SurfaceMounted Device㊁Surface Mount Technology㊁array㊁heat sink㊁Phospho㊁Fluorescent Powder㊁epoxy resin㊁glue㊁holder㊁wire㊁coat㊁dispens㊁Die bond㊁wire bond　2.3　实证结果与分析　表2显示了模型中所有变量的描述性统计分析,包括最小值㊁最大值㊁均值和标准差㊂数据显示除了样本专利的先前技艺数标准差较大,为37.5089外,其他变量的标准差都较小㊂说明从整体上看,各变量的分布比较集中㊂表2　描述性统计量 变量最小值最大值均值标准差诉讼/未诉讼专利010.00890.0941专利权利要求数122816.867513.8459先前技艺数074720.215437.5089专利家族深度0.5531.82902.3972专利家族强度02413.796411.2474 本研究使用相关分析㊁T检验㊁Logistic回归分析方法充分地应证所提出的假设㊂相关分析:表3分析了该模型所有变量的相关关系㊂由表3可知,专利权利要求数㊁先前技艺数㊁专利家族深度和专利家族强度与专利价值在1%的显著性水平上呈正相关关系,这说明模型中的所有自变量可能对专利价值有正向影响㊂表3　相关系数矩阵 变量1.2.3.4.诉讼/未诉讼专利1专利权利要求数0.0455**1先前技艺数0.0457**0.1894**1专利家族深度0.0600**0.1833**0.3064**1专利家族强度0.0319**0.0348**-0.0628**-0.0793** T检验:本文对诉讼专利样本和未诉讼专利样本的四个变量进行了T检验,结果如表4所示㊂四个变量在两组样本中均存在显著性差异,其P值都小于㊃57㊃　第1期 张克群,等:专利价值的影响因素分析 专利布局战略观点0.01㊂诉讼专利中,专利权利要求数㊁先前技艺数㊁专利家族深度和专利家族强度的均值都显著高于未诉讼专利,这与之前的假设是一致的㊂专利权利要求数㊁先前技艺数通常是衡量专利价值的标准,而专利家族深度和专利家族强度反映了企业的战略,进而体现了专利价值㊂因为诉讼专利更具价值,所以其在这四个变量上的均值更高㊂表4　诉讼专利与未诉讼专利的特征变量诉讼专利未诉讼专利t值p值专利权利要求数23.5016.81-3.85310.0001先前技艺数38.2720.05-3.87090.0001专利家族深度3.34281.8153-5.08330.0000专利家族强度7.57563.7623-2.70140.0069 Logistic回归分析:本文以专利诉讼是否涉讼为因变量,以专利权利要求数㊁先前技艺数㊁专利家族深度和专利家族强度为自变量进行Logistic回归分析,回归结果如表5所示㊂回归结果支持了先前假设,所有自变量都对专利价值有显著的正向影响,其中专利家族深度和专利家族强度在1%的显著性水平上对专利价值产生正向影响,专利权利要求数和先前技艺数在5%的显著性水平上对专利价值产生正向影响㊂从各变量的系数和显著性水平可看出,专利家族深度和专利家族强度对专利价值的影响程度大于专利权利要求数和先前技艺数㊂表5　回归分析结果变量模型截距-5.299**专利权利要求数0.014*先前技艺数0.004*专利家族深度0.066**专利家族强度0.014**Log Likelihood-353.3196Prob>χ20.0001 注:Note:**p<0.01,*p<0.053　研究结论与建议本文根据Thomson Innovation数据库收录的截至2011年5月31日的美国授权专利文献信息,选取LED领域中的专利为样本,进行专利分析,其中未诉讼专利7100笔,诉讼专利64笔㊂通过对未诉讼专利和诉讼专利进行描述性统计分析,该两种专利在权利要求数㊁先前技艺数㊁专利家族深度和专利家族强度上有显著性差别㊂本文构建了回归模型,上述四个变量对专利价值有正向影响㊂企业可以根据本文研究结果评价专利价值㊁改进专利组合并采取相应应对措施㊂由于专利权利要求数和先前技艺数对专利价值有正向影响,本文建议企业在申请专利时,应该尽量具体化专利权利要求,增加专利权利要求数量,并尽可能多引用先前技艺,为所申请专利打好坚实的理论基础,提高专利价值㊂专利家族深度和专利家族强度是两个衡量企业战略的指标,本文研究表明这两个指标对专利价值也产生正向影响㊂专利家族深度表示企业对某区域市场的重视程度,此指标越大表明该市场能够给企业带来越高回报,因而该市场的地位在战略上对企业越重要㊂所以本文建议企业在能够占有较大市场份额并获得高额回报的区域申请专利及后续专利,以防止竞争对手进入该技术领域并挤占市场,最终提高专利价值㊂而专利家族强度表示企业的专利家族申请战略能够赢得后续创新者和竞争者追随的能力,该指标低则表明企业将过多资源投入到了具有较小竞争力的专利上,因此该专利对外部竞争者的吸引程度不够,专利价值较低㊂所以本文建议企业在申请专利时,要仔细考虑专利布局规划因素,包括该技术在目标市场的新颖程度㊁相关产品在目标市场的销售情况等,以此判断该专利能否在当地获得竞争者的追随,以提高专利价值㊂最后,对于竞争者来说,只有将具有价值的专利进行商业化推广,才能提升企业价值㊂所以,竞争者可以根据本模型评估专利价值以寻找潜在的目标专利,并与专利权人协商通过专利授权以获得专利使用权㊂而且,竞争者也可以通过分析双方的专利布局策略以寻求潜在的合作机会,以交互授权的方式实现资源互补式发展㊂参考文献[1]　Lanjouw J O,Schankerman M.Characteristics of Patent Litiga⁃tion:A Window on Competition[J].The Rand Journal of Eco⁃nomics,2001,32(1):129-151.[2]　Allison J R,Lemley M A,Moore K 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——当代美国史学的曲折反映程群（华东理工大学哲学与政治学院讲师，上海 200237）摘要：史学“碎化”是美国史学重建中面临的一大难题。

史学“综合”几乎是大多数美国史学家的心愿。

但也有史学家提出，关键并不在于史学“综合”，而在于重建史学的宏大叙事。

关于宏大叙事的各种解读有助于我们理解史学中的“宏大叙事”。

美国史学中的宏大叙事在不同的历史阶段有着不同的曲折反映。

由于各种因素的影响，二战后的美国史学出现疏离宏大叙事的趋势。

史学家们惊叹宏大叙事的缺失，呼吁和设计宏大叙事的重建与复归。

当代美国史学宏大叙事的重建面临种种难题。

这些难题从理论上讲是由于宏观社会理论的缺失与后现代理论的瓦解作用造成的，这是当代西方史学乃至整个世界史学需要直面的问题。

而宏观社会理论的缺失、后现代理论的产生与宏大叙事的缺失一样都可以追溯到更深层的社会历史根源。

关于美国历史的宏大叙事的前景如何，只有将来的美国史学家能够回答。

关键词：宏大叙事；美国史学；缺失；重建Loss and Reconstruction of Grand Narrative？——In the Current American HistoriographyCHENG Qun(Philosophy and Politics School, East China University of Science and Technology, Shanghai, China)Key Words: Grand Narrative American Historiography Loss ReconstructionAbstract:As fragmentation was a main problem for reconstruction of American historiography, most American historians appealed for the synthesis of history. But some historians considered that the resolution was not to do the synthesis but to reconstruct the grand narrative of history. Grand narrative in American historiography revealed itself differently on different historical stages. For the influence of various ingredients, American historiography after World War II tended to be alien from the grand narrative. So historians lamented the loss of grand narrative and gave a lot of designs of reconstruction of it. These designs confronted some problems, which theoretically resulted from the lack of grand social theory and the deconstruction of postmodernism, which like the loss of grand narrative have their social and historical roots. As for the future of the grand narrative in American historiography, it is waiting the future American historians to answer.——当代美国史学的曲折反映程群史学“碎化”是美国史学重建面临的一大难题，史学“综合”是大多数美国史学家的心愿。

扩展巴科斯范式（转⾃维基）https:///wiki/%E6%89%A9%E5%B1%95%E5%B7%B4%E7%A7%91%E6%96%AF%E8%8C%83%E5%BC%8F扩展巴科斯范式[]维基百科，⾃由的百科全书扩展巴科斯-瑙尔范式(EBNF, Extended Backus–Naur Form)是表达作为描述计算机和的正规⽅式的的(metalanguage)符号表⽰法。

它是基本(BNF)元语法符号表⽰法的⼀种扩展。

它最初由开发，最常⽤的 EBNF 变体由标准，特别是 ISO-14977 所定义。

⽬录[隐藏]基本[]，如由即可视字符、数字、标点符号、空⽩字符等组成的的。

EBNF 定义了把各符号序列分别指派到的:digit excluding zero = "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" ;digit = "0" | digit excluding zero ;这个产⽣规则定义了在这个指派的左端的⾮终结符digit。

竖杠表⽰可供选择，⽽终结符被引号包围，最后跟着分号作为终⽌字符。

所以digit是⼀个 "0"或可以是 "1或2或3直到9的⼀个digit excluding zero"。

产⽣规则还可以包括由逗号分隔的⼀序列终结符或⾮终结符:twelve = "1" , "2" ;two hundred one = "2" , "0" , "1" ;three hundred twelve = "3" , twelve ;twelve thousand two hundred one = twelve , two hundred one ;可以省略或重复的表达式可以通过花括号 { ... } 表⽰:natural number = digit excluding zero , { digit } ;在这种情况下，字符串1, 2, ...,10,...,12345,... 都是正确的表达式。

人工智能是一门新兴的具有挑战力的学科。

自人工智能诞生以来,发展迅速,产生了许多分支。

诸如强化学习、模拟环境、智能硬件、机器学习等。

但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。

下面是搜索整理的人工智能英文参考文献的分享，供大家借鉴参考。

人工智能英文参考文献一：[1]Lars Egevad,Peter Str?m,Kimmo Kartasalo,Henrik Olsson,Hemamali Samaratunga,Brett Delahunt,Martin Eklund. The utility of artificial intelligence in the assessment of prostate pathology[J]. Histopathology,2020,76(6).[2]Rudy van Belkom. The Impact of Artificial Intelligence on the Activities ofa Futurist[J]. World Futures Review,2020,12(2).[3]Reza Hafezi. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments[J]. World Futures Review,2020,12(2).[4]Alejandro Díaz-Domínguez. How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence[J]. World Futures Review,2020,12(2).[5]Russell T. Warne,Jared Z. Burton. Beliefs About Human Intelligence in a Sample of Teachers and Nonteachers[J]. Journal for the Education of the Gifted,2020,43(2).[6]Russell Belk,Mariam Humayun,Ahir Gopaldas. Artificial Life[J]. Journal of Macromarketing,2020,40(2).[7]Walter Kehl,Mike Jackson,Alessandro Fergnani. Natural Language Processing and Futures Studies[J]. World Futures Review,2020,12(2).[8]Anne Boysen. Mine the Gap: Augmenting Foresight Methodologies with Data Analytics[J]. World Futures Review,2020,12(2).[9]Marco Bevolo,Filiberto Amati. The Potential Role of AI in Anticipating Futures from a Design Process Perspective: From the Reflexive Description of “Design” to a Discussion of Influences by the Inclusion of AI in the Futures Research Process[J]. World Futures Review,2020,12(2).[10]Lan Xu,Paul Tu,Qian Tang,Dan Seli?teanu. Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)[J]. Complexity,2020,2020.[11]L. Grant,X. Xue,Z. Vajihi,A. Azuelos,S. Rosenthal,D. Hopkins,R. Aroutiunian,B. Unger,A. Guttman,M. Afilalo. LO32: Artificial intelligence to predict disposition to improve flow in the emergency department[J]. CJEM,2020,22(S1).[12]A. Kirubarajan,A. Taher,S. Khan,S. Masood. P071: Artificial intelligence in emergency medicine: A scoping review[J]. CJEM,2020,22(S1).[13]L. Grant,P. Joo,B. Eng,A. Carrington,M. Nemnom,V. Thiruganasambandamoorthy. LO22: Risk-stratification of emergency department syncope by artificial intelligence using machine learning: human, statistics or machine[J]. CJEM,2020,22(S1).[14]Riva Giuseppe,Riva Eleonora. OS for Ind Robots: Manufacturing Robots Get Smarter Thanks to Artificial Intelligence.[J]. Cyberpsychology, behavior and social networking,2020,23(5).[15]Markus M. Obmann,Aurelio Cosentino,Joshy Cyriac,Verena Hofmann,Bram Stieltjes,Daniel T. Boll,Benjamin M. Yeh,Matthias R. Benz. Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT[J]. Abdominal Radiology,2020,45(1).[16]Haytham H. Elmousalami,Mahmoud Elaskary. Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence[J]. Journal of Petroleum Exploration and Production Technology,2020,10(10).[17]Rüdiger Schulz-Wendtland,Karin Bock. Bildgebung in der Mammadiagnostik –Ein Ausblick <trans-title xml:lang="en">Imaging in breast diagnostics—an outlook [J]. Der Gyn?kologe,2020,53(6).</trans-title>[18]Nowakowski Piotr,Szwarc Krzysztof,Boryczka Urszula. Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection[J]. Science of the Total Environment,2020,730.[19]Wang Huaizhi,Liu Yangyang,Zhou Bin,Li Canbing,Cao Guangzhong,Voropai Nikolai,Barakhtenko Evgeny. Taxonomy research of artificial intelligence for deterministic solar power forecasting[J]. Energy Conversion and Management,2020,214.[20]Kagemoto Hiroshi. Forecasting a water-surface wave train with artificial intelligence- A case study[J]. Ocean Engineering,2020,207.[21]Tomonori Aoki,Atsuo Yamada,Kazuharu Aoyama,Hiroaki Saito,Gota Fujisawa,Nariaki Odawara,Ryo Kondo,Akiyoshi Tsuboi,Rei Ishibashi,Ayako Nakada,Ryota Niikura,Mitsuhiro Fujishiro,Shiro Oka,Soichiro Ishihara,Tomoki Matsuda,Masato Nakahori,Shinji Tanaka,Kazuhiko Koike,Tomohiro Tada. Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading[J]. Digestive Endoscopy,2020,32(4).[22]Masashi Fujii,Hajime Isomoto. Next generation of endoscopy: Harmony with artificial intelligence and robotic‐assisted devices[J]. Digestive Endoscopy,2020,32(4).[23]Roberto Verganti,Luca Vendraminelli,Marco Iansiti. Innovation and Design in the Age of Artificial Intelligence[J]. Journal of Product Innovation Management,2020,37(3).[24]Yuval Elbaz,David Furman,Maytal Caspary Toroker. Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence[J]. Advanced Functional Materials,2020,30(18).[25]Dinesh Visva Gunasekeran,Tien Yin Wong. Artificial Intelligence in Ophthalmology in 2020: A Technology on the Cusp for Translation and Implementation[J]. Asia-Pacific Journal of Ophthalmology,2020,9(2).[26]Fu-Neng Jiang,Li-Jun Dai,Yong-Ding Wu,Sheng-Bang Yang,Yu-Xiang Liang,Xin Zhang,Cui-Yun Zou,Ren-Qiang He,Xiao-Ming Xu,Wei-De Zhong. The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks[J]. Journal of the Chinese Medical Association,2020,83(5).[27]Matheus Calil Faleiros,Marcello Henrique Nogueira-Barbosa,Vitor Faeda Dalto,JoséRaniery Ferreira Júnior,Ariane Priscilla Magalh?es Tenório,Rodrigo Luppino-Assad,Paulo Louzada-Junior,Rangaraj Mandayam Rangayyan,Paulo Mazzoncini de Azevedo-Marques. Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging[J]. Advances in Rheumatology,2020,60(1078).[28]Balamurugan Balakreshnan,Grant Richards,Gaurav Nanda,Huachao Mao,Ragu Athinarayanan,Joseph Zaccaria. PPE Compliance Detection using Artificial Intelligence in Learning Factories[J]. Procedia Manufacturing,2020,45.[29]M. Stévenin,V. Avisse,N. Ducarme,A. de Broca. Qui est responsable si un robot autonome vient à entra?ner un dommage ?[J]. Ethique et Santé,2020.[30]Fatemeh Barzegari Banadkooki,Mohammad Ehteram,Fatemeh Panahi,Saad Sh. Sammen,Faridah Binti Othman,Ahmed EL-Shafie. Estimation of Total Dissolved Solids (TDS) using New Hybrid Machine Learning Models[J]. Journal of Hydrology,2020.[31]Adam J. Schwartz,Henry D. Clarke,Mark J. Spangehl,Joshua S. Bingham,DavidA. Etzioni,Matthew R. Neville. Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?[J]. The Journal of Arthroplasty,2020.[32]Ivana Nizetic Kosovic,Toni Mastelic,Damir Ivankovic. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis[J]. Journal of Cleaner Production,2020.[33]Lauren Fried,Andrea Tan,Shirin Bajaj,Tracey N. Liebman,David Polsky,Jennifer A. Stein. Technological advances for the detection of melanoma: Part I. Advances in diagnostic techniques[J]. Journal of the American Academy of Dermatology,2020.[34]Mohammed Amoon,Torki Altameem,Ayman Altameem. Internet of things Sensor Assisted Security and Quality Analysis for Health Care Data Sets Using Artificial Intelligent Based Heuristic Health Management System[J]. Measurement,2020.[35]E. Lotan,C. Tschider,D.K. Sodickson,A. Caplan,M. Bruno,B. Zhang,Yvonne W. Lui. Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future[J]. Journal of the American College of Radiology,2020.[36]Fabien Lareyre,Cédric Adam,Marion Carrier,Juliette Raffort. Artificial Intelligence in Vascular Surgery: moving from Big Data to Smart Data[J]. Annals of Vascular Surgery,2020.[37]Ilesanmi Daniyan,Khumbulani Mpofu,Moses Oyesola,Boitumelo Ramatsetse,Adefemi Adeodu. Artificial intelligence for predictive maintenance in the railcar learning factories[J]. Procedia Manufacturing,2020,45.[38]Janet L. McCauley,Anthony E. Swartz. Reframing Telehealth[J]. Obstetrics and Gynecology Clinics of North America,2020.[39]Jean-Emmanuel Bibault,Lei Xing. Screening for chronic obstructive pulmonary disease with artificial intelligence[J]. The Lancet Digital Health,2020,2(5).[40]Andrea Laghi. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence[J]. The Lancet Digital Health,2020,2(5).人工智能英文参考文献二：[41]K. Orhan,I. S. Bayrakdar,M. Ezhov,A. Kravtsov,T. ?zyürek. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans[J]. 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2014年1月第7卷第1期文化艺术研究Studies in Culture ＆Art Jan.，2014Vol.7No.1收稿日期：2013-12-12作者简介：张经武（1974—），男，湖北黄冈人，博士研究生，副教授，主要从事新媒体艺术产业和文艺传播研究。

*基金项目：本文系2013年度国家社科基金艺术学项目“数码艺术潜学科群研究”（批准号：13BA008）研究成果之一。

文章编号：1674-3180（2014）01-0111-08恐怖谷理论及其对于数码影像艺术角色设计的意义*张经武（1.厦门大学人文学院，厦门361005； 2.广西财经学院文化传播学院，南宁530003）摘要：1970年，日本机器人专家森政弘提出了“恐怖谷理论”，它推测，机器人及其他类人仿真物的外观和动作越逼真，人们对其亲和度也会越高，当逼真性到达一个较高的临界点，亲和度会陡然下降直至最低点。

近几年，恐怖谷理论成为西方新的研究热点，越来越多的研究成果支持这一理论的合理性。

恐怖谷理论对于数码影像艺术角色设计有显著的指导意义。

设计者应该注意规避和利用恐怖谷效应，既要防止跌入恐怖谷，又要利用恐怖谷曲线去追求特定的角色虚拟效果。

关键词：恐怖谷理论；数码影像艺术；角色设计；规避与利用中图分类号：TP242文献标识码：AThe Uncanny Valley Theory and Its Significance to theＲole Design in Digital Video ArtsZHANG Jing-wuAbstract ：In 1970，the uncanny valley theory was proposed by a Japanese robotic expert namedMasahiro Mori.It hypothesizes that the more human-like robots and other hominine simulation ob-jects become in appearance and motion ，the more positive the humans＇emotional reactions towardsthem become.This trend continues until a certain point is reached beyond which the emotional re-sponses quickly drop to the lowest.In recent years ，the uncanny valley theory has become a newhotspot in the Western academic circle ，and more and more researches support the rationality of thistheory.To the role design in the digital video arts ，the uncanny valley theory has an obvious guidingsignificance.Creators should pay more attentions ，avoiding falling into the uncanny valley on the one文化艺术研究第7卷hand while pursuing the specific effects of virtual roles according to the uncanny valley curve on theother.Key words ：uncanny valley theory ；digital video arts ；role design ；evasion and utilization1970年，日本东京工业大学著名机器人专家森政弘教授（Masahiro Mori ）在日本一家很不起眼的杂志《能源》（Energy ）上发表了一篇名为《恐怖谷》（The uncanny valley ）的文章［1］。