A Multi-match Approach to the Author Uncertainty Problem

Purpose: The ability to identify the scholarship of individual authors is essential for performance evaluation. A number of factors hinder this endeavor. Common and similarly spelled surnames make it difficult to isolate the scholarship of individual authors indexed on large databases. Variations in name spelling of individual scholars further complicates matters. Common family names in scientific powerhouses like China make it problematic to distinguish between authors possessing ubiquitous and/or anglicized surnames(as well as the same or similar first names). The assignment of unique author identifiers provides a major step toward resolving these difficulties. We maintain, however, that in and of themselves, author identifiers are not sufficient to fully address the author uncertainty problem. In this study we build on the author identifier approach by considering commonalities in fielded data between authors containing the same surname and first initial of their first name. We illustrate our approach using three case studies.Design/methodology/approach: The approach we advance in this study is based on commonalities among fielded data in search results. We cast a broad initial net—i.e., a Web of Science(WOS) search for a given author's last name, followed by a comma, followed by the first initial of his or her first name(e.g., a search for ‘John Doe' would assume the form: ‘Doe, J'). Results for this search typically contain all of the scholarship legitimately belonging to this author in the given database(i.e., all of his or her true positives), along with a large amount of noise, or scholarship not belonging to this author(i.e., a large number of false positives). From this corpus we proceed to iteratively weed out false positives and retain true positives. Author identifiers provide a good starting point—e.g., if ‘Doe, J' and ‘Doe, John' share the same author identifier, this would be sufficient for us to conclude these are one and the same individual. We find email addresses similarly adequate—e.g., if two author names which share the same surname and same first initial have an email address in common, we conclude these authors are the same person. Author identifier and email address data is not always available, however. When this occurs, other fields are used to address the author uncertainty problem.Commonalities among author data other than unique identifiers and email addresses is less conclusive for name consolidation purposes. For example, if ‘Doe, John' and ‘Doe, J' have an affiliation in common, do we conclude that these names belong the same person? They may or may not; affiliations have employed two or more faculty members sharing the same last and first initial. Similarly, it's conceivable that two individuals with the same last name and first initial publish in the same journal, publish with the same co-authors, and/or cite the same references. Should we then ignore commonalities among these fields and conclude they're too imprecise for name consolidation purposes? It is our position that such commonalities are indeed valuable for addressing the author uncertainty problem, but more so when used in combination.Our approach makes use of automation as well as manual inspection, relying initially on author identifiers, then commonalities among fielded data other than author identifiers, and finally manual verification. To achieve name consolidation independent of author identifier matches, we have developed a procedure that is used with bibliometric software called VantagePoint(see www.thevantagepoint.com). While the application of our technique does not exclusively depend on VantagePoint, it is the software we find most efficient in this study. The script we developed to implement this procedure is designed to implement our name disambiguation procedure in a way that significantly reduces manual effort on the user's part. Those who seek to replicate our procedure independent of VantagePoint can do so by manually following the method we outline, but we note that the manual application of our procedure takes a significant amount of time and effort, especially when working with larger datasets.Our script begins by prompting the user for a surname and a first initial(for any author of interest). It then prompts the user to select a WOS field on which to consolidate author names. After this the user is prompted to point to the name of the authors field, and finally asked to identify a specific author name(referred to by the script as the primary author) within this field whom the user knows to be a true positive(a suggested approach is to point to an author name associated with one of the records that has the author's ORCID iD or email address attached to it).The script proceeds to identify and combine all author names sharing the primary author's surname and first initial of his or her first name who share commonalities in the WOS field on which the user was prompted to consolidate author names. This typically results in significant reduction in the initial dataset size. After the procedure completes the user is usually left with a much smaller(and more manageable) dataset to manually inspect(and/or apply additional name disambiguation techniques to).Research limitations: Match field coverage can be an issue. When field coverage is paltry dataset reduction is not as significant, which results in more manual inspection on the user's part. Our procedure doesn't lend itself to scholars who have had a legal family name change(after marriage, for example). Moreover, the technique we advance is(sometimes, but not always) likely to have a difficult time dealing with scholars who have changed careers or fields dramatically, as well as scholars whose work is highly interdisciplinary.Practical implications: The procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research, especially when the name under consideration is a more common family name. It is more effective when match field coverage is high and a number of match fields exist.Originality/value: Once again, the procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research. It combines preexisting with more recent approaches, harnessing the benefits of both.Findings: Our study applies the name disambiguation procedure we advance to three case studies. Ideal match fields are not the same for each of our case studies. We find that match field effectiveness is in large part a function of field coverage. Comparing original dataset size, the timeframe analyzed for each case study is not the same, nor are the subject areas in which they publish. Our procedure is more effective when applied to our third case study, both in terms of list reduction and 100% retention of true positives. We attribute this to excellent match field coverage, and especially in more specific match fields, as well as having a more modest/manageable number of publications.While machine learning is considered authoritative by many, we do not see it as practical or replicable. The procedure advanced herein is both practical, replicable and relatively user friendly. It might be categorized into a space between ORCID and machine learning. Machine learning approaches typically look for commonalities among citation data, which is not always available, structured or easy to work with. The procedure we advance is intended to be applied across numerous fields in a dataset of interest(e.g. emails, coauthors, affiliations, etc.), resulting in multiple rounds of reduction. Results indicate that effective match fields include author identifiers, emails, source titles, co-authors and ISSNs. While the script we present is not likely to result in a dataset consisting solely of true positives(at least for more common surnames), it does significantly reduce manual effort

基于多链存储优化的水产品交易匹配模型研究

区块链技术应用到水产品线上交易架构中可以使交易双方隐私信息得到基本保障,然而,目前区块链水产品线上交易模型和系统存在海量数据存储负载大、维护成本高、数据查询效率低等问题。为进一步缓解以上问题,在梳理和分析水产品交易流程基础上,根据水产品交易业务技术需求,提出了基于多链存储优化的水产品交易匹配模型。该模型在智能合约中通过贪心算法实现了效率较高的多属性水产品线上交易匹配过程,通过区块链多通道技术构建了水产品交易多链架构,实现了用户交易信息分布式存储,提高了交易信息查询效率,同时,采用区块链与本地数据库双模式存储技术,缓解了区块链网络中各个节点海量数据存储的负载。基于Hyperledger Fabric平台实现了基于多链存储优化的水产品交易原型系统。该原型系统测试结果表明,临界值900s平均最多可以完成1296笔交易,说明系统在处理千条交易数据量时可以正常运行,满足水产品线上交易平台日常实际交易业务需求,同时在链上存储1600条合同信息时查询1条用户合同信息平均时间为4.018s,多链存储结构提高了链上数据查询速度。

融合领域要素知识的多粒度法律文本匹配方法

法律文本匹配的目标是快速提炼对比要素信息并发现关联案件,保障法律适用的统一性同案同判.现有方法未能充分利用特定类型案件的先验知识,其核心要素提取准确率低,仅进行词向量的权重计算,忽略字义、句义、句法的向量信息,影响匹配效果.提出一种融合领域要素知识的多粒度法律文本匹配方法,通过建立特定案件类型领域知识库准确提取法律要素,引入字、词、句3个粒度的注意力机制计算不同文本向量的权重提升匹配模型效果.实验结果表明,该方法在公开数据集上可达到最好效果.

一种密集多尺度特征引导代价聚合的改进立体匹配网络

针对目前立体匹配算法在重复纹理、无纹理、边缘等不适定性区域仍存在匹配不准确的问题,提出了一种基于PSMNet的密集多尺度特征引导代价聚合的立体匹配算法—DGNet(Dense multi-scale features Guided aggregation Network)。首先,基于密集连接空洞空间金字塔池化结构设计了密集多尺度特征提取模块,该模块利用不同膨胀率的空洞卷积提取不同尺度的区域级特征,并通过密集连接方式有效整合不同尺度的图像特征,使网络捕获丰富的上下文关系;其次,在每个视差等级下将左右特征图串联形成初始代价体,再提出密集多尺度特征引导代价聚合结构,在聚合代价体的同时自适应融合代价体和密集多尺度特征,从而使后续的解码层在多尺度上下文信息的引导下解码出更加精确和高分辨率的几何信息;最后,将全局优化后的高分辨率代价体送入视差回归模块以获得视差图。实验结果表明:所提算法在KITTI 2015和KITTI 2012数据集上的误匹配率分别降至1.76%和1.24%,SceneFlow数据集上的端点误差降至0.56 px,与GWCNet、CPOP-Net等先进算法相比,所提算法在不适定区域有明显改善。

制造业众包平台服务资源的特征构建及聚合匹配

针对制造业众包平台中服务资源的特征构建缺乏规范、聚合匹配不准确的问题,提出了基于双向编码表示(BERT)模型的特征构建方法和基于多粒度特征交互的聚合匹配模型(MFIM)。前者改进了经典的BERT模型,采用停止符掩码操作和基于分类任务的学习策略,实现了服务资源所具有的语义特征建模,精准衡量了平台商所具有的服务资源能力水平,为大规模的语义特征体系的构建提供了有力支撑。后者针对服务资源的语义粒度差异较大的问题,采用了多层次的扩张卷积结构,并构建了跨粒度信息交互的关联矩阵。这有效解决了服务资源的聚合匹配及语义辨识问题。实验表明,所提出的一系列研究方法能够有效地建模服务资源文本,为服务资源的深入挖掘和精准的匹配检索提供了一种新的思路和手段。

基于注意力机制和软匹配的多标签遥感图像检索方法

针对卷积神经网络对于多标签遥感图像特征提取能力弱、不能准确反映遥感图像多标签复杂性的问题,提出基于注意力机制和软匹配的多标签遥感图像检索方法。在特征提取阶段,以密集卷积神经网络模型为基础,在每个密集块(Dense Block)后添加CBAM(Convolutional Block Attention Module)层,实现对多标签图像区域特征提取。在模型训练时,利用区分硬匹配与软匹配的联合损失函数,学习图像的哈希编码表示。通过评估遥感图像哈希编码间的汉明距离,实现相似图像的检索。实验结果表明,所提方法在数据集NUS-WIDE和多标签遥感图像数据集DLRSD上与其他基于全局特征的深度哈希方法相比,明显提升了检索准确率。

复杂场景下多模态点云数据配准技术

针对复杂环境下多模态点云数据获取难,以及对点云数据配准、三维模型构建精度的要求越来越高的情况。本文以南通大剧院实景三维建模为例,当初始点云和校准点云两组多模态融合点云位置差较大时,采用ICP算法进行点云配准易导致局部最优问题,利用所提出的基于控制点辅助约束的最近点迭代(CPA-ICP)算法通过对点云数据进行配准,并与其他3种点云配准算法的试验进行对比,可知该方法的配准精度和配准效率较高,对复杂场景下的多模态点云数据融合有较好的参考意义。

大规模协同虚拟环境下并行层次兴趣匹配算法

大规模协同虚拟环境运行过程中易产生大量冗余通信数据,影响虚拟现实技术性能的发挥。为了提升虚拟环境运行效率,提出并行层次兴趣匹配算法。创建协同虚拟环境概念模型,分解处理订阅区域信息,引入树结构理论搭建兴趣管理树,将订阅区域映射到兴趣管理树节点上,形成层次化结构,结合LSQR并行算法,实现更新区域与订阅区域的快速匹配。实验结果表明,提出算法获得的兴趣匹配消耗时间最小值为0.36 s,兴趣匹配成功指数最大值为0.94,充分证实了提出算法兴趣匹配性能较好。

外卖智能派单的订单-骑手多目标匹配模型及其适应性算法研究

随着外卖业的快速发展,外卖智能派单成为外卖服务的重要形式。然而,目前相关研究侧重于研究外卖配送路径问题,不利于解决外卖智能派单的多目标动态决策中存在的问题。本文从外卖订单-骑手匹配的角度,分析了外卖平台智能派单的五个基本目标和四个基本约束,构建了多目标的订单-骑手匹配优化模型,进而给出了基于适应性策略的实时优化算法。最后,本文设计了外卖智能派单的仿真实验,对比了骑手-订单数量均衡、骑手供过于求、骑手供不应求等三种情况下的优化情况,验证了基于适应性策略的实时优化算法的优化效果。本研究对外卖平台发展智能派单模式具有较好的理论与实践价值。

高频宽带换能器多匹配层研究

在水声应用中,高频换能器往往需要较宽的工作带宽,以获得更多的目标信息。文章首先建立了等效电路模型,利用粒子群算法对匹配层材料和厚度进行初步选定,使得换能器具有最宽的工作频带;其次,通过有限元方法对匹配层换能器的导纳和发射电压响应进行分析计算;最后,在理论分析的基础上成功制得三匹配层高频宽带换能器,其工作频段约为150~430 kHz,相对带宽为93%,带内发送电压响应起伏为-6 dB。实验结果表明,三匹配层设计方案可以有效拓宽高频换能器的工作频段。

基于倾斜摄影的多回输电线路实景三维模型构建系统设计

为了使改进多回输电线路三维模型更加符合实际情况,该文将倾斜摄影技术引入多回输电线路实景三维模型构建系统中,提升数据获取的精准性。硬件方面,微处理器作为指令核心处理结构,负责读取并解析指令;设计多回输电线路数据采集模块和EP3SE110F1152C4数据存储器,并利用数据存储器对系统运行过程中的多项数据进行存储;软件方面,借助倾斜摄影技术,获取多回输电线路数据,并计算其对应的比例尺和航高,再对获取的数据进行优化,消除数据中的误差,并对其进行配准处理和特征匹配以及纹理映射和归一化处理,由此完成对多回输电线路实景三维模型的构建。经过实验验证,和以往的多回输电线路实景三维模型构建系统相比,设计的基于倾斜摄影的多回输电线路实景三维模型构建系统在实际应用中模型构建完整度为95.6%,构建效果更好。

基于多粒度匹配的文本引导服装图像检索

文本引导的图像检索是将查询图像与文本条件集成为多模态查询。现有的方法通过构建更先进的细粒度度量学习来提升性能,但这可能会使模型在文本条件不够精确的情况下对目标图像过拟合,并使得检索结果特征单调。针对该问题,提出了基于特征增强和多粒度匹配的文本引导的服装图像检索方法。首先,根据目标特征的分布,产生服从正态分布的噪声,使其产生小幅度的类内抖动;然后,根据目标特征的波动对增强特征施加约束,波动越大,则对增强特征的惩罚越大,由此得到粗粒度匹配损失;最后,优化学习策略,使用随着训练迭代不断衰减的动态权重将粗粒度与细粒度损失进行统一。通过该方法降低模型对潜在目标图像的排斥,提高特征识别的多样化。在两个公开服装数据集FashionIQ和Shoes上的大量实验表明,该方法能够提高召回率,并且检索结果更丰富。

基于卷积神经网络和NCC的两阶段的多尺度高精度定位的模板匹配算法

当前模板匹配算法中,基于灰度的模板匹配算法具有较好的稳定性和鲁棒性.但是对于大型图像和复杂模板,它可能需要大量的计算资源和时间.此外,在应对目标尺度变化较大时,基于灰度的模板匹配算法匹配效果较差.对于NCC算法自身速度较慢的问题,本文对NCC算法进行了改进,减少了平均36%的匹配时间.为了应对多尺度的问题,本文结合卷积神经网络,提出了基于卷积神经网络和NCC的两阶段的多尺度高精度定位的模板匹配算法.其中,在一阶段目标检测阶段,本文在YOLOX算法的基础上改进了主干网络和损失函数,改善了算法的计算速度以及匹配成功率,并利用一阶段目标检测的结果使二阶段NCC算法动态调整模板大小,极大地减少了NCC算法大规模制作模板时间,最终使得整体匹配精度远远高于传统基于灰度的模板匹配算法.

基于混合结构的多视图三维场景重建

基于PatchMatch的多视图高效和高精度三维重建一直是一个挑战性问题。红黑棋盘格模式的传播方式并行计算效率高,但对应的视图选择策略精度较差;基于马尔可夫链的视图选择策略能获取更为准确的匹配结果,但算法并行度较低。为了达成场景重建质量与重建时间的平衡,本文提出了一种基于混合结构的多视图三维重建算法,在第一阶段采用沿行/列并行的传播策略和马尔可夫链式的视图选择策略,得到质量较高的初始深度图,并通过引入多层次处理提升弱纹理区域的重建质量;在第二阶段采用棋盘格式传播方式和基于投票的视图选择策略提高计算效率,缩短重建所需时间。通过在Strecha和ETH3D数据集上进行了大量实验和对比表明,本文算法在不降低计算精度的前提下,计算效率提高2.5倍以上。

基于视觉和惯性传感器的大型邮轮室内旅客身份识别方法

邮轮内部结构及场景复杂,基于监控用单目摄像头的旅客身份识别方法缺乏深度信息,无法准确识别旅客位置、航向及航向变化信息,难以在复杂场景下准确识别旅客身份。针对上述问题,提出了基于监控用单目摄像头与手持惯性传感器的大型邮轮室内旅客身份识别方法。根据YOLOv5视觉目标检测算法,提取监控视频帧中旅客的像素坐标与边界框;利用2D-3D坐标转换公式,将相机图像中旅客的像素坐标转换为物理世界中旅客与相机的相对坐标;再基于改进神经网络模型,估计旅客在相机相对坐标系下的航向角。利用旅客智能手机中惯性传感器,采集旅客运动数据,检测旅客加速度的变化,判别旅客行走状态;融合磁场强度,推算旅客在大地坐标系下的真实航向角;融合提取的视觉和惯性传感器数据,对旅客的有限特征及其关系进行编码,包括瞬时时刻行走状态、步长、相对航向角和相对距离,解决传感器信号的误差累积问题;基于构建的2幅多关联图,提出特征之间的相似度计算公式,再利用视觉与惯性传感器特征图匹配(vision and inertial sensors graph matching,VIGM)算法求解最大相似度矩阵,实现对2幅图中的同一旅客的识别。经长江“黄金3号”邮轮大厅、棋牌室、多功能厅和走廊4个场景实验,可以发现:VIGM算法在1~3 s窗口内平均匹配准确率达83.9%,与使用高成本深度相机的ViTag身份匹配算法相比,平均匹配准确率仅相差4.5%。实验结果表明:所提基于摄像头与惯性传感器的旅客身份识别方法及算法实现成本低,但识别效果与使用高成本深度相机的方法相当。

基于联合GLMB滤波器的可分辨群目标跟踪

针对联合广义标签多伯努利(joint generalized labeled multi-Bernoulli, J-GLMB)滤波算法中群目标之间距离较近、容易关联错误的问题,结合超图匹配(hypergraph matching, HGM)提出一种基于HGM-J-GLMB滤波器的可分辨群目标跟踪算法。首先,采用J-GLMB滤波器估计群内各目标的状态、数目及轨迹信息,并利用HGM结果提升量测与预测状态之间的关联性能。其次,通过图理论计算邻接矩阵,获取群结构信息和子群数目。随后,利用群结构信息估计协作噪声,进而校正目标的预测状态。最后,通过平滑算法改善滤波效果,并设置轨迹长度阈值,使其在平滑状态达到消除短轨迹的目的。仿真实验表明,所提算法在线性系统下能有效提升群目标跟踪性能。

一种多方法融合的藏语情感词典构建方法

深度学习在藏语情感分析领域备受关注,相较于传统机器学习方法其表现更出色。然而,构建藏语情感词典仍面临着挑战,如词汇量不足、过度依赖机器翻译系统、词典匹配源单一、缺少口语情感词典等。为解决上述问题,文章提出了一种多方法融合的藏语情感词典构建方法。首先,统计并分析已有情感词标注规则后提出了一种藏语情感词的标注规则作为情感词分类的主要依据;其次,提出了一种多词典匹配的藏语情感词典构建方法构建了藏语基准情感词典,为了扩大基准情感词典的规模,利用SO-PMI和基于word2vec词向量相似度扩充方法对基准词典进行词汇扩充,并且利用藏语3大方言的口语词典经人工筛选后构建了藏语口语情感词典;然后,将基准词典和扩充词典合并去重后得到了《藏语书面语与口语情感词典》;最后,为了证明本文方法的可行性和所构建词典的可用性而进行了藏语情感词典性能评估实验;实验中准确率、召回率、F值分别为60.80%、90.31%、72.67%,达到了较好的应用水平,验证了多方法融合的藏语情感词典构建方法的可行性。

M对N型贸易关系的国际集装箱海运服务多目标多边匹配决策研究

国际集装箱海运因其运价低、运距远的特征,对于促进国际贸易、实现全球化生产具有重大意义。针对M对N型贸易关系下的国际集装箱海运服务匹配问题,将匹配指标划分为效益型指标和成本型指标;同时,创新性地采用三方主体在四个匹配环节中匹配满意度的算数平均值来表达整体匹配满意度。在构建最大化匹配主体满意度为目标函数的基础上,还增加对匹配中介收益的考虑,最终将模型转化为类似经典的运输平衡问题。发货人运输给收货人的集装箱数量是在匹配任务开始前根据他们先前订立的贸易合同就已经确定的,是无法改变的,且三方主体之间的匹配满意度不会因为箱量的增减发生线性的变化。因此,在采用最大元素法后获得的初始解即为最优解,无需做调整,也进一步说明本模型的实用性和操作性。

基于特征增强和语义相关性匹配的图像文本检索方法

为实现图像文本检索中图像与文本的精确语义连接,提出一种基于特征增强和语义相关性匹配(FESCM)的图像文本检索方法。首先,通过特征增强表示模块,引入多头自注意力机制增强图像区域特征和文本单词特征,以减少冗余信息对图像区域和文本单词对齐的干扰;其次,通过语义相关性匹配模块,不仅利用局部匹配捕获局部显著对象之间的对应相关性,还把图像背景信息融入图像全局特征,利用全局匹配实现精确的全局语义相关性;最后,通过局部匹配分数和全局匹配分数获取图像和文本的最终匹配分数。实验结果表明,基于FESCM的图像文本检索方法在Flickr8k和Flickr30k基准数据集上的召回率总值比扩展的视觉语义嵌入方法分别提升了5.7和7.5个百分点,在MS-COCO数据集比双流层次相似度推理方法提升了3.7个百分点。因此该方法可以有效提高图像文本检索的准确度,实现图像与文本的语义连接。

联合多尺度块匹配的非局部均值去噪算法

针对非局部均值(Non-Local Means,NLM)图像去噪算法易产生伪影与平滑细节的问题,提出一种联合多尺度图像块匹配的像素相似性测度,提高NLM算法去噪性能。首先,研究与分析了加权欧氏距离与欧氏距离两种相似性度量以及图像块尺寸设置对NLM算法的影响。其次,通过引入图像特征信息并利用K-means聚类方法将图像划分为平坦区域和包含边缘与纹理的结构区域,对每个类别中的像素点,联合两种尺度图像块匹配计算像素的平滑权重。最后,优化了算法的滤波参数。实验结果表明,提出的算法在噪声去除与细节保持方面明显优于经典的NLM算法,相比其他改进的NLM算法也有优势。