Discriminative feature mining with relation regularization for person re-identification     

《Information processing & management》2023,60(3):103295

The appearance attribute and pose are two important and complementary features, so integrating them can effectively alleviate the impact of misalignment and occlusion on re-identification. In this paper, we deeply investigate the inner relation between attribute features and the spatial semantic relation between key-point region features of the pose in a person image and propose a person re-identification method based on discriminative feature mining with relation regularization. Firstly, an attribute relation detector based on nonlinear graph convolution is built on mining the inner correlation between attribute features of a person, providing relational attribute features for more effectively distinguishing persons with a similar appearance. Then, we construct a hierarchical pose pyramid to model the multi-grained semantic features of key-point regions of the pose and propose intra-graph and cross-graph node relation information propagation structures to infer the spatial semantic relation between node features within-graph and between-graph. This module is robust to complex pose changes and can suppress noise background redundancy caused by inaccurate key point detection and occlusion. Finally, a refined feature model is proposed to effectively fuse the global appearance feature with the relational attribute and multi-grained pose features, thus providing a more discriminative fusion feature for person re-identification. Many experiments on three large-scale datasets verify the effectiveness and state-of-the-art performance of the proposed method.  相似文献   

Structural Analysis for Replicating Japanese Cultural Property Braids     

Masumi Tada  Kazuaki Yamashiro  Iwataro Oka  Akihiko Goto  Noriaki Kuwahara 《文物保护研究》2020,65(7):399-410

ABSTRACT

Historic Japanese textiles from over 1000 years ago generally show marked deterioration and only very rare examples show their original forms and much information about textile reproduction has been lost. The replication of textile braids lacks systematic methodology and is still being practiced by only few individual braiding experts. The recreation of original braids as close as possible to original braids is a part of Japan’s intangible cultural heritage. The aim of this study is to clarify the decision-making procedure through which the braiding experts can decipher the original braiding structures. As a preliminary study of this project, interviews of a braid researcher and a replicating expert, Makiko Tada were performed regarding her working practices. It is important to clarify the braiding parameters for structural analysis such as the number of transits and the balance of ridges, and it became clear that the orientation of multiple colored threads plays an important role. The expert’s replicate works were also analyzed using a text-mining statistical technique to clarify the relationship of braiding parameters. The relationship between each braiding parameter and production method such as loop manipulation and Taka-dai became clear. As a result, the process of deciphering the original braid structure has been compiled in simplified workflows, which could contribute to the standardization and improvement in efficiency of replication of cultural property braids.  相似文献   

基于深度学习的中文专利自动分类方法研究   总被引：2，自引：0，他引：2  

吕璐成  韩涛  周健  赵亚娟 《图书情报工作》2020,64(10):75-85

[目的/意义] 面向当前国内专利审查和专利情报分析工作中对于海量专利分类的客观需求，设计了7种基于深度学习的专利自动分类方法，对比各种方法的分类效果，从而助力专利分类效率和效果的提升。[方法/过程] 针对传统机器学习方法存在的缺陷，基于Word2Vec、CNN、RNN、Attention机制等深度学习技术，考虑专利文本语序特征、上下文特征以及分类关键特征，设计Word2Vec+TextCNN、Word2Vec+GRU、Word2Vec+BiGRU、Word2Vec+BiGRU+TextCNN等7种深度学习模型，以中国专利为例，选取IPC主分类号的"部"作为分类依据，对比这7种模型与3种传统分类模型在中文专利分类任务中的效果。[结果/结论] 实证研究效果显示，采用考虑语序特征、上下文特征及强化关键特征的深度学习方法进行中文专利分类具有更优的分类效果。  相似文献   

Coupled matrix factorization and topic modeling for aspect mining     

Ding Xiao  Yugang Ji  Yitong Li  Fuzhen Zhuang  Chuan Shi 《Information processing & management》2018,54(6):861-873

Aspect mining, which aims to extract ad hoc aspects from online reviews and predict rating or opinion on each aspect, can satisfy the personalized needs for evaluation of specific aspect on product quality. Recently, with the increase of related research, how to effectively integrate rating and review information has become the key issue for addressing this problem. Considering that matrix factorization is an effective tool for rating prediction and topic modeling is widely used for review processing, it is a natural idea to combine matrix factorization and topic modeling for aspect mining (or called aspect rating prediction). However, this idea faces several challenges on how to address suitable sharing factors, scale mismatch, and dependency relation of rating and review information. In this paper, we propose a novel model to effectively integrate Matrix factorization and Topic modeling for Aspect rating prediction (MaToAsp). To overcome the above challenges and ensure the performance, MaToAsp employs items as the sharing factors to combine matrix factorization and topic modeling, and introduces an interpretive preference probability to eliminate scale mismatch. In the hybrid model, we establish a dependency relation from ratings to sentiment terms in phrases. The experiments on two real datasets including Chinese Dianping and English Tripadvisor prove that MaToAsp not only obtains reasonable aspect identification but also achieves the best aspect rating prediction performance, compared to recent representative baselines.  相似文献   

Complementing subjective with objective data in analysing expertise: A machine-learning approach applied to badminton     

Olivier Dieu  Christophe Schnitzler  Clément Llena  François Potdevin 《Journal of sports sciences》2020,38(17):1943-1952

ABSTRACT

This study aimed to assess which combination of subjective and empirical data might help to identify the expertise level. A group of 10 expert coaches classified 40 participants in 5 different expertise groups based on the video footage of the rallies. The expertise levels were determined using a typology based on a continuum of 5 conative stages: (1) structural, (2) functional, (3) technical, (4) contextual, and (5) expertise. The video allowed empirical measurement of the duration of the rallies, and tri-axial accelerometers measured the intensity of the player’s involvement. A principal component analysis showed that two dimensions explained 54.9% of the total variance in the data and that conative stage and empirical parameters during rallies (duration, intensity of the game) were correlated with axis 1, whereas duration and acceleration data between rallies were correlated with axis 2. A random forest algorithm showed that among the parameters considered, acceleration, duration of the rallies, and time between rallies could predict conative stages with a prediction accuracy above possibility.

This study suggests that performance analysis benefits from the confrontation of subjective and objective data in order to design training plans according to the expertise level of the participants.  相似文献   

Score Reporting for Examinees with Incomplete Data on Large‐Scale Educational Assessments     

Sandip Sinharay 《Educational Measurement》2021,40(1):79-91

Technical difficulties occasionally lead to missing item scores and hence to incomplete data on computerized tests. It is not straightforward to report scores to the examinees whose data are incomplete due to technical difficulties. Such reporting essentially involves imputation of missing scores. In this paper, a simulation study based on data from three educational tests is used to compare the performances of six approaches for imputation of missing scores. One of the approaches, based on data mining, is the first application of its kind to the problem of imputation of missing data. The approach based on data mining and a multiple imputation approach based on chained equations led to the most accurate imputation of missing scores, and hence to most accurate score reporting. A simple approach based on linear regression performed the next best overall. Several recommendations are made regarding the reporting of scores to examinees with incomplete data.  相似文献   

高校矿业类专业人才培养模式探讨   总被引：6，自引：0，他引：6  

何廷峻  汪佑武 《淮南职业技术学院学报》2005,5(1):103-105

分析了采矿工程专业教学的现状和存在的问题，通过调查研究，吸收国内外成功的教学改革经验，对采矿工程专业人才培养模式、教学内容、教学计划、教学体系等问题进行了探讨。  相似文献   

基于关联分析的计算机软件数据挖掘技术     

陈翠娟 《安阳师范学院学报》2021,(2):28-31

计算机软件蕴含大量工作信息,有效挖掘软件数据信息之间的内在关联是信息时代对软件应用的潜在要求。针对经典Apriori算法挖掘数据效率低、复杂度高的问题,提出一种改进Apriori算法用于挖掘计算机软件数据的关联规则。为计算机软件算法设置双重支持度阈值,即频繁项集与非频繁项集支持度阈值,快速获得强关联的频繁项集;在此基础上基于映射规则重构事务数据库,压缩数据库规模,减少算法的剪枝操作,降低计算机软件数据关联规则挖掘复杂度。以人力资源类计算机软件数据为例展开关联分析测试,结果显示,该算法挖掘的关联信息与人力资源实际管理情况一致,相比经典Apriori算法其效率有所提升。  相似文献   

计算机文化基础MOOC平台互动教学实践探索     

任会峰  阚希  陆松  董庆超 《教育技术导刊》2021,19(12):43-47

针对大学各学科培养目标存在差异,以及学生入学时计算机基础不平衡等教育教学现状,依托大规模开放在线课程（MOOC）平台,以计算机文化基础课程为例,从开课前、授课中、结课后3个阶段分别论述开设课程、组织授课和追踪反馈等多个教学环节的实践过程,并形成闭合环路,探索面向应用能力培养的互动教学模式,同时探讨教学中存在的问题及解决办法。抽组测试发现,混合教学的及格率达到98%,相比传统教学提高了18%,约33%的学生自主选择混合式学习,表明MOOC平台的混合教学有利于培养学生的计算思维,提高其计算机应用能力,最后分析MOOC平台采用大数据挖掘技术的重要性和可行性。  相似文献   

运用数据挖掘技术预测客户购买倾向--方法与实证研究     

王萍 《情报科学》2005,23(5):738-741

商家大部促销行为，其实都是针对客户的促销，分类模型的主要功能是根据客户数据的属性将他们分派到不同的组中。本文提出利用数据挖掘技术建立客户购买意向分类模型，利用这种分类模型可以预测客户的购买倾向。  相似文献