国外术语注册与术语服务综述

本文对国外上最具代表性的三个术语注册与术语服务系统(FAO注册系统与术语服务,OCLC术语服务,Open Metadata Registry)和四个相关研究项目(STAR,HILT,ADL地名表与叙词表协议,PoolParty)进行了详细的调研分析,在此基础上对我国术语注册与术语服务系统的开发与构建提出建议,主要包括:采用新兴的语义网和关联数据技术开发兼具术语注册与术语服务功能的完整系统,提供人与机器对注册词表元数据和词表内容的访问,采用RESTful Web服务构建术语服务,支持以关联数据方式发布词表内容等。图5。表4。参考文献30。     

Data fusion with factored quantization for stock trend prediction using neural networks

As compared to the continuous temporal distributions, discrete data representations may be desired for simplified and faster data analysis and forecasting. Data compression can introduce one of the efficient ways to reduce continuous historical stock market data and present them in discrete forms; while predicting stock trend, a primary concern is towards up and down directions of the price movement and thus, data discretization for a focused approach can be beneficial. In this article, we propose a quantization-based data fusion approach with a primary motivation to reduce data complexity and hence, enhance the prediction ability of a model. Here, the continuous time-series values are transformed into discrete quantum values prior to applying them to a prediction model. We extend the proposed approach and factorize quantization by integrating different quantization step sizes. Such fused data can reduce the data to mainly concentrate on the stock price movement direction. To empirically evaluate the proposed approach for stock trend prediction, we adopt long short-term memory, deep neural network, and backpropagation neural network models and compare our prediction results with five existing approaches on several datasets using ten performance metrics. We analyze the impact of specific quantization factors and determine the individual best as well as overall best factor sizes; the results indicate a consistent performance enhancement in stock trend prediction accuracy as compared to the considered baseline methods with an improvement up to 7%. To evaluate the impact of quantization-based data fusion, we analyze time required to execute the experiments along with percentage reduction in the number of unique numeric terms. Further, these results are statistically evaluated using Wilcoxon signed-rank test. We discuss the superiority and applicability of factored quantization-based data fusion approach and conclude our work with potential future research directions.     

基于图挖掘的文本主题识别方法研究综述

本文通过文献调研分析,将基于图挖掘的文本主题识别方法总结为中心度方法、紧密关联子图查找和图聚类三种,后两者又细分为基于clique子团或类clique子团、基于图拓扑结构或结点属性聚类的方法。中心度方法通过对比文本网络中术语结点的重要度来实现文本主题的识别,紧密关联子图查找和图聚类方法则是根据文本图中术语结点和边的属性相似度来识别文本核心主题。基于语言文本网络自身特性,如何构建复杂文本关系图来同时揭示术语间的句法、共现和语义关系,如何基于术语关联和图拓扑结构识别其中的紧密关联子团,基于何种标准将紧密关联子团聚类以揭示文本核心主题,都是未来需要进一步深入研究的问题。表1。     

面向图书馆关联数据的自动问答技术研究

早期针对语义网的自动问答主要是面向单一RDF数据集,随着网络上相互关联数据集的急速增加,迫切需要将自动问答扩展到多个RDF数据集,但同时在语义标注、答案整合方面也带来了更大的难度与挑战。本文提出了一种面向图书馆关联数据的自动问答新方法,通过将自然语言提问转换为结构化的SPARQL查询,从图书馆领域相互关联的五个RDF数据集中提取特定答案。该方法的创新点在于,将问句分为涉及一个数据集的简单句和涉及多个数据集的复杂句分别进行处理,又将简单句分为查询属性和查询实例两种类别分别制定SPARQL查询构建规则,将复杂句分解成若干个简单句进行处理,有利于SPARQL查询的构建和答案的整合。 通过实验测评,100个问句的回答精确率达到91%,表明这是一种行之有效的问答方法,对于促进关联数据在图书馆中的应用具有重要意义。图5。表5。     

我国城镇居民消费与收入关系分析——基于地理加权回归模型(GWR)的实证分析

空间数据一般具有空间非平稳性的特点,究其原因是地理空间上的区域缺乏均质性,存在高消费地区和低消费地区、中心(核心)和外围(边缘)等经济地理结构,从而导致我国城镇居民消费存在较大的空间差异性。对此,本文试图采用地理加权回归模型来分析我国城镇居民消费与收入的关系,从实证分析的结果提出几点建议和对策。     

Business analytics use in CRM: A nomological net from IT competence to CRM performance

Business analytics (BA) becomes increasingly important under rapidly changing business environment. A research challenge is that BA use is not fully understood. We tackle this challenge from the perspective of dynamic capability by using an empirical model with the emphasis on BA use in customer relationship management (CRM). Based on 170 samples from firm-level survey, we analyze the nomological linkage from IT competence to CRM performance. The results show data management capability fully mediates between IT competence and BA use, while customer response capability partially mediates between BA use and CRM performance.     

数字图书馆中的数据压缩及信息共享问题

数据压缩旨在利用图像的冗余度来减少表示图像所需的比特数,从而节省图像存贮容量,提高传输效率。文章归纳了目前一些主要的图像压缩技术并结合数字图书馆信息共享作初步探讨。     

采矿工程虚拟仿真实验教学体系建设与实践

采矿工程虚拟实验教学中心构建的"模块化、层次化、多元化"的虚拟仿真实验教学体系,在整合和完善虚拟仿真实验教学资源的同时,建成了一支教学理念先进、学术水平高、教学科研能力强的高素质实验教学队伍;建设了虚拟仿真实验网络信息平台,实现了虚拟仿真实验教学资源的智能化和网络化管理,使学生可自由选择实验时间和实验内容、自主配置虚拟实验仪器设备、自由搭建实验器材开展实验活动;虚拟仿真实验教学平台及实验项目基本实现了行业高校共享。采矿工程虚拟实验教学中心的建设为矿业类大学生实践和创新能力的培养创出了一条新路。     

Team and seasonal performance indicator evolution in the ANZ Championship netball league

ABSTRACT

Analysis of netball has received scant attention in the literature and there is little understanding of the dynamics of netball game-play. This study aimed to analyse team and seasonal performance indicator (dis)similarity in the ANZ Championship (netball). Total season values for nine performance indicators were analysed for the ten ANZ Championship teams from 2009 to 2016. The data were analysed using a multivariate, distance-based, approach. Specifically, non-metric multidimensional scaling was used to examine seasonal and team (dis)similarity. After declining from 2009, shooting percentage, goal assists, centre pass receives, penalties and turnovers began to rise from 2011. Both penalties and turnovers declined from 2015, in addition to attempts at goal. The two-dimensional multivariate ordination plot showed relative similarity between each team and season over the observational period, suggesting stagnant game-play dynamics. Further, despite idiosyncratic seasonal profiles, teams generally followed similar directional progression across the ordination surface. Despite being observed in other team invasion sports, league-wide synchronous evolutionary tendencies were not observed within the ANZ Championships between the 2006 to 2016 seasons. However, certain teams did display seasonal fluctuation in their observed multivariate profile, with these seasonal idiosyncrasies being discussed relative to coaching and playing roster changes specific to the analysed team.     

基于SOM聚类的电网可视化数据挖掘模型

面对电力系统中海量的多维数据,传统的可视化数据挖掘无法满足空间数据处理的需要,多维数据可视化也不利于用户获取知识。因此提出了基于SOM(自组织特征映射网络)聚类的电网可视化数据挖掘新模型VSDMmodel,模型利用改进的SOM聚类算法对高维电网数据进行降维,提出一种基于颜色映射的可视化方法,对聚类结果进行低维展现,加快了用户对挖掘结果的理解,并且允许用户对结果中感兴趣的区域加以深入分析,实现对电力系统海量数据的可视化挖掘。