灰色预测法在情报研究中的应用研究综述

[目的/意义] 灰色预测法可有效处理情报研究中广泛存在的小样本数据，通过对灰色预测法在情报研究中的应用情况进行梳理，总结其在应用过程中存在的不足，为灰色预测法在情报研究中的进一步应用提供参考。[方法/过程] 通过综述情报研究中涉及灰色预测法的相关文献，从数据选取、模型构建和解决的问题等方面对情报研究中灰色预测法的应用进行概述，总结当前情报研究中灰色预测法的应用所存在的问题，并提出改进建议。[结果/结论] 在方法应用上，已有研究主要采用数列灰预测，且模型集中在单变量灰色预测模型，根据预测对象不同，灰色预测法已经在包括期刊分析、图书馆运行管理、热点主题分析及科研机构评价方面得到了很好的应用，未来可根据预测对象特点及研究目标尝试不同的灰色预测方法，扩宽灰色预测法在其他方面的情报研究问题中的应用。     

Location extraction from tweets

Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. These events are represented by three main dimensions: time, location and entity-related information. The focus of this paper is location, which is an essential dimension for geo-spatial applications, either when helping rescue operations during a disaster or when used for contextual recommendations. While the first type of application needs high recall, the second is more precision-oriented. This paper studies the recall/precision trade-off, combining different methods to extract locations. In the context of short posts, applying tools that have been developed for natural language is not sufficient given the nature of tweets which are generally too short to be linguistically correct. Also bearing in mind the high number of posts that need to be handled, we hypothesize that predicting whether a post contains a location or not could make the location extractors more focused and thus more effective. We introduce a model to predict whether a tweet contains a location or not and show that location prediction is a useful pre-processing step for location extraction. We define a number of new tweet features and we conduct an intensive evaluation. Our findings are that (1) combining existing location extraction tools is effective for precision-oriented or recall-oriented results, (2) enriching tweet representation is effective for predicting whether a tweet contains a location or not, (3) words appearing in a geography gazetteer and the occurrence of a preposition just before a proper noun are the two most important features for predicting the occurrence of a location in tweets, and (4) the accuracy of location extraction improves when it is possible to predict that there is a location in a tweet.     

基于短时交通流量预测对路网微观交通仿真的研究

针对早高峰短时交通流量预测数据少、波动大的特点,提出用灰色模型进行预测。将灰色GM(2,1)改进为灰色GM(2,1,λ,ρ)预测模型,以提高预测精度。针对粒子群算法(PSO)的早熟现象,将Logistic混沌搜索嵌入到PSO算法,应用混沌粒子群算法(CPSO)寻找灰色GM(2,1,λ,ρ)预测模型最优的参数λ和ρ。结合两者提出了基于CPSO–GM(2,1,λ,ρ)的早高峰短时交通流预测模型。利用VISSIM对研究路网进行微观交通仿真,通过VISSIM–Excel、VBA–Matlab平台实现了短时交通流量预测和路网微观交通仿真数据的交互,对集成交通控制系统的架构进行了方案设计。仿真结果表明,结合流量预测的路网优于固定信号配时下的路网仿真。     

Coupled matrix factorization and topic modeling for aspect mining

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.     

An Analytic Strategy to Assist Institutional Recruitment and Marketing Efforts

This article demonstrates how to fit a statistical model to historical data, test whether the model can accurately predict enrollment out-of-sample, and use the results to segment admitted students into groups so that different recruitment and marketing interventions can be applied. Conceptual and practical issues are discussed, as well as policy considerations.     

灰色理论在大学生生活费用预测中的应用

大学生生活费用的高低反映出他们的生活现状和价值取向,影响到大学生的思想道德建设和学校的教育管理,运用灰色系统理论对其生活费用进行预测,结果证明该方法的实用性。     

基于BP神经网络的高性能混凝土早龄期自干燥收缩预测

采用人工神经网络方法,建立高性能混凝土原材料配合比与其早龄期自干燥收缩之间的非线性映射关系.计算结果表明,该模型可以预测不同配合比混凝土的早龄期自干燥收缩,为高性能混凝土体积稳定性的研究提供一种思路.     

光磁共振实验中干扰峰的分析及利用

本文分析了在光磁共振实验中用扫场法测量铷原子的因子时所产生干扰峰的形成原因,并且巧妙地利用干扰峰来计算地磁场的水平分量.     

大数据时代大学生自主学习能力预测研究

大数据时代知识增长速度呈指数级增长,提升大学生自主学习能力迫在眉睫。根据大学生自主学习能力调查问卷,利用马尔可夫链构建大学生自主学习能力预测模型,通过模糊综合评判法对大学生自主学习能力进行评估,并对实验结果进行分析比较,给出不同年级、不同性别以及学习动机对自主学习能力的影响,最后给出大学生提升自主学习能力的方法与建议。     

地质超前预报系统在栋梁坡隧道施工中的应用

基于地震反射波法超前预测系统的原理及主要技术手段,以栋梁坡隧道工程为实例,针对隧道复杂的地质条件,施工过程中将超前地质预报和地质素描调查相结合,先对隧道全段范围内地形、地层岩性等进行全面核查,然后结合核查结果运用地震反射波法预报掌子面前方的节理裂隙发育情况,围岩完整性等地质信息。开挖后验证情况与预测结果基本一致,为后续预防突水塌方等隧道地质灾害事故的发生提供数据支撑,有效保证栋梁坡隧道的施工安全。