基于社会责任的国有林业企业综合绩效评价指标的研究

本文根据林业企业在追求企业效益过程中存在忽视企业承担的社会责任的现象,利用层次分析法和模糊综合评价法构建企业基于社会责任的企业综合绩效评价指标,并对该项评价指标进行了综合评价。     

企业信息门户成熟度综合评价研究

随着互联网技术的飞速发展,企业信息门户(Enterprise Information Portal,EIP)在现代企业管理中扮演着越来越重要的角色,因此有必要去对企业信息门户的成熟度进行综合评价研究。首先阐述了EIP的概念、功能,进而提出了企业信息门户成熟度的概念;其次在结合EIP自身特点的基础上,构建了EIP成熟度的综合评价指标体系;并选用层次分析法(AHP)与模糊综合评价法进行评价,即利用AHP法确定指标权重,模糊综合评价法计算评价对象的量化得分;最后,选取某一企业的信息门户建设案例,进行了综合评价研究,并分析了案例中的相关问题。     

中小企业协同制造中投标企业可拓评价方法

针对中小企业协同制造过程中投标企业的综合评价问题,提出符合中小企业特点的评价指标,然后给出了形式化的投标企业评价物元模型,进行投标企业可拓综合评价。该方法利用可拓理论对经典集合理论和模糊集合理论的语义进行扩展,通过关联度、合格度和优度计算,客观地给出了投标企业的综合评价结果,并在中小企业网络化协作系统中得到验证。     

基于AHP的企业循环经济发展水平灰色综合评价研究

针对多指标综合评价的实际问题,将Delphi法、层次分析法、灰色关联和模糊综合评价综合集成为灰色综合评价模型,以充分利用专家评判信息的模糊性与灰性.通过设置协调性、效益性、减量化、资源化、再利用、健康性、稳定性等7大类30个指标,建立灰色综合评价模型,利用该模型对企业循环经济发展水平进行综合评价研究,通过计算,该组合评价方法应用企业循环经济发展水平的评价可行且有效.     

基于AHP方法的高校图书馆期刊重要度评价

利用层次分析法(The Analytic Hierarchy Process,简称AHP)原理,结合流通统计法等多种定量分析方法,建立了高校期刊综合评价指标体系;并对评价指标、具体评价过程和应用方法进行了解释说明.     

基于AHP的宁夏财政扶持企业项目绩效评价指标权重设置研究

财政扶持企业项目是宁夏大力扶持企业加快发展,促进经济、社会进步的重大战略举措。财政扶持企业项目绩效评价是加强政府资金宏观管理,提高资金使用效用的重要手段。在综合评价中,评价指标的选取是否合适,指标权重的确定科学与否,直接影响到综合评价的结果。此次绩效评价通过群组层次分析法设立指标体系,利用专家的判断矩阵,确定指标权重,为利用模糊综合评判进行绩效评价奠定了科学基础。     

企业科技成果转化综合评价研究——以重庆市为例

根据企业科技成果转化的特点,建立评价指标体系;在相关专家和学者调查、讨论的基础上。利用层次分析法统计出评价体系中各指标所占的权重;将同层指标进行归一化处理;最后利用综合统计模型得出综合绩效值。以重庆市为例,对企业科技成果转化情况进行综合评价,并提出了相应的对策建议。     

政府面向企业信息服务水平的数学评价模型与应用

2008年5月1日,<中华人民共和国政府信息公开条例>正式施行,我国各地政府纷纷加大了针对企业的信息服务力度.政府面向企业信息服务水平的数学评价,首先采用专家打分法提出政府面向企业信息服务体系的评价指标.主观指标采用模糊综合评价方法,客观指标采用动态综合评价方法,最后将两者结合产生数学评价模型,利用此模型进行了实例应用.结果表明:该模型较好地反映了政府信息服务水平.为政府决策提供了参考.     

基于灰色理论的企业技术创新能力综合评价模型

企业技术创新能力的强弱决定了企业的发展能力。而企业管理者对本企业创新能力能否做出正确的评价关系到企业的成败。文章通过对企业技术创新能力的分析,建立了技术创新能力的评价指标体系,设计了一种基于灰色理论的模糊综合评价方法,即采用灰色统计的方法来确定模糊评判矩阵,并利用灰色关联度来确定评价指标的权重,然后运用模糊方法对企业技术创新能力进行了综合评价,从而使得对其评价更为科学合理。     

面向过程的企业文化建设测评体系构建

基于PDCA循环理论,对企业文化建设的基本过程进行了研究.分析了建设过程中每个步骤的特点;建立了面向过程的企业文化建设评价指标体系;采用灰色统计方法建立测评矩阵,利用灰色关联度法确定评价指标的权重,并运用模糊综合评价方法对企业文化建设过程进行合理的评价.最后,通过实例验证了该测评体系的有效性和可行性.     

科研合作网络及特性分析

为揭示超网络的某些特性,基于小世界模型和无标度模型,提出和建立两种超网络演化模型,并定义交叉度用于表征超网络节点之间合作关系,最后将超网络演化模型应用于中国原子能科学研究院核技术应用研究所科研合作网的实证分析。研究结果表明:不论是对于超网络理论模型还是实证分析,交叉度都能够优于以往的网络特性用于分析超网络不同层次之间节点的相互合作关系,为从理论上进一步深入分析超网络及其应用打下一定的基础。     

A Dual-Pointer guided transition system for end-to-end structured sentiment analysis with global graph reasoning

Structured sentiment analysis is a newly proposed task, which aims to summarize the overall sentiment and opinion status on given texts, i.e., the opinion expression, the sentiment polarity of the opinion, the holder of the opinion, and the target the opinion towards. In this work, we investigate a transition-based model for end-to-end structured sentiment analysis task. We design a transition architecture which supports the recognition of all the possible opinion quadruples in one shot. Based on the transition backbone, we then propose a Dual-Pointer module for more accurate term boundary detection. Besides, we further introduce a global graph reasoning mechanism, which helps to learn the global-level interactions between the overlapped quadruples. The high-order features are navigated into the transition system to enhance the final predictions. Extensive experimental results on five benchmarks demonstrate both the prominent efficacy and efficiency of our system. Our model outperforms all baselines in terms of all metrics, especially achieving a 10.5% point gain over the current best-performing system only detecting the holder-target-opinion triplets. Further analyses reveal that our framework is also effective in solving the overlapping structure and long-range dependency issues.     

Locality-adapted kernel densities of term co-occurrences for location prediction of tweets

While geographical metadata referring to the originating locations of tweets provides valuable information to perform effective spatial analysis in social networks, scarcity of such geotagged tweets imposes limitations on their usability. In this work, we propose a content-based location prediction method for tweets by analyzing the geographical distribution of tweet texts using Kernel Density Estimation (KDE). The primary novelty of our work is to determine different settings of kernel functions for every term in tweets based on the location indicativeness of these terms. Our proposed method, which we call locality-adapted KDE, uses information-theoretic metrics and does not require any parameter tuning for these settings. As a further enhancement on the term-level distribution model, we describe an analysis of spatial point patterns in tweet texts in order to identify bigrams that exhibit significant deviation from the underlying unigram patterns. We present an expansion of feature space using the selected bigrams and show that it eventually yields further improvement in prediction accuracy of our locality-adapted KDE. We demonstrate that our expansion results in a limited increase in the size of feature space and it does not hinder online localization of tweets. The methods we propose rely purely on statistical approaches without requiring any language-specific setting. Experiments conducted on three tweet sets from different countries show that our proposed solution outperforms existing state-of-the-art techniques, yielding significantly more accurate predictions.     

基于数据挖掘技术的图书馆流通数据的关联分析

运用改进的Apriori算法,对具有代表性的与分析任务相关的数据进行样本抽取,利用Weak作为数据挖掘算法开发工具,产生了一系列强关联规则。根据这些规则,我们可以解读出一些现象,它不仅能揭示隐藏在大量数据后的重要关系信息,同时也为这种关系提供了量化描述手段。这些定性定量的信息不仅能对图书馆的各项工作提供技术上的支持,还可对学校的教学,课程的设置,学科的交叉渗透等提供信息。     

History-dependent percolation on multiplex networks

The structure of interconnected systems and its impact on the system dynamics is a much-studied cross-disciplinary topic. Although various critical phenomena have been found in different models, study of the connections between different percolation transitions is still lacking. Here we propose a unified framework to study the origins of the discontinuous transitions of the percolation process on interacting networks. The model evolves in generations with the result of the present percolation depending on the previous state, and thus is history-dependent. Both theoretical analysis and Monte Carlo simulations reveal that the nature of the transition remains the same at finite generations but exhibits an abrupt change for the infinite generation. We use brain functional correlation and morphological similarity data to show that our model also provides a general method to explore the network structure and can contribute to many practical applications, such as detecting the abnormal structures of human brain networks.     

Profiting from voluntary information spillovers: how users benefit by freely revealing their innovations

Empirical studies of innovation have found that end users frequently develop important product and process innovations. Defying conventional wisdom on the negative effects of uncompensated spillovers, innovative users also often openly reveal their innovations to competing users and to manufacturers. Rival users are thus in a position to reproduce the innovation in-house and benefit from using it, and manufacturers are in a position to refine the innovation and sell it to all users, including competitors of the user revealing its innovation. In this paper, we explore the incentives that users might have to freely reveal their proprietary innovations. We then develop a game-theoretic model to explore the effect of these incentives on users’ decisions to reveal or hide their proprietary information. We find that, under realistic parameter constellations, free revealing pays. We conclude by discussing some implications of our findings.     

TLS握手协议的计算模型分析*

本文用BR模型对TLS握手协议进行了计算分析与安全性证明. TLS握手协议作为典型的认证密钥交换协议,可以自然结合到BR认证协议计算模型分析框架当中. 本文对该协议主要通信过程进行了总结、建模和分析,证明当协议所采用的公钥加密函数和消息认证函数均满足计算模型安全性要求时,TLS握手协议在BR计算模型下是安全的.     

百度百科用户参与行为与参与动机关系的实证研究

 百度百科是采用大众生产这一新型知识生产方式运作的在线百科。本文以百度百科为对象,构建了百度百科用户参与动机与参与行为关系的结构方程模型。结果显示：实用价值动机、兴趣动机、胜任性动机、交往动机和求知动机皆影响其参与行为。     

武汉光电子信息产业专利标准化机制研究

专利标准化日益成为国际技术创新和市场竞争中国家利益的重要实现途径.本文应用质性研究方法,分析武汉光电子信息产业具有代表性的9个案例企业的R&D与标准化活动,以识别和揭示促进自主专利标准化的机制.依据该分析,本文构建了一个包含11个机制的命题框架,并将这些机制与内部或外部集成相联系,拓展了集成创新理论的现有研究.本文发现的这一机制框架在一定范围内解释了影响专利标准化过程的因素,有助于在制定微观和宏观标准化战略时避免战略盲点;同时,该框架也为后续研究提供了演绎性起点.     

A comparative assessment of sentiment analysis and star ratings for consumer reviews

Electronic word of mouth (eWOM) is prominent and abundant in consumer domains. Both consumers and product/service providers need help in understanding and navigating the resulting information spaces, which are vast and dynamic. The general tone or polarity of reviews, blogs or tweets provides such help. In this paper, we explore the viability of automatic sentiment analysis (SA) for assessing the polarity of a product or a service review. To do so, we examine the potential of the major approaches to sentiment analysis, along with star ratings, in capturing the true sentiment of a review. We further model contextual factors (specifically, product type and review length) as two moderators affecting SA accuracy. The results of our analysis of 900 reviews suggest that different tools representing the main approaches to SA display differing levels of accuracy, yet overall, SA is very effective in detecting the underlying tone of the analyzed content, and can be used as a complement or an alternative to star ratings. The results further reveal that contextual factors such as product type and review length, play a role in affecting the ability of a technique to reflect the true sentiment of a review.