商品类型对消费者评论认知的影响：基于眼动实验

在线评论是消费者在线购买产品或服务的重要信息来源。近年来,许多商家鼓励消费者在发表文字评论的同时上传买家秀图片,表明多数商家认为含有图片的评论更有助于消费者完成购买任务。已有在线评论有用性的相关研究广泛探讨了纯文字评论对消费者决策的影响,缺少消费者对纯文字评论与含有图片评论认知差异的研究。 在此背景下,基于模式一致性理论提出不同商品类型情景下消费者对纯文字和含有图片两类评论认知行为的研究假设,从认知心理学视角系统地研究商品类型对消费者评论认知行为的影响机理;通过眼动实验收集数据,采用独立样本t检验和双因素方差分析检验研究假设;同时检验性别在商品类型与评论认知关系中的调节作用,从理论上证明商品类型对消费者评论认知的影响,为验证商品类型、评论形式和性别因素间的内在机制提供实证支持。 研究结果表明,商品类型显著影响消费者在线评论认知行为。眼动实验证实消费者在购买搜索型商品时更依赖纯文字评论做出决策。对于体验型商品,女生组眼动数据支持所有研究假设,当女性消费者购买体验型商品时,对网页中含有图片评论的关注度更高;当她们购买搜索型商品时,对网页中纯文字评论的关注度更高。性别在商品类型影响评论认知的关系中起调节作用,并给出多因素间更为复杂的交互关系。 研究结论表明消费者对两种评论形式存在认知差异,揭示了消费者在线评论认知行为规律,对于商家及社交商务平台展开有针对性的促销策略都具有实际指导意义,同时眼动实验方法的应用为消费者行为研究提供了新的视角。     

含非连续性信息多属性案例中的决策知识发现方法

医疗决策案例中非连续性属性信息大量存在,含该类信息的案例知识发现是多属性案例决策的关键和难点。该文研究了含非连续性属性信息案例中的决策知识发现,将条件概率和GAs融合技术整合到案例推理方法之中,开发了KNN的延伸方法--CRMGACP法。该方法的核心是基于Gas进行权重获取和基于融合条件概率的改进相似度算法进行案例知识获取。在某大型综合医院收集数据,获取有效数据300条,基于VC++开发实现的BC-CBRsys平台进行了实验研究,结果表明CRMGACP比其他常见方法具有更好的性能,在多个统计指标上展示出显著的优势。显然,改进的案例决策方法克服了含非连续性信息案例决策知识难以获取的问题,在临床决策领域具有广阔的前景。     

主动视觉:视动任务中的眼动

视动任务(visuomotor task)中的眼动反映了人类视觉的主动性。视动任务是指在视觉控制下的动作任务,是个体与环境交互的普遍的行为方式。在视动任务中,眼睛接受从自上而下的基于任务的眼动控制;在注视间保持和任务相关的视觉信息;并通过眼动实现视觉对任务动作的控制;而且任务的进行需要眼动和身体动作的相互协调。视动任务中的眼动研究促进了对人类视觉的新的认识——主动视觉,即强调眼睛如何运动以从环境中提取视觉信息以及如何加工被提取的信息来支持个体的行动。     

主动视觉:视动任务中的眼动

视动任务(visuomotor task)中的眼动反映了人类视觉的主动性.视动任务是指在视觉控制下的动作任务,是个体与环境交互的普遍的行为方式.在视动任务中,眼睛接受从自上而下的基于任务的眼动控制;在注视间保持和任务相关的视觉信息;并通过眼动实现视觉对任务动作的控制;而且任务的进行需要眼动和身体动作的相互协调.视动任务中的眼动研究促进了对人类视觉的新的认识--主动视觉,即强调眼睛如何运动以从环境中提取视觉信息以及如何加工被提取的信息来支持个体的行动.     

初中化学探究实验教学初探

化学是一门以实验为基础的自然科学.事实证明探究实验是化学教育的一种很有效的教学形式,它可以有效涮动学生学习兴趣,培养学生自主学习的能力,帮助学生获取化学知识,培养其科学素养.     

运用地图教学 提高学生能力

历史地图是历史教科书内容的一个重要组成部份,它是一种专门性的为适应教学需要而编制的特殊图种。它通过简明的地图语言,显示历史现象发生的时空概念,地图中载负的图形信息能够获取文字表达所不易或不能获取的     

运用地图教学 提高学生能力

历史地图是历史教科书内容的一个重要组成部份,它是一种专门性的为适应教学需要而编制的特殊图种.它通过简明的地图语言,显示历史现象发生的时空概念,地图中载负的图形信息能够获取文字表达所不易或不能获取的效果.因此注重历史地图在历史教学中的运用,可以加深学生对文字内容的理解,促进学生能力的提高.     

基于知识管理的企业信息搜寻模式研究

企业知识管理与信息管理有密切的联系,信息是知识的来源与基础,信息获取渠道与方法的优化是企业进行高效知识管理的重要条件.企业信息搜索模式可以建立在企业组织结构与信息内容上,也可以建立在信息本质特征与搜索方式上.对两种不同信息搜索模式进行综合应用才能满足企业通过知识管理建立和发展核心竞争力的需求.     

浅谈农村初中物理实验教学的问题及建议

新课程的全面实施,其根本目的是为了提高全民族素质,为学生终身发展打好基础,这就决定了物理教学中不只是让学生积累现成知识,而是充分运用认知过程来感觉现象,获取、分析、处理信息,将知识通过“内化”转变为能力.这样,学生从被动接受知识的地位向主动探索知识的地位转化.物理学是一门以实验为基础的科学,物理实验是中学物理教学的基本手段,具有重要的教学功能,中学物理实验教学是培养学生的创造性思维和创新能力的基础.由于学生自身基础差、学校及教师重视不够、实验器材匮乏等原因,使农村初中物理实验教学存在一些问题.下面就存在的问题及解决方法做一些探讨.     

构建高校图书馆阅读推广策略的发力点与价值点

阅读是通过书籍获得知识、认知世界、提高逻辑思维的一项体验活动。它是从书籍中获取知识的过程,阅读具有主动性的过程,是阅读者根据不同的读书目的加以调节控制、自我修养以及提升自我文化内涵的过程,阅读可以改变思想,从而可能改变命运。     

The Effect of Cognitive Load on Decision Making with Graphically Displayed Uncertainty Information

An experiment examined the ability of five graphical displays to communicate uncertainty information when end users were under cognitive load (i.e., remembering an eight‐digit number). The extent to which people could accurately derive information from the graphs and the adequacy of decisions about optimal behaviors based on the graphs were assessed across eight scenarios in which probabilistic outcomes were described. Results indicated that the load manipulation did not have an overall effect on derivation of information from the graphs (i.e., mean and probability estimation) but did suppress the ability to optimize behavioral choices based on the graph. Cognitive load affected people's use of some graphical displays (basic probability distribution function) more than others. Overall, the research suggests that interpreting basic characteristics of uncertainty data is unharmed under conditions of limited cognitive resources, whereas more deliberative processing is negatively affected.     

基于DNA遗传算法的表面贴装生产线负荷优化分配

印制电路板组装任务的负荷优化分配包含设备约束、工艺约束等大量约束,是电子行业表面贴装生产线中的一类重要优化问题。其优化目标是在生产节拍给定和一定约束条件下,使得不同贴装机负荷均衡,任务分配达到最优。首先,根据不同表面贴装机、不同吸嘴及多种类型元件匹配的的复杂性,提出贴装机任务分配组合优化的问题;然后分析设备和元件的参数、组装可行性、贴装时间,以及贴装优化关系等因素,并提出假设条件,建立了平衡率最大化条件下的负荷分配组合优化的数学模型;最后,针对贴装生产线负荷分配问题的复杂性与特殊性,通过改良编码方式后的DNA遗传算法来优化组合数学模型,计算适应度,并借助MATLAB进行仿真求解,进而找到最优解。结果表明：本文提出的贴装生产线负荷分配方法可以解决带复杂约束的印制电路板组装负荷优化分配问题,提高设备的平衡率和生产效率,促进生产线的优化运行。     

On the Selection of Distributions for Stochastic Variables

One of the main steps in an uncertainty analysis is the selection of appropriate probability distribution functions for all stochastic variables. In this paper, criteria for such selections are reviewed, the most important among them being any a priori knowledge about the nature of a stochastic variable, and the Central Limit Theorem of probability theory applied to sums and products of stochastic variables. In applications of these criteria, it is shown that many of the popular selections, such as the uniform distribution for a poorly known variable, require far more knowledge than is actually available. However, the knowledge available is usually sufficient to make use of other, more appropriate distributions. Next, functions of stochastic variables and the selection of probability distributions for their arguments as well as the use of different methods of error propagation through these functions are discussed. From these evaluations, priorities can be assigned to determine which of the stochastic variables in a function need the most care in selecting the type of distribution and its parameters. Finally, a method is proposed to assist in the assignment of an appropriate distribution which is commensurate with the total information on a particular stochastic variable, and is based on the scientific method. Two examples are given to elucidate the method for cases of little or almost no information.     

知识积累、知识激活与创新能力关系研究

关于知识与创新间的关系,现有研究多强调不同类型知识对创新总体绩效的影响差异,较少关注同一知识基对创新微观过程的不同阶段是否具有同等影响。针对这一理论缺口,本研究首先建构了知识积累与基于创新微观过程的企业创新能力间关系模型,并利用问卷调查获得的419份国内企业数据、利用层次回归方法进行实证检验。研究发现：企业知识积累对创新能力具有显著提升作用,并且这种积极效应贯穿在创新的整个微观过程中;分别以内、外组织学习为特征的不同知识激活机制在促进知识贡献于创新不同阶段能力提升过程中具有不同影响。本文最后讨论了理论贡献、管理意义及局限。     

Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia

In this article, the use of time series of satellite imagery to flood hazard mapping and flood risk assessment is presented. Flooded areas are extracted from satellite images for the flood‐prone territory, and a maximum flood extent image for each flood event is produced. These maps are further fused to determine relative frequency of inundation (RFI). The study shows that RFI values and relative water depth exhibit the same probabilistic distribution, which is confirmed by Kolmogorov‐Smirnov test. The produced RFI map can be used as a flood hazard map, especially in cases when flood modeling is complicated by lack of available data and high uncertainties. The derived RFI map is further used for flood risk assessment. Efficiency of the presented approach is demonstrated for the Katima Mulilo region (Namibia). A time series of Landsat‐5/7 satellite images acquired from 1989 to 2012 is processed to derive RFI map using the presented approach. The following direct damage categories are considered in the study for flood risk assessment: dwelling units, roads, health facilities, and schools. The produced flood risk map shows that the risk is distributed uniformly all over the region. The cities and villages with the highest risk are identified. The proposed approach has minimum data requirements, and RFI maps can be generated rapidly to assist rescuers and decisionmakers in case of emergencies. On the other hand, limitations include: strong dependence on the available data sets, and limitations in simulations with extrapolated water depth values.     

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

Many real-world optimization problems involve two different subsets of variables: decision variables, and those variables which are not present in the cost function but constrain the solutions, and thus, must be considered during optimization. Thus, dependencies between and within both subsets of variables must be considered. In this paper, an estimation of distribution algorithm (EDA) is implemented to solve this type of complex optimization problems. A Gaussian Bayesian network is used to build an abstraction model of the search space in each iteration to identify patterns among the variables. As the algorithm is initialized from data, we introduce a new hyper-parameter to control the influence of the initial data in the decisions made during the EDA execution. The results show that our algorithm improves the cost function more than the expert knowledge does.

     

A Critical Reflection on the Experimental Method for Planning Research: Testing the Added Value of PSS in a Controlled Environment

For planning research to successfully generate usable mechanisms for planning practitioners more hypothesis-testing research designs are needed. Currently, the academic field seems more geared toward generating hypotheses, either by observing practice or from theoretical studies. This approach is especially common in research that generates knowledge of planning. In this paper, I map several relevant research designs that allow for such hypothesis testing and discuss their usability in planning research. Then, I particularly focus on the experimental method as a promising design for generating contextualized mechanisms for planning practice. I describe and analyze a study that aimed to develop mechanisms about the added value of knowledge technologies for the quality of planning. Two consecutive experiments are described in detail, after which the usability of the experimental method for planning research is discussed. Reflecting on these experiments indicate that the main strength of this research design is that it allows to create ideal circumstances for falsification (which is virtually impossible in planning practices. The main weakness is the ecological validity of findings, especially when the research is executed with students.     

基于个人知识地图的专家推荐

利用维基百科作为背景知识构建出专家个人的知识地图,从而直观量化地度量专家的知识构成和研究兴趣。在此基础上,提出了基于知识节点密度和最大公共子图的2种推荐算法,并且将这2种推荐算法和经典的推荐算法结合。最后,用一个真实的数据集合验证:这种基于个人知识地图的专家推荐是有效的。     

The Effectiveness of Different Representations for Managerial Problem Solving: Comparing Tables and Maps

Geographic Information Systems (GIS) enable decision makers to view tabular data geographically, as maps. This simple yet powerful visual format appears to facilitate problem solving, yet how it does so is not clear, nor do we know the types of problems that benefit from this representation. To begin to understand the contributions of geographic representations over tabular representations, we conducted a three-factor experiment in problem solving. The experiment contained two different representations (map and table), three different geographic relationships (proximity, adjacency, and containment), and three levels of task difficulty (low, medium, and high). We found that maps generally produced faster problem solving than tables, and that problem-solving time increased with task difficulty. Most importantly, for the proximity and adjacency geographic relationships we found that maps kept problem-solving time low, while tables tended to increase time dramatically. However, we found that the number of knowledge states for each task explains performance times quite well and is a useful tool for understanding performance differences and interaction effects. As tasks become more difficult, representing them as maps generally keeps the number of knowledge states small, while for tables, the number of knowledge states increases dramatically. Correspondingly, problem-solving times increase dramatically with tables, but not with maps. In sum, as difficulty increases, maps are more effective for problem-solving tasks. Using maps, the tasks are simplified using visual heuristics that keep problemsolving times and error rates from rising as quickly as they do with tables.     

A systematic literature review of mining weak signals and trends for corporate foresight

Due to the ever-growing amount of data, computer-aided methods and systems to detect weak signals and trends for corporate foresight are in increasing demand. To this day, many papers on this topic have been published. However, research so far has only dealt with specific aspects, but it has failed to provide a comprehensive overview of the research domain. In this paper, we conduct a systematic literature review to organize existing insights and knowledge. The 91 relevant papers, published between 1997 and 2017, are analyzed for their distribution over time and research outlets. Classifying them by their distinct properties, we study the data sources exploited and the data mining techniques applied. We also consider eight different purposes of analysis, namely weak signals and trends concerning political, economic, social and technological factors. The results of our systematic review show that the research domain has indeed been attracting growing attention over time. Furthermore, we observe a great variety of data mining and visualization techniques, and present insights on the efficacy and effectiveness of the data mining techniques applied. Our results reveal that a stronger emphasis on search strategies, data quality and automation is required to greatly reduce the human actor bias in the early stages of the corporate foresight process, thus supporting human experts more effectively in later stages such as strategic decision making and implementation. Moreover, systems for detecting weak signals and trends need to be able to learn and accumulate knowledge over time, attaining a holistic view on weak signals and trends, and incorporating multiple source types to provide a solid foundation for strategic decision making. The findings presented in this paper point to future research opportunities, and they can help practitioners decide which sources to exploit and which data mining techniques to apply when trying to detect weak signals and trends.