基于模块化引文网络的网络计量学知识扩散水平测度

以国内网络计量学期刊论文的题录和引文数据为基础,用 Gephi 软件构建引文网络,并进行模块化分析。利用知识净扩散量和知识扩散相对速度两种计量指标对探测到的四个模块化子网的知识扩散水平进行测度,最后结合实际对测度结果进行了分析。     

基于信息风险感知的社交网络舆情传播模型研究

结合实际的社交网络使用行为,在分析社交网络舆情传播模式的基础上,构建了社交网络舆情传播模型。详细分析了影响传播概率的主要因素,建立了以信息风险感知为主的传播概率的数学模型。仿真结果表明：获知概率的大小对舆情传播规模、弛豫时间均有影响;而信息风险感知的大小对舆情传播规模的影响不大,对弛豫时间的影响较大,究其根源在于信息风险感知直接决定了传播概率的取值,而传播概率直接决定了网络中传播者的数量。     

移动图书馆情景感知信息推荐服务用户接受行为研究

[目的/意义]基于多理论假设,为移动图书馆平台提供基于情景感知的信息推荐服务提供理论和实证依据,以丰富移动图书馆个性化服务的内容。[方法/过程]结合技术接受行为理论、创新扩散理论和感知风险理论,构建基于情景感知的信息推荐服务用户接受行为影响因素模型,并运用逻辑回归方法进行实证研究。[结果/结论]情景感知信息推荐服务作为移动图书馆信息服务的新方式,有着个性化的显著优势。同时,技术和设备的兼容为移动图书馆情景感知信息推荐服务提供了外部动力和重要保障。     

Modeling Growth in Electronic Learning Environments Using a Longitudinal Random Item Response Model

In educational environments, monitoring persons' progress over time may help teachers to evaluate the effectiveness of their teaching procedures. Electronic learning environments are increasingly being used as part of formal education and resulting datasets can be used to understand and to improve the environment. This study presents longitudinal models based on the item response theory (IRT) for measuring persons' ability within and between study sessions in data from web-based learning environments. Two empirical examples are used to illustrate the presented models. Results show that by incorporating time spent within- and between-study sessions into an IRT model; one is able to track changes in ability of a population of persons or for groups of persons at any time of the learning process.     

The Multidimensionality of Measurement Bias in High-Stakes Testing: Using Machine Learning to Evaluate Complex Sources of Differential Item Functioning

Test developers and psychometricians have historically examined measurement bias and differential item functioning (DIF) across a single categorical variable (e.g., gender), independently of other variables (e.g., race, age, etc.). This is problematic when more complex forms of measurement bias may adversely affect test responses and, ultimately, bias test scores. Complex forms of measurement bias include conditional effects, interactions, and mediation of background information on test responses. I propose a multidimensional, person-specific perspective of measurement bias to explain how complex sources of bias can manifest in the assessment of human knowledge, skills, and abilities. I also describe a data-driven approach for identifying key sources of bias among many possibilities—namely, a machine learning method commonly known as regularization.     

Small Circles: Mobile Telephony and the Cultivation of the Private Sphere

This article examines how we use mobile telephony to maintain our physically and socially closest social circle. The analysis is based on traffic data gathered from Norway using approximately 24 million calls and texts made by private individuals. Previous research has shown that our temporal and spatial movement is highly predictable and that the majority of calls and text messages are sent to only four to six different persons. This article extends this research by examining both tie strength and the distance between the interlocutors in urban and rural settings. The findings show that even as information and communication technologies (ICTs) potentially put the world at our fingertips, the mobile phone is an instrument of a more limited geographical and social sphere. Approximately two-thirds of our calls/texts go to strong ties that are within a 25-km radius.     

嵌入式系统亚稳态高阶扩散模糊决策能耗管理

嵌入式系统最优能耗敏捷管理技术是解决广泛应用下系统能耗制约的关键。传统的系统能耗敏捷管理方法采用基于双基准态稳定下的判决思想,对固定的稳态和固定的非稳态进行低阶扩散研究,受限于低阶扩散特性对于系统高阶风险管理的片面性,无法实现最优敏捷管理。提出一种基于亚稳态高阶扩散模糊决策的最优嵌入式系统能耗敏捷管理设计,采用亚稳态下的高阶扩散特性,对嵌入式系统亚稳态下的实际特性进行描述分析,采用亚稳态描述符判断中间量,然后基于模糊决策思想,对亚稳态高阶扩散进行模糊向量积分,得到模糊决策,并将模糊决策结果应用于能耗管理,实现最优能耗敏捷管理。最后采用实际的嵌入式系统进行最优化系统能耗敏捷管理测试实验,结果表明,该方法的嵌入式系统在各种风险态下的能耗管理均优于传统方法,能耗平均节约37%,对于系统能耗管理具有很好的指导意义。     

大脑是如何处理信息的——神经元及其模型的计算阐释

神经元作为神经系统的基本单元,提供了人类认知的基本信息功能处理机制。文章通过对神经元及其模型进行计算分析,指出了神经元内离子对刺激信息的反应,在此基础上阐释了误差驱动任务学习与BP学习法,最后文章对神经元的计算启示给出了解读。文章表明,脑认知是动态的表征,其非线性地处理认知现象,并指出计算神经科学在解读大脑处理信息上正在尝试突破,具有重要的研究意义和价值。     

论运动素质转移与运动项目迁移的关系* ——以健美操与体育舞蹈、体操为例

运动素质是可以相互转移的,各个运动项目需要的身体素质不尽相同,但他们之间都是可以相互转移的。通过描述了多个运动项目之间的身体素质之间的关系和转移,主要介绍了竞技健美操与体育舞蹈之间素质的转移,说明,每个项目需要的身体素质不同,运动员都要朝着正迁移的方向来进行训练。     

Understanding information diffusion with psychological field dynamic

Several approaches focus on how to automatically capture the latent features from original diffusion data and predict the future scale of cascades utilizing a black box framework. However, they ignore the penetrating insight into the underlying mechanism that how each participant is involved in the cascade. In this work, we bridge the gap between prediction and understanding of information diffusion by incorporating deep learning techniques and social psychology. To characterize individual participation driven by both subjective and objective impetus and integrate it into the macro-level cascade, we propose an end-to-end model, named PFDID, which is designed based on the field dynamics theory of psychology, including the intrinsic cognition field and the extrinsic environment field. We represent these two field dynamics respectively with the pairwise semantic relation between the message itself and corresponding comment and the forwarder’s micro-community activity embedding to provide educated explanations for forwarding behaviour. Afterwards, the cross infusion mechanism is designed to calculate the mutual influence of inhomogeneous field dynamics inside users and cross influence of homogeneous field dynamics among individuals, whose output is fed into the diffusion network aggregation layer for the cascade size prediction. Extensive experiments on two typical social networks, Sina Weibo and Twitter, manifest that the proposed PFDID outperforms state-of-the-art approaches. Our model achieves excellent prediction results, with MSLE = 1.856 on Sina Weibo and MSLE = 1.962 on Twitter, providing 6.54% and 10.53% relative performance gains, respectively. Furthermore, the interpretability is also discussed based on detailed visualization. We observe that the psychological impetus behind social behaviour varies mainly following two patterns with the spread of information, including gradual change and joint influence. Additionally, the indirect dependencies have also been verified.