医院信息系统综合评价体系模型

为推动医院信息化建设工作的规范化,加强医院信息化建设的管理、监督、指导和评审工作,构建出一个具有广泛代表性的医院信息系统评价体系模型,使得我区医院信息化进程能够持续健康的发展. 我们运用一种改进的层次分析法原理,对指标体系中的每一个单一指标进行权重求解,从而得出一个可以求解出系统性能测试和功能测试总得分的指标体系,对医院信息系统进行综合评价. 通过该模型对新疆医科大学两家附属医院信息系统进行计算评分,最终科学有效地评价了该两家医院的信息系统,找出医院信息化工作的不足之处并给出了科学的改进建议.     

Understanding buyers' loyalty to a C2C platform: the roles of social capital,satisfaction and perceived effectiveness of e‐commerce institutional mechanisms

Drawing upon social capital theory, this study aims to investigate how different dimensions of social capital affect online buyers' satisfaction and ultimately boost their loyalty to a Consumer‐to‐Consumer (C2C) platform. Specifically, we propose that three dimensions of social capital (i.e., cognitive, structural and relational capital) contribute positively to the two types of online buyers' satisfaction (i.e., economic and social satisfaction). In addition, we posit that perceived effectiveness of e‐commerce institutional mechanisms (PEEIM) moderates the relationships between economic and social satisfaction and buyers' loyalty to the platform. Three hundred buyers on the Consumer‐to‐Consumer platform, TaoBao, were surveyed to test the proposed model. The results suggest that buyers' evaluation of social capital with the community of sellers can enhance their satisfaction with the sellers, which subsequently affect their loyalty to the platform. Furthermore, perceived effectiveness of e‐commerce institutional mechanisms negatively moderates the effect of economic satisfaction and positively moderates the effect of social satisfaction on buyers' loyalty to the platform. The theoretical contributions and practical implications are discussed.     

An AK-BRP dictionary learning algorithm for video frame sparse representation in compressed sensing

Sparsifying transform is an important prerequisite in compressed sensing. And it is practically significant to research the fast and efficient signal sparse representation methods. In this paper, we propose an adaptive K-BRP (AK-BRP) dictionary learning algorithm. The bilateral random projection (BRP), a method of low rank approximation, is used to update the dictionary atoms. Furthermore, in the sparse coding stage, an adaptive sparsity constraint is utilized to obtain sparse representation coefficient and helps to improve the efficiency of the dictionary update stage further. Finally, for video frame sparse representation, our adaptive dictionary learning algorithm achieves better performance than K-SVD dictionary learning algorithm in terms of computation cost. And our method produces smaller reconstruction error as well.

     

Top-<Emphasis Type="Italic">k</Emphasis> temporal keyword search over social media data

Social media services have already become main sources for monitoring emerging topics and sensing real-life events. A social media platform manages social stream consisting of a huge volume of timestamped user generated data, including original data and repost data. However, previous research on keyword search over social media data mainly emphasizes on the recency of information. In this paper, we first propose a problem of top-k most significant temporal keyword query to enable more complex query analysis. It returns top-k most popular social items that contain the keywords in the given query time window. Then, we design a temporal inverted index with two-tiers posting list to index social time series and a segment store to compute the exact social significance of social items. Next, we implement a basic query algorithm based on our proposed index structure and give a detailed performance analysis on the query algorithm. From the analysis result, we further refine our query algorithm with a piecewise maximum approximation (PMA) sketch. Finally, extensive empirical studies on a real-life microblog dataset demonstrate the combination of two-tiers posting list and PMA sketch achieves remarkable performance improvement under different query settings.     

基于Web of Science的社会化媒体环境下社区发现研究综述

社会化媒体是一种新型在线媒体,发现并研究其中的社区有利于揭示社会化媒体环境下信息传播与共享的特点和规律。该文基于Web of Science检索得到的文献数据,使用CiteSpace、SATI、UCINET等科学知识图谱软件,从共被引文献、关键词及突现词等角度构建了社会化媒体环境下有关社区发现的科学知识图谱,并对该领域的研究现状、知识演进过程、研究热点和研究前沿进行了可视化分析。     

基于桥系数的分裂社区检测算法研究

研究社区结构有助于揭示网络结构和功能之间的关系,而社区检测是社区结构研究的基础和核心。该文定义了一种聚集度桥系数,将其应用到社区检测中,设计出一种分裂社区检测方法,包括分裂和合并两个算法。分裂算法使用桥系数识别社区间边,通过迭代删除社区间边分解网络,从而发现网络中的社区结构;合并算法根据社区连接强度合并社区,可以揭示社区结构中的分层嵌套的现象。在六个社会网络数据集上的实验表明,本文算法可以有效的将网络分裂为有意义的社区,并且准确性接近或超过经典的社区检测算法。     

分布电容对施密特触发器与RC微分电路构成的多谐振荡电路的影响

由多谐振荡器构成的交流电路在传输信号时,微小的电容量变化,可能会严重影响电路工作.描述了实验室废液收集器检测电路误报警的故障现象.依据检测装置的结构和工作原理,结合实验数据和仿真结果,分析并得出导致故障产生的原因为不同连接状态分布电容的差异,并给出分布电容的构成.针对如何弱化分布电容的影响,给出了解决措施.实测结果表明,对电缆屏蔽皮接电源地可以消除241pF分布电容.     

基于Hadoop平台的分布式SVM参数寻优

参数的选择对算法分类与预测的正确率有直接影响。在参数选择中全局网格搜索有着计算可靠、简单、优化效果明显的优势,适合应用于可靠性要求高的工程运算,如在复杂系统的故障诊断中对故障模式识别算法进行参数寻优等。但是,全局网格搜索在寻优过程中耗时过长,仍然是一个制约其使用的问题,尤其对于实时性要求较高的系统。以支持向量机的参数全局寻优问题为例,针对网格搜索寻优时间长的缺点,利用Hadoop平台进行分布式参数寻优,借助HDFS将参数自动划分到计算节点上,并运用MapReduce计算框架建立分布式参数寻优模型,完成模型训练预测及参数优化。实验结果表明,在不降低算法性能的前提下提高了寻优效率。     

运动物体的头戴式视线追踪控制系统设计

设计了一种基于嵌入式的头戴式视线追踪控制系统.它将实际运动物体作为控制对象,以人眼为控制源,采用图像处理算法和坐标映射模型,实现视线追踪技术.通过质心检测和椭圆拟合算法的比较与优化,系统定位瞳孔中心的角度误差精确到1.4°以内,再利用最小二乘法进行视线空间坐标变换,最终控制运动对象移动到注视点.实验结果表明,人眼距离被控对象2 m内时,被控对象的定点误差在5 cm内,系统响应时间在0.3 s内,满足用户日常操作要求.     

云计算研究

云计算模式是在基础设施即服务（IaaS）、平台即服务（PaaS）、软件即服务（SaaS）、分布式计算、并行计算和网格计算等概念演进并产生的结果。云计算模式是一种全新的计算应用模式,将会成为人们获取服务的主导方式。本文首先介绍云计算在业界中的概念,接着分析云计算和相关计算,最后关于云计算发展的前景进行展望。