信息资源管理研究的多重视角及其共同体的形成（Ⅲ）——学科互涉特征与IRM共同体的形成

信息资源管理领域中,以政府、企业和情报学界的不同视角的研究各具特点。随着发展,其研究将会由关注技术转向关注内容,并进而形成一种综合型研究视角和整合机制。自然科学领域中"科学共同体"概念适用于人文社科领域,也适用于IRM。IRM共同体有其共同规范。     

基于MATLAB的Reed-Solomon编解码仿真

RS纠错码被广泛用在各种数字通信、数字广播和数据存储系统中.本文解释了纠错理论及RS码的相关原理,通过MATLAB仿真分析RS编码的误码率与信噪比之间的关系.     

用于再生伪码测距时钟恢复的新型码片跟踪环性能分析

为了提高低信噪比条件下再生伪码测距系统中的复合伪码时钟恢复性能,采用了一种新型的码片跟踪环.此码片跟踪环是一种改进型的数据转换跟踪环,两者之间的区别在于,码片跟踪环的Q路输出直接乘上了时钟分量,而数据转换跟踪环乘上了I路的转换检测器输出.在准方波伪码测距信号的情况下,详细分析了码片跟踪环的跟踪(均方定时抖动)性能.分析对比了不同码片信噪比条件下的码片跟踪环和数据转换跟踪环跟踪性能.分析表明,码片跟踪环和数据转换跟踪环在大信噪比条件下性能一致;而在小信噪比条件下,码片跟踪环性能明显好于数据转换跟踪环.     

泊车机器人障碍物视觉识别系统研究

针对智能停车库中的泊车机器人视觉系统研究需求,提出一种基于双目视觉的泊车机器人障碍物识别系统。通过双目摄像头进行图像采集,利用张正友棋盘标定法进行双目相机标定;采用Bouguet进行立体校正,将高斯滤波与拉普拉斯算子相结合进行图像预处理;采用YOLO卷积神经网络对目标障碍物进行快速识别;利用区域匹配算法进行立体匹配并生成目标障碍物视差图;通过成像点和目标障碍物的立体几何关系计算得到目标障碍物的深度信息。实验结果表明,该系统具有良好的实时性和较高精度,障碍物识别时间平均为0.0901s,在2 600mm具有最佳测距精度,可为泊车机器人自动泊车提供保障。     

关于利用"出版号"作书次号的探讨--兼与蒋鸿标先生商榷

"出版号"即出版者代号+出版物的流水号(即书名号),它是中国标准书号的一部分,按序排列,形成一个恒寂的8位数.这个编码系统科学性强,且具唯一性.利用它作书次号排架,乱架率可达零;索书,漏检率可达零;组织目录,款目错位率适可达零.     

Why would medical publishers not incorporate core bioethical values into their Ethics Guidelines?

It is quite universally acknowledged by bioethicists, at least in the western world, that respect for the patients’ autonomy, non-malevolence, beneficence, and justice (also called equity) are four core ethical values in medicine. The Ethics Guidelines of key journals in laboratory medicine are not explicit about the first three of these values, and even implicitly, they seem to miss values of justice. Health equity being one of the main objectives of public health policy across the world, we suggest that values of equity explicitly become part of the Ethics Guidelines of laboratory medicine journals. Biochemia Medica could show the way to other medical publishers by incorporating into its Ethics Guidelines these very important core bioethical values.     

浅谈我馆图书索书号取法

随着科学技术的发展,各高校图书馆都在探究适合自身的图书取号方法,并在实际的工作中不断摸索改进,基本上都确立了各自的取号方法。本文首先对索书号的构成进行了叙述,重点对华侨大学厦门工学院图书馆图书索书号的取法进行了阐述,针对一般图书、不同版本图书、多卷书、连续出版物、配套书以及套书种次号的取法进行论述,全面讲述了一个让图书馆的藏书排架更科学合理、便于利用的图书取号法。     

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.     

MTGCN: A multi-task approach for node classification and link prediction in graph data

Both node classification and link prediction are popular topics of supervised learning on the graph data, but previous works seldom integrate them together to capture their complementary information. In this paper, we propose a Multi-Task and Multi-Graph Convolutional Network (MTGCN) to jointly conduct node classification and link prediction in a unified framework. Specifically, MTGCN consists of multiple multi-task learning so that each multi-task learning learns the complementary information between node classification and link prediction. In particular, each multi-task learning uses different inputs to output representations of the graph data. Moreover, the parameters of one multi-task learning initialize the parameters of the other multi-task learning, so that the useful information in the former multi-task learning can be propagated to the other multi-task learning. As a result, the information is augmented to guarantee the quality of representations by exploring the complex constructure inherent in the graph data. Experimental results on six datasets show that our MTGCN outperforms the comparison methods in terms of both node classification and link prediction.     

字数笔画与邮发代号相结合--一种简便合理的中文报纸排架排序法

本文提出了一种简便合理的中文报纸排架排序法——字数笔画与邮发代号相结合的方法。这种方法主要根据报纸名称的字数和笔画数进行排架排序．而对于少数特殊的字数笔画数完全相同的中文报纸，则灵活运用邮发代号进行辅助排序。