教育知识图谱构建中的标准词汇参考模型研究

教育是典型的知识密集型领域,因此,进行领域知识的共享研究对教育领域的发展非常重要。多年信息化的历史经验告诫我们:共享的前提是必须对领域资源实施标准化。尽管我国教育领域研发了系列教育元数据标准,但是从智慧教育的需求来看,这些标准普遍表现为缺乏语义、比较零散、体系不够完善等。随着近些年知识图谱的提出,为教育资源的知识整合发展提供了重要的技术手段。然而,目前知识图谱构建的主要焦点在构建技术方面,忽视了构建中的标准化问题。长此以往,领域将会构建出很多“知识孤岛”。为解决这些问题,本文在对国内外教育领域内标准进行研究的基础上,以系统理论为依据提出并构建了一个集预定义词汇的W3C语义Web标准、顶层词汇标准和领域词汇标准于一体的三层架构模型——教育资源知识图谱标准词汇参考模型。基于该模型建构知识图谱,不仅为教育领域数据的标准化提供标准词汇参考,而且也为规范构建教育领域知识图谱提供了参考。     

一类单圈图的谱刻画

称图是由谱确定的,如果没有非同构的图具有相同的谱．用Cp标记长度为P的圈．设Hp,2标记一个长度为p的圈上的任意一个点邻接两个度为1的点．本文将证明图Hp,2（p是正奇数）由它的邻接谱确定的,并且图Hp,2是由它的拉普拉斯谱确定的．     

湖北宜都方言中的“得得V”

湖北宜都方言中有一种比较常见的表可能的句式"得得V"。对"得得V"的研究不仅涉及"得得"和"V"本身而且还从"得得V"的对称否定式展开多角度阐述分析。     

Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Micro-blogging services such as Twitter allow anyone to publish anything, anytime. Needless to say, many of the available contents can be diminished as babble or spam. However, given the number and diversity of users, some valuable pieces of information should arise from the stream of tweets. Thus, such services can develop into valuable sources of up-to-date information (the so-called real-time web) provided a way to find the most relevant/trustworthy/authoritative users is available. Hence, this makes a highly pertinent question for which graph centrality methods can provide an answer. In this paper the author offers a comprehensive survey of feasible algorithms for ranking users in social networks, he examines their vulnerabilities to linking malpractice in such networks, and suggests an objective criterion against which to compare such algorithms. Additionally, he suggests a first step towards “desensitizing” prestige algorithms against cheating by spammers and other abusive users.     

旗传递（v，k，3）-对称设计的自同构群。与它们的抛物子群

文章研究了（v,k,3）-对称设计D的分类;证明了如果群G是D的几乎单型的自同构群,即存在非交换单群X使得X≤G≤Aut（D）,那么X∩Ga不可能是X的抛物子群．     

A new variant of the Pathfinder algorithm to generate large visual science maps in cubic time

In the last few years, there is an increasing interest to generate visual representations of very large scientific domains. A methodology based on the combined use of ISI–JCR category cocitation and social networks analysis through the use of the Pathfinder algorithm has demonstrated its ability to achieve high quality, schematic visualizations for these kinds of domains. Now, the next step would be to generate these scientograms in an on-line fashion. To do so, there is a need to significantly decrease the run time of the latter pruning technique when working with category cocitation matrices of a large dimension like the ones handled in these large domains (Pathfinder has a time complexity order of O(n⁴), with n being the number of categories in the cocitation matrix, i.e., the number of nodes in the network).     

图像检索技术研究进展

本文介绍了图像检索技术发展的各个阶段,重点介绍基于内容的图像检索技术和基于图理论的图像检索技术。     

局部对称共形平坦黎曼流形的伪脐点子流形

本文研究局部对称共形平坦黎曼流形的具有平行非零中曲率向量的焦脐点子流形的截面曲率和数量曲率的拼挤问题。     

Temporal group-aware graph diffusion networks for dynamic link prediction

Dynamic link prediction is a critical task in network research that seeks to predict future network links based on the relative behavior of prior network changes. However, most existing methods overlook mutual interactions between neighbors and long-distance interactions and lack the interpretability of the model’s predictions. To tackle the above issues, in this paper, we propose a temporal group-aware graph diffusion network(TGGDN). First, we construct a group affinity matrix to describe mutual interactions between neighbors, i.e., group interactions. Then, we merge the group affinity matrix into the graph diffusion to form a group-aware graph diffusion, which simultaneously captures group interactions and long-distance interactions in dynamic networks. Additionally, we present a transformer block that models the temporal information of dynamic networks using self-attention, allowing the TGGDN to pay greater attention to task-related snapshots while also providing interpretability to better understand the network evolutionary patterns. We compare the proposed TGGDN with state-of-the-art methods on five different sizes of real-world datasets ranging from 1k to 20k nodes. Experimental results show that TGGDN achieves an average improvement of 8.3% and 3.8% in terms of ACC and AUC on all datasets, respectively, demonstrating the superiority of TGGDN in the dynamic link prediction task.     

图形液晶显示模块在智能仪器上的应用

本文介绍了液晶显示控制器T6963C的工作原理及其与Philips P89C669单片机的接口电路。阐述了以模块化为基础的显示软件的设计,以及利用该图形液晶显示模块显示多道γ能谱仪的实现。