C++程序设计教学中的技能培养

本文阐述了知识与技能的关系,以及C++程序设计教学中知识和技能的双重性要求。以选择语句为例,演示了"先知识后技能"的教学过程。实践证明,这样的教学安排行之有效。     

基于知识图谱的互补项目推荐

在缺乏用户交互互补项目方面数据的情况下,将用户对项目的偏好融合到只考虑项目关系的互补项目推荐中,提高推荐模型的性能。提出一种基于知识图谱的互补项目推荐方法,在用户历史交互项目集中推测用户交互的互补项目,基于知识图谱提取用户对互补项目的偏好,利用图像与文本学习项目之间的互补关系,最后基于神经网络实现二者的共同学习。提出的方法在Amazon数据集上与次优的基线方法相比,ACC提升了7%,precision提升了3%,这说明提出的方法性能优异。该算法共同学习用户对项目的偏好与项目之间的互补关系,提升了推荐性能。     

DETECTOR: A knowledge-based system for injection molding diagnostics

A knowledge-based system (KBS) for diagnosis of multiple defects in injection molding is presented. The general scheme for knowledge representation based on fuzzy set theory has been shown useful in representing inexact and incomplete information for developing the KBS. An optimality criterion is created for selecting a simple and best cover to explain the given problem. An efficient search algorithm for finding such cover is also discussed.     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

Wide gamut LCD using locally dimmable four‐primary‐color LED backlight

This paper proposes a wide gamut LCD using locally dimmable four‐primary‐color (4PC) LED backlight. Although the color gamut of LCDs has been improved in recent years, it is insufficient to reproduce all the colors in the real world. The objective of this paper is to propose a wide gamut LCD that reproduces all the colors in the real world while keeping the cost increases to a minimum. We evaluated the color gamut reproduced by LEDs of multiple primary colors and selected cyan as the optimal color to be added to the three primary colors to reproduce all the colors in the real world. Therefore, we designed an LED backlight consisting of an additional only‐cyan LED with three‐primary‐color LEDs and developed a prototype LCD with 4PC LED backlight. Furthermore, we developed a local dimming algorithm for the 4PC LED backlight. As a result, we confirmed that the prototype LCD with the 4PC LED backlight is able to cover almost all the colors in the real world and also able to display natural images with highly saturated colors by local dimming.     

Efficient design of SIW filters with knowledge‐embedded space mapping technique

This article presents an optimization technique for the design of substrate‐integrated waveguide (SIW) filters using knowledge‐embedded space mapping. An effective coarse model is proposed to represent the SIW filter. The proposed coarse model can be analyzed in the available commercial software ADS. The embedded knowledge includes not only formulas but also extracted design curves, which help to build the mapping between the coarse and fine models. The effectiveness of the proposed method is demonstrated through a design example of a six‐pole SIW filter. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

Taking advantage of collective knowledge in emergency response systems

When large groups work on a theme, they have the potential to produce a lot of useful knowledge, regardless of whether they are acting in a coordinated manner or individually. Spontaneously generated information has received much attention in recent years, as organizations and businesses discover the power of crowds. New technologies, such as blogs, Twitter, wikis, photo sharing, collaborative tagging and social networking sites, enable the creation and dissemination of content in a relatively simple way. As a result, the aggregate body of knowledge is growing at an accelerated rate. Many organizations are looking for ways to harness this power, which is being called collective intelligence. Research has shown that it is possible to obtain high quality results from collectively produced work.In this paper, we consider the domain of emergency response. Research has shown that individuals respond quickly and massively to emergencies, and that they try to help with the situation. Thus, it seems like a logical step to attempt to harness collective knowledge for emergency management. Disaster relief groups and field command frequently suffer from lack of up to date information, which may be critical in a rapidly evolving situation. Some of this information could be generated by the crowd at large, enabling more effective response to the situation. In this paper, we discuss the possibilities for the introduction of collective knowledge in disaster relief and present architecture and examples of how this could be accomplished.     

对大学计算机基础中硬件部分教学方法的探讨

大学计算机基础是大中专院校的非计算机专业学生必修的一门公共基础课。这门课程主要讲述计算机的基础知识,基本操作;学生对其中计算机的硬件知识很感兴趣,但由于教学条件的限制,难以在课堂上用实物进行讲解和组装演练,因此我们要广开思路寻找适合当前教学境况的创新教学方法。     

基于图的语义网络构造算法研究

在自然语言处理的基础上,将中医领域本体与图的基本性质相结合,提出了构造中医诊疗知识语义网络算法。给出了图的基本性质、语义网络的图定义,并为知识的网络表示建立了相应的数据结构。将算法用于中医领域语义网络和语义场构建。试验结果表明,该算法具有较好的实用性。提出的基于图的语义网络构造算法,对于文本挖掘、知识获取技术研究有重要意义,也是中医专家知识获取的有效方法。