数据驱动语言学习-教学模式的转变

对基于语料库的语言教学模式研究的缺乏是目前国内在教学中使用语料库所面临的困难之一。本文介绍并分析了数据驱动语言学习这一基于语料库的语言教学模式，认为这一模式在教学理念，学习内容，以及学习材料等方面与传统的教学模式存在很大的区别。应用在语法学习、词汇搭配、比较同义词和近义词、纠正语言失误、检查翻译译法等方面具有积极的意义。     

数据驱动的航空装备协同分析与智能保障决策方法

现代化战争模式对装备保障的响应性、精准性、可视性及自协同提出了更高的要求,而传统被动保障方式具有信息不精准、协同能力差、保障响应不及时等问题,迫切需要利用当前先进信息物理融合技术,实现“精准”、“敏捷”的主动式保障。针对主动式装备保障管理的需求,以及航空装备建立部件/系统数学模型困难等问题,提出一种状态协同分析与智能保障决策方法。该方法通过故障测试用例离线和在线的执行,产生测试数据；基于传感器的在线状态监测和历史数据,对装备状态进行估计和判断,从而提供智能保障决策服务。选取飞机燃油系统为对象,验证了提出方法的有效性,为主动式装备保障系统的研制奠定技术基础。     

Understanding climate phenomena with data-driven models

In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple climate models are characterized by a larger graspability, state-of-the-art models have a higher representational accuracy and representational depth. We then compare the fitness-for-providing understanding of process-based to data-driven models that are built with machine learning. We show that at first glance, data-driven models seem either unnecessary or inadequate for understanding. However, a case study from atmospheric research demonstrates that this is a false dilemma. Data-driven models can be useful tools for understanding, specifically for phenomena for which scientists can argue from the coherence of the models with background knowledge to their representational accuracy and for which the model complexity can be reduced such that they are graspable to a satisfactory extent.     

设计研究中的数据驱动和艺术导向

从中国产业调整和工业设计发展的角度,来研究现代设计方法及理念在中国设计发展中的历史和现状。首先论述在中国产业升级和调整的背景下数据驱动型设计的方法及思潮,进而对比分析传统设计方法与数据驱动型设计的学科结构、流程,以及与其他相关学科的关系等。在此基础上,试图探索中国设计的走向和设计教育的取向,并提出前瞻性的建议。     

数据驱动下的高校网络运维

基于数据驱动网络运维的理念，从数据治理的角度出发，建立网络运维数据中台来提供可靠的数据分析依据，实现业务数据化、数据资产化、资产价值化，最终达到网络运维的智能化和自动化，为高校的教学和科研活动提供稳定、安全的网络环境。