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关键词： 剩余寿命预测   自注意力机制   卷积神经网络   特征融合   随机过程   涡扇发动机  

摘要： 在工业大数据和万物互联背景下,针对复杂运行条件中的设备健康状态需从多维度进行表征,如何有效地融合多维监测数据,对随机退化设备的精准剩余寿命预测具有重要意义,由此提出一种基于深度特征融合网络的数模联动随机退化设备剩余寿命预测方法,构建多维自注意力时间卷积网络对时间窗处理后的多维监测数据做深度特征提取,设计模式加权的特征融合方法获取融合退化指标,然后采用随机过程对退化指标进行建模,通过表征预测效果的优化目标函数对网络参数、模式系数和失效阈值进行反向调整,形成数模联动的剩余寿命预测方法,实现退化指标和随机模型的自动匹配。最后,在涡扇发动机运行数据集上验证了该方法的准确性和优越性。

关键词： 人机翻译   题名翻译   《中央民族大学学报》   民族期刊  

摘要： 优质的题名翻译有助于论文被同行或检索机构正确查找、分类、标引及收录。以2021—2022年《中央民族大学学报(社会科学版)》发表的212篇民族论文为例,分析民族期刊论文题名的英译现状,并按照国外期刊收录国内期刊时对英文题名的修改原则,改正了错误,建立了记忆库。通过中国网、中国日报网等找到民族研究双语材料,建立中英平行术语库。通过计算机辅助工具MemoQ翻译2022年《中央民族大学学报》17篇论文题名,以测试计算机辅助翻译的效率和正确率。研究发现,《中央民族大学学报》英文题名总体质量高,但也普遍存在滥用“Study on”“Research on”以及冠词等现象。与传统的人工翻译或单纯的机器翻译相比,如果译者具有较高的英语水平,了解民族专业知识,借助计算机辅助翻译工具,就能优质高效地做好民族论文题名翻译工作。

关键词： Computer science education   Holistic learning   Motivation  

摘要： With the increasing integration of computer science into school curricula, a growing number of pupils are coming into contact with this subject. To get as many pupils as possible interested in computer science, the teaching strategies and methods must meet the heterogeneous prerequisites of the pupils. The purpose of this paper is to find out how to identify two different student behavioral preferences mentioned in the computer science education literature. Considering these preferences, the focus was set on the preferences for socially enriched learning on the one hand and task-oriented learning on the other hand. Even though related research has been found on the preferences of preschoolers and adults, research is missing in the field of computer science education at the secondary school level. Thus, this study focuses on the learning preferences of pupils in secondary school education regarding the two behavioral preferences. As a result, an instrument was developed and piloted that, validated by qualitative methods, measures these different preferences. Preliminary exploratory results from analyses with school classes show that preference clusters can be found in secondary education. Further research aims to use these findings to improve computer science education. © The Author(s) 2022.

关键词： 安宁疗护   临终问题   互动   互动支持者  

摘要： 通过在某三级医院安宁疗护科为期5个月的田野研究,分析临终病人与家属的临终互动过程,阐明互动支持者在促进其临终互动过程中的作用。期间共收集21例研究对象的20份家庭会议录音、79张照片、21份个案笔记,梳理出7个临终互动情境。研究显示,互动支持者发挥的作用有互动促发激发互动意愿、互动支持增加互动勇气和互动保障促进互动进行。由此得出结论,互动支持者作为病人与家属的互动桥梁,通过互动促发、互动支持和互动保障,翻译其行为背后的情感与期待、转述情感的表达,促进有效陪伴和及时表达爱,使病人善终、家属善别。

关键词： 科学建模学习   科学教育   计算思维   科学思维   核心素养   跨学科   实践创新  

摘要： 人工智能时代的科学创新人才,需要具备用计算思维解决科学问题的能力。而计算机支持的科学建模学习,可成为科学教育中融入计算思维培养的有效途径,是学生基于计算机建模软件或系统通过模型构建、模型分析、模型修正和模型运用等建模实践活动,来解决科学问题的一种学习方法。这种学习对科学思维和计算思维协同发展的促进机制是怎样的?不同学段的相关教学具体该如何实施?其学习模式是怎样的?为探究这些问题,亟待在梳理科学建模学习理论和研究现状的基础上,论证计算机支持的科学建模学习促进科学思维和计算思维协同发展的内在机制,并构建科学建模学习模式。研究表明:这种学习实践不仅可以培养学生的分析与综合、抽象与概括、归纳与演绎等科学思维,而且蕴含着问题表征、抽象、分解、仿真、数据处理、验证和预测等计算思维技能,进而形成以“创设情境—结构化任务—建模实践—反思总结”为实施环节的科学建模学习模式。而大学生材料科学建模、中学生牛顿第二定律中“力和运动”科学建模、小学生生态系统科学建模的多学段案例分析亦表明:在该学习模式下实施不同学段教学,可促进学生科学思维和计算思维的协同发展。可见,计算机支持的科学建模学习与科学思维和计算思维是相互促进、相辅相成的;该学习模式可以培养“智能+”时代所需的“计算机+”学科的跨学科创新型人才。

关键词： Knowledge graph   text mining   knowledge unit   graph mining  

摘要： The importance of prerequisites for education has recently become a promising research *** work proposes a statistical model for measuring dependencies in learning resources between knowledge *** are expected to present knowledge units in a semantically well-organized manner to facilitate students’understanding of the *** proposed model reveals how inner concepts of a knowledge unit are dependent on each other and on concepts not in the knowledge *** help understand the complexity of the inner concepts themselves,WordNet is included as an external knowledge base in *** goal is to develop a model that will enable instructors to evaluate whether or not a learning regime has hidden relationships which might hinder students’ability to understand the *** evaluation,employing three textbooks,shows that the proposed model succeeds in discovering hidden relationships among knowledge units in learning resources and in exposing the knowledge gaps in some knowledge units.

关键词： Computer Science   College Education   University Social Responsibility  

摘要： At the end of the keynotes during the second Global Forum of Development of Computer Science,a panel discussion was held to encourage further discussion on ways for universities to adapt to the rapidly changing computer science *** deans of top computer science departments participated,including the *** discussions were guided along three topics,namely the role of computer science departments in universities today,the nature of computer science as a fundamental discipline or an applied one,and computer science *** of these topics,the panelists mainly focused on the interdisciplinary nature of computer science in teaching,research,and *** panelists agreed about ways to prepare for the interdisciplinary future,for example by establishing new research centers,introducing projectbased curricula,and collaborating with industry while keeping the campus *** also pointed out that universities may be under equipped for preparing future professionals to keep up with rapid new advances,especially in machine learning and artificial intelligence.

关键词： Psychology   Depression   Risk factors   Computer science  

摘要： We study the language differentially associated with loneliness and depression using 3.4-million Facebook posts from 2986 individuals, and uncover the statistical associations of survey-based depression and loneliness with both dictionary-based (Linguistic Inquiry Word Count 2015) and open-vocabulary linguistic features (words, phrases, and topics). Loneliness and depression were found to have highly overlapping language profiles, including sickness, pain, and negative emotions as (cross-sectional) risk factors, and social relationships and activities as protective factors. Compared to depression, the language associated with loneliness reflects a stronger cognitive focus, including more references to cognitive processes (i.e., differentiation and tentative language, thoughts, and the observation of irregularities), and cognitive activities like reading and writing. As might be expected, less lonely users were more likely to reference social relationships (e.g., friends and family, romantic relationships), and use first-person plural pronouns. Our findings suggest that the mechanisms of loneliness include self-oriented cognitive activities (i.e., reading) and an overattention to the interpretation of information in the environment. These data-driven ecological findings suggest interventions for loneliness that target maladaptive social cognitions (e.g., through reframing the perception of social environments), strengthen social relationships, and treat other affective distress (i.e., depression). © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2022.

关键词： 大数据   计算机科学   教学方案  

摘要： 阐述大数据与计算机科学技术的教学方案,包括教学内容的改革、实践教学改革、教学方式的实践,通过翻转平台,学生可以对自己的项目作业进行系统化的展示。

关键词： Optimization   Recommender systems   Collaboration   Time complexity   Computer science   Task analysis   Stochastic processes   One-class collaborative filtering   implicit regularization   item recommendation   coordinate descent   alternating least square  

摘要： One-class collaborative filtering (OCCF) problems are ubiquitous in real-world recommendation systems, such as news recommendation, but suffer from data sparsity and lack of negative items. To address the challenge, the state-of-the-art algorithm assigns uninteracted items with smaller weights of being negative and performs low-rank approximation over the user-item interaction matrix. However, the prior ratings are usually suggested to be zero but may not be well-defined. To avert the direct utilization of prior ratings for uninteracted items, we propose a novel ranking-based implicit regularizer by hypothesizing that users' preference scores for uninteracted items should not deviate a lot from each other. The regularizer is then used in a ranking-based OCCF framework to penalize large differences of preference scores between uninteracted items. To efficiently optimize model parameters in this framework, we develop the scalable alternating least square algorithm and coordinate descent algorithm, whose time complexity is linearly proportional to the data size. Finally, we extensively evaluate the proposed algorithms on six public real-world datasets. The results show that the proposed regularizer significantly improves the recommendation quality of ranking-based OCCF algorithms, such as BPRMF and RankALS. Moreover, the ranking-based framework with the proposed regularizer outperforms the state-of-the-art recommendation algorithms for implicit feedback.