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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.

关键词： Cloud computing   Cryptography   Sensors   Internet of Things   Data models   Costs   Computer science   Brute-force attacks   fog-cloud network   frequency analysis   message equality information   similar data deduplication  

摘要： Distributed fog computing has received wide attention recently. It enables distributed computing and data management on the network nodes within the close vicinity of IoT devices. An important service of fog-cloud based systems is data deduplication. With the increasing concern of privacy, some privacy-preserving data deduplication schemes have been proposed. However, they cannot support lossless deduplication of encrypted similar data in the fog-cloud network. Meanwhile, no existing design can protect message equality information while resisting brute-force and frequency analysis attacks. In this paper, we propose a privacy-preserving and compression-based data deduplication system under the fog-cloud network, which supports lossless deduplication of similar data in the encrypted domain. Specifically, we first use the generalized deduplication technique and cryptographic primitives to implement secure deduplication over similar data. Then, we devise a two-level deduplication protocol that can perform secure and efficient deduplication at distributed fog nodes and the cloud. The proposed system can not only resist brute-force and frequency analysis attacks but also ensure that only the data operator can capture the message equality information. We formally analyze the security of our design. Performance evaluations demonstrate that our proposed design is efficient in computing, storage, and communication.

关键词： Metadata  

摘要： The number of scientific publications continues to rise exponentially, especially in Computer Science (CS). However, our ability to analyze those publications does not follow the same speed, which prevents us from finding and understanding implicit patterns hidden in their metadata (e.g., venues, document types). Current solutions are limited by restricting access behind a paywall, offering no features for visual analysis, limiting access to their data, only focusing on niches or sub-fields, and/or not being flexible and modular enough to be transferred to other datasets. In this thesis, we conduct a scientometric analysis to uncover those implicit patterns hidden in CS metadata and to determine the state of CS research. Specifically, we investigate trends of the quantity, impact, and topics for authors, venues, document types (conferences vs. journals), and fields of study (compared to, e.g., medicine). To achieve this we introduce the Computer Science Insights (CS-Insights) system, an interactive web application to analyze CS publications through multiple perspectives. The data underlying this system is the DBLP Discovery Dataset (D3), which contains metadata from 5 million scholarly publications in CS. We create D3 with data from DBLP, the largest open-access bibliography for scientific papers and articles in CS, and enrich it with further metadata (e.g., abstracts, citations). CS-Insights offers dedicated dashboards with multiple visualizations for all main features of D3 (e.g., publications, authors, venues, and citations) and multiple filters for more fine-grained analysis. Both D3 and CS-Insights are open-access, and CS-Insights can be easily adapted to other datasets in the future. The most interesting findings of our scientometric analysis include that i) there has been a stark increase in publications, authors, and venues in the last two decades, ii) many authors only recently joined the field, iii) the most cited authors and venues focus on computer visio

关键词： Bibliometrics   Education   Databases   Computer science   Software   Productivity   Economics   general literary works   journal impact study   learning technologies  

摘要： IEEE Transactions on Learning Technologies (IEEE-TLT) is a leading journal in the fields of computer science and educational research with a focus on learning technologies. It published its first issue in 2008 and commemorated its 15th anniversary in 2022. Inspired by this event, this article provides a general lifetime overview of the journal using bibliometric indicators and science mapping analysis. The main objective is to provide a complete overview of the main components that have affected the journal. This analysis includes key factors such as the most cited articles and the leading authors, institutions, and countries for the journal, along with an insight into the publication and the citation structure. We use the Web of Science Core Collection database to analyze the bibliometric data and VOSviewer software to graphically map the bibliographic material using a bibliographic coupling, cocitation, and co-occurrence of author keywords. With this analysis, we gain a deeper understanding of how IEEE-TLT is connected to other journals and researchers across the globe and how it contributes to scientific communities. Results indicate that IEEE-TLT is a high-impact journal in computer Science and education and has been referenced by a wide range of authors, institutions, countries, and the main topics related to learning technologies from all over the world.