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关键词： Students  

摘要： Collaboration skills are important for future software engineers. In computer science education, these skills are often practiced through group assignments, where students develop software collaboratively. The approach that students take in these assignments varies widely, but often involves a division of labour. It can then be argued whether collaboration still takes place. The discipline of computing education is especially interesting in this context, because some of its specific features (such as the variation in entry skill level and the use of source code repositories as collaboration platforms) are likely to influence the approach taken within groupwork. The aim of this research is to gain insight into the work division and allocation strategies applied by computer science students during group assignments. To this end, we interviewed twenty students of four universities. The thematic analysis shows that students tend to divide up the workload to enable working independently, with pair programming and code reviews being often employed. Motivated primarily by grade and efficiency factors, students choose and allocate tasks primarily based on their prior expertise and preferences. Based on our findings, we argue that the setup of group assignments can limit students' motivation for practicing new software engineering skills, and that interventions are needed towards encouraging experimentation and learning. Copyright © 2021, The Authors. All rights reserved.

关键词： Cloud computing scientific literature   cloud related concepts   CSO ontology  

摘要： Our primary research hypothesis stands on a simple idea:The evolution of top-rated publications on a particular theme depends heavily on the progress and maturity of related *** this even when there are no clear relations or some concepts appear to cease to exist and leave place for newer ones starting many years *** implemented our model based on Computer Science Ontology(CSO)and analyzed 44 years of *** we derived the most important concepts related to Cloud Computing(CC)from the scientific collection offered by Clarivate *** methodology includes data extraction using advanced web crawling techniques,data preparation,statistical data analysis,and graphical *** obtained related concepts after aggregating the scores using the Jaccard coefficient and CSO *** article reveals the contribution of Cloud Computing topics in research papers in leading scientific journals and the relationships between the field of Cloud Computing and the interdependent subdivisions identified in the broader framework of Computer Science.

关键词： 计算思维   混合式教学   Raptor   思维导图  

摘要： 计算思维能力是计算机类专业学生必须掌握的能力,是进入计算机类专业大门的钥匙。《计算机导论》是高校计算机类专业新生入学后的第一门专业导引性课程,也是培养计算思维能力的前沿阵地。目前的导论课程内容庞杂,并且学时有限,如何有效利用这门课培养同学们的计算思维能力,为后续的专业课学习做好准备,是《计算机导论》课程的重中之重,本文阐述了结合线上线下混合式教学模式和有效的思维训练工具Raptor对导论课程进行改革实践的研究,旨在培养学生分析和解决问题的能力。

关键词： 地方高校   培养质量   研究生  

摘要： 随着科学技术的不断发展,计算机专业研究生的招生规模越来越大,招生人数迅速增加。在这种大规模、高速发展的情况下,研究生教育的质量成为引人关注的问题。特别是地方高校在科研平台、师资等方面难以获得先进和充足的资源,在研究生培养过程中面临诸多困难。该文对地方高校研究生招生、研究生自身及研究生培养方式等的现状进行分析,探讨计算机专业研究生培养过程中存在的问题及对策。

关键词： 八年级   教学设计  

摘要： 1.教学目标 1.1语言知识目标。 (1)能掌握以下单词:grow up,computer programmer,cook,doctor,engineer,violinist,driver,pilot,pianist,scientist, 能掌握以下句型: ─What do you want to be when you grow up? ─I want to be a basketball player. ─How are you going to do that?

关键词： 计算机科学与技术   专业思政   课程思政   三全育人  

摘要： 根据《关于加强和改进新形势下高校思想政治工作的意见》中提出的全员全过程全方位育人的“三全育人”要求,北京联合大学智慧城市学院计算机科学与技术专业开展了“专业思政”和“课程思政”建设的探索与实践。通过分析专业特点、专业核心价值及“专业思政”的载体,建立“专业思政”体系。在培养方案设计中融入思政元素,在课程大纲中明确思政目标要求,将“课程思政”作为“专业思政”的基础,以学生实践活动促进“专业思政”建设。计算机科学与技术专业的“专业思政”取得了初步成效,实现了思政建设与专业建设的有机结合,形成了有“三全育人”特色的专业人才培养模式。

关键词： Bridges   Monitoring   Computer science   Sensors   Learning automata   Internet of Things   Civil engineering   Bridge structural health monitoring (BSHM)   confident information coverage (CIC) model   Internet of Things (IoT)   learning automata (LA)   partial coverage   reinforcement learning  

摘要： Internet-of-Things (IoT)-based bridge structural health monitoring (BSHM) has recently attracted considerable attention from both academic and industrial communities of civil engineering and computer science. In conjunction with researchers from civil engineering and computer science, this article studied a fundamental problem motivated from practical IoT-based BSHM: how to effectively prolong network lifetime while guaranteeing desired coverage. Integrating a promising reinforcement learning model named learning automata (LA) with confident information coverage (CIC) model, this article presented an energy-efficient sensor scheduling strategy for partial CIC coverage in IoT-based BSHM system to guarantee network coverage and prolong network lifetime. The proposed scheme fully exploits cooperation among deployed nodes and alternatively schedules the wake/sleep status of nodes while satisfying network connectivity and partial coverage ratio. Especially, the proposed scheme takes full advantage of the LA model to adaptively learn the optimal sensor scheduling strategy and significantly extend network lifetime. A series of comparison simulations using real data sets collected by a practical BSHM system strongly verify the effectiveness and energy efficiency of the proposed algorithm. To the best of our knowledge, this is the first study on how to combine the reinforcement learning mechanism with partial coverage for maximizing the network lifetime of the IoT-based BSHM.

关键词： Machine learning   Internet of Things   Biological neural networks   Security   Neurons   Object recognition   Computer science   AI   deep learning   Internet of Things (IoT)   network alignment   network security  

摘要： With the advent of the era of the Internet of Things (IoT), a large number of interconnected smart devices form a huge network. The network can be abstracted as a graph, and we propose to identify similar IoT devices in different networks by graph alignment. However, most methods rely on prelabeled cross-network node pairs such as anchor links, which are difficult to obtain due to personal privacy and security restrictions, especially in IoT. In addition, existing network entity alignment methods focus on individual pairs of nodes but ignore the tightly connected group structure in the network, which is a significant feature of IoT devices. In this article, we propose a method of hierarchical unsupervised network alignment (HUNA) to identify similar IoT devices in different networks by a deep learning approach. First, we propose an unsupervised network alignment method based on cycle adversarial networks (UNA), which utilizes the adversarial characteristics of cycle adversarial networks to achieve entity alignment under unsupervised conditions. Second, we further expand the model by carefully designing the group structure aggregation optimization module to aggregate the nodes with closely related attributes and structures into a coarse-grained node and align the coarse-grained nodes. Finally, we evaluate HUNA with real and synthetic data sets. Experimental results show that this method can improve the accuracy of node alignment by 10% and perform well in terms of parameter sensitivity.

关键词： 计算机   材料科学   教学内容   教学方法  

摘要： 《计算机在材料科学中的应用》是一门材料科学相关专业的专业基础课,具有很强的跨学科性和时代技术特征,理论性和实践性都很强。本文简要回顾了该课程在我校的发展历程、分析了其开设现状,并对其教学方法、教学手段等提出了看法和建议。

关键词： Performance evaluation   Three-dimensional displays   Time factors   Computer science   Nonvolatile memory   Memory management   Hardware   Flash memory   block retention time   data lifetime   data refreshing  

摘要： With the emerge of high-density triple-level-cell (TLC) and 3D NAND flash, the access performance and endurance of flash devices are degraded due to the downscaling of flash cells. In addition, we observe that the mismatch between data lifetime requirement and flash block retention capability could further worsen the access performance and endurance. This is because the "lifetime-retention mismatch" could result in massive internal data migrations during garbage collection and data refreshing, and further aggravate the already-worsened access performance and endurance of high-density NAND flash devices. Such an observation motivates us to resolve the lifetime-retention mismatch problem by proposing a "time harmonization strategy", which coordinates the flash block retention capability with the data lifetime requirement to enhance the performance of flash devices with very limited endurance degradation. Specifically, this study aims to lower the amount of internal data migrations caused by garbage collection and data refreshing via storing data of different lifetime requirement in flash blocks with suitable retention capability. The trace-driven evaluation results reveal that the proposed design can effectively reduce the average response time by about 99 percent on average without sacrificing the overall endurance, as compared with the state-of-the-art designs.