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关键词： 计算机科学与技术   现代化   创新化运用  

摘要： 在新时期,社会转型发展的进程不断加快,对计算机科学与技术的应用提出了更高的要求。为全方位发挥计算机科学与技术的功能和作用,为社会生产提供服务,需要结合时代需求,系统化地运用计算机科学与技术,不断创新运用模式,强化安全管理力度,进而使其更好地为人们的生活及社会生产提供服务,助推现代化、创新化发展目标的顺利实现。文中从计算机科学与技术的现代化应用出发,分析了其现代化应用的策略。

关键词： 计算机科学与技术   高职教育教学   信息化建设  

摘要： 随着信息时代的到来,社会对计算机科学与技术人才的需求量也逐渐提升。目前,很多高职院校开设了计算机科学与技术专业,但在实际教学中,其存在教学模式陈旧、教学效果较差等问题。鉴于此,文中探讨了在高职院校计算机与信息技术专业的课程教学中,如何优化教学模式、课程结构,实现对学生职业技能的培养,提升学生的实践操作应用能力。

关键词： 作业车间调度   人员调度   学习效应   相似性   多目标最优化  

摘要： 考虑作业相似性和人员学习效应的影响,建立了一人多机生产模式下多目标作业车间的调度模型,并设计了基于网格筛选外部档案的遗传算法(GFEAGA)对其进行求解。采用两阶段的编解码方式和改进的N6邻域搜索方法以提高求解效率。为平衡人员负荷,设计了人员的选派方法。基于网格排序筛选非支配个体,以提高解集的多样性。实验验证了GFEAGA求解的高效性和优越性,分析了模型中相似性和学习率的灵敏性。

关键词： 计算机   应用型人才   就业能力   市场需求  

摘要： 近年来,市场需求与高校毕业生就业能力之间始终存在严重的“技能差距”,毕业生的能力素质与市场需求之间存在显著的不匹配性。文章以计算机专业应用型人才为研究范本,从毕业生就业能力评估与供需匹配、人才需求与培养目标两方面进行论述,提出我院计算机专业应用型人才就业能力提升的有效路径。

关键词： Trajectory   Internet of Vehicles   Quality of experience   Predictive models   Costs   Computer science   Vehicle dynamics   Edge caching   Internet of Vehicles (IoV)   mobility-aware scheme   multimedia services   proactive caching  

摘要： By shifting the requested content to the edge in the Internet of Vehicles (IoV), edge caching is expected to be an effective solution to satisfy the low latency and high-reliability requirements of IoV users for multimedia services. However, the edge node's coverage area and storage space are limited. Moreover, since vehicles have high mobility and in-vehicle multimedia applications require sequential delivery for contents, we need to address two main issues: 1) how to optimize the proactive content caching decision (i.e., the placement of cached content chunks) among edge nodes (ENs) to provide better Quality of Services (QoS) for IoV users and 2) how to ensure that vehicles can download the required contents sequentially to improve Quality of Experience (QoE). In this article, we propose a mobility-aware proactive edge caching scheme (MSTPS), where the spatial and temporal prediction of vehicles are taken into account for content deployment and scheduling. Specifically, we optimize the caching decision based on predicting the vehicle's driving trajectory and travel preference. The scheme learns the vehicle's travel preferences to cope with mobility uncertainty by combining users with similar travel patterns. Meanwhile, the proposed scheme can support the sequential downloading of content chunks. Furthermore, in order to deal with the dynamic characteristics and unpredictable challenges of the IoV, we design a system recovery strategy, which can avoid the degradation of the proposed scheme due to the failure of prediction. Finally, by using real mobility data sets and scenarios, we explore the impact of the number of ENs deployed in advance for each vehicle's request when the cache needs to be updated on system performance. In addition, we evaluate the effectiveness of the proposed scheme. Our proposed scheme can achieve the best cache hit ratio and decrease caching costs compared to the existing mobility-aware in-order caching schemes.

关键词： Legged locomotion   Task analysis   Computer science   Tracking   Visualization   Head-mounted displays   User experience   Rotation gains   amplified head rotation   head-mounted displays  

摘要： Head tracking in head-mounted displays (HMDs) enables users to explore a 360-degree virtual scene with free head movements. However, for seated use of HMDs such as users sitting on a chair or a couch, physically turning around 360-degree is not possible. Redirection techniques decouple tracked physical motion and virtual motion, allowing users to explore virtual environments with more flexibility. In seated situations with only head movements available, the difference of stimulus might cause the detection thresholds of rotation gains to differ from that of redirected walking. Therefore we present an experiment with a two-alternative forced-choice (2AFC) design to compare the thresholds for seated and standing situations. Results indicate that users are unable to discriminate rotation gains between 0.89 and 1.28, a smaller range compared to the standing condition. We further treated head amplification as an interaction technique and found that a gain of 2.5, though not a hard threshold, was near the largest gain that users consider applicable. Overall, our work aims to better understand human perception of rotation gains in seated VR and the results provide guidance for future design choices of its applications.

关键词： 方法知识元   知识元库   知识元抽取   知识交流  

摘要： [研究目的]为细粒度发现相近学科间的知识关联,推动学科间知识交流,通过抽取方法知识元,探析不同学科领域研究方法的交流态势。[研究方法]以情报学和计算机科学学为例,首先,编写方法知识元抽取规则,基于规则抽取两学科领域大样本文献的方法知识元,构建学科领域方法知识元库;再利用LDA模型抽取两学科领域代表性期刊的方法知识元;以此为基础,对比分析两学科领域的高频研究方法、已交流的研究方法、存在交流潜力的研究方法。[研究结论]研究发现,两学科已交流的研究方法有:聚类分析、LDA模型、BP神经网络、支持向量机、主成分分析、协同过滤、文献计量等;具有交流潜力的研究方法有:提高准确率的分类算法(AdaBoost算法、XGBoost算法、SMOTE算法)、图卷积神经网络、自然语言处理模型GloVe模型等。该研究不仅能为情报学研究者选择计算机科学学领域的研究方法提供参考,而且也为学科交叉研究提供了一种新思路。

关键词： 计算机科学与技术   师范生   混合式教学能力标准  

摘要： 为迎接新时代背景下基础教育的核心素养导向新阶段和后疫情时代带来的新挑战,提高师范类专业人才培养质量,要从源头上持续提升师范生教书育人的能力水平。文章采用文献研究、问卷调查及专家访谈等科学方法,对计科师范生混合式教学能力进行了深入地探究和分析,并最终形成了一套涵盖混合式教学技能构成、混合式教学理念、多元化考评能力、教育信息化能力和混合式教学设计能力5个方面的计科师范生混合式教学能力标准。

关键词： Representation learning   Task analysis   Matrix decomposition   Decoding   Computer science   Complexity theory   Unsupervised learning   Multi-view clustering   deep clustering   unsupervised learning   self-supervised learning  

摘要： Multi-view clustering is an important research topic due to its capability to utilize complementary information from multiple views. However, there are few methods to consider the negative impact caused by certain views with unclear clustering structures, resulting in poor multi-view clustering performance. To address this drawback, we propose self-supervised discriminative feature learning for deep multi-view clustering (SDMVC). Concretely, deep autoencoders are applied to learn embedded features for each view independently. To leverage the multi-view complementary information, we concatenate all views' embedded features to form the global features, which can overcome the negative impact of some views' unclear clustering structures. In a self-supervised manner, pseudo-labels are obtained to build a unified target distribution to perform multi-view discriminative feature learning. During this process, global discriminative information can be mined to supervise all views to learn more discriminative features, which in turn are used to update the target distribution. Besides, this unified target distribution can make SDMVC learn consistent cluster assignments, which accomplishes the clustering consistency of multiple views while preserving their features' diversity. Experiments on various types of multi-view datasets show that SDMVC outperforms 14 competitors including classic and state-of-the-art methods. The code is available at https://***/SubmissionsIn/SDMVC.