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关键词： 大数据   信息时代   互联网技术   计算机科学  

摘要： 随着我国科技水平的不断提升,互联网技术被广泛应用在多个行业中,互联网技术的应用给人们生活及工作带来便利的同时,也暴露出一些信息安全方面的问题。因此,在日常生活中应用互联网技术时一定要利用好这把双刃剑。文章深入地分析了面向大数据信息时代计算机科学的具体应用情况。

关键词： Harmonic analysis   Hafnium   Symbols   Search problems   Measurement   Indexes   Computer science   Group closeness centrality   pruning   prioritization   approximation  

摘要： Given a graph and an integer k, the goal of group closeness maximization is to find, among all possible sets of k vertices (called seed sets), a set that has the highest group closeness centrality. Existing techniques for this NP-hard problem strive to quickly find a seed set with a high, but not necessarily the highest centrality. We propose PRESTO, a new solution that can efficiently provide both approximate and exact answers to the group closeness maximization problem. PRESTO continuously calculates the centrality of different seed sets until a time limit is reached or it identifies a seed set with the highest possible centrality. It prioritizes seed sets to quickly find ones that are highly central and thus can be used as accurate approximate answers to the problem. Furthermore, PRESTO can proactively discard large groups of seed sets that cannot have the highest centrality, thereby drastically speeding up the discovery of approximate and exact answers. In our evaluations, compared to other state-of-the-art solutions, PRESTO finds seed sets that have up to 39% higher centrality.

关键词： computer   simplicity   Turing  

摘要： *** We have defined the Conscious Turing Machine(CTM)for the purpose of investigating a theoretical computer science(TCS)approach to consciousness[1].For this,we have hewn to the TCS demand for simplicity and *** CTM is consequently and intentionally a simple *** is not a model of the brain,though its design has greatly benefited—and continues to benefit—from neuroscience and psychology.

摘要： iTOL is a powerful and comprehensive phylogenetic tree visualization engine. However, adjusting to new templates can be time-consuming, especially when many templates are available. We developed an R package namely *** to help users generate all 23 types of annotation files in iTOL. This R package also provides an all-in-one data structure to store data and themes, accelerating the step from metadata to annotation files of iTOL visualizations through automatic workflows.

关键词： 健康教育互动模式   子宫脱垂   盆底重建术   心理状态   生活质量  

摘要： 目的:探讨健康教育互动模式干预,对子宫脱垂盆底重建患者心理状态、生活质量的影响.方法:选取2020年1月至2022年1月本院收治的96例子宫脱垂患者作为研究对象.根据护理干预方案不同将其分为观察组(n=51,健康教育互动模式干预)与对照组(n=45,常规护理干预).比较两组患者干预前后心理状态和生活质量.结果:两组干预后抑郁自评量表(SDS)及焦虑自评量表(SAS)评分均较干预前下降,观察组SAS、SDS评分低于对照组,差异有统计学意义(P<0.05);两组干预后健康感觉、功能恢复、生活满足感、认知功能、心理状态评分均较干预前上升,且观察组上述评分高于对照组,差异有统计学意义(P<0.05).结论:健康教育互动模式干预可明显改善子宫脱垂患者心理状态和生活质量.

关键词： 大学生学习内驱力   教学管理改革   计算机学院  

摘要： 本研究以计算机学院为例,探究了大学生学习内驱力和教学管理改革的关系,并提出了相应的教学管理改革策略。研究表明大学生学习内驱力与教学管理改革之间具有密切的关联性,可以通过实施科学合理的教学管理改革来提升大学生学习内驱力,促进大学生的全面发展。

关键词： 技能等级   团体标准   规范性引用文件   切纸机   职业道德   评价规范   术语和定义   EIA  

摘要： ICS03.100.30CCS A18 T/PEIAC中国印刷及设备器材工业协会团体标准T/PEIAC 016—2022发布2022-05-312022-06-15实施IC目次前言引言1范围2规范性引用文件3术语和定义4基本要求5晋级培训要求6评价要求7申报要求8等级设置和各等级工作要求9晋级考试权重表附录A(规范性)职业道德基本要求附录B(规范性)职业技能等级申报条件.

关键词： Feature extraction   Task analysis   Correlation   Electronic mail   Training   Dinosaurs   Computer science   Common and label-specific features   feature selection   hierarchical classification   label distribution learning   label enhancement  

摘要： Hierarchical classification learning, which organizes data categories into a hierarchical structure, is an effective approach for large-scale classification tasks. The high dimensionality of data feature space, represented in hierarchical class structures, is one of the main research challenges. In addition, the class hierarchy often introduces imbalanced class distributions and causes overfitting. In this paper, we propose a feature selection method based on label distribution learning to address the above challenges. The crux is to alleviate the class imbalance problem and learn a discriminative feature subset for hierarchical classification process. Due to correlation between different class categories in the hierarchical tree structure, sibling categories can provide additional supervisory information for each learning sub tasks, which, in turn, alleviates the problem of under-sampling of minority categories. Therefore, we transform hierarchical labels to a hierarchical label distribution to represent this correlation. After that, a discriminative feature subset is selected recursively, by the common features and label-specific feature constraints, to ensure that downstream classification tasks can achieve the best performance. Experiments and comparisons, using seven well-established feature selection algorithms on six real data sets with different degrees of imbalance, demonstrate the superiority of the proposed method.