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ISBN: (数字)9787302499442

摘要： 本书是数据结构课程的辅助教材, 采用C和C++两种语言来描述数据结构, 让学生在实验与习题中体会与掌握数据结构, 同时培养编程能力和分析能力。主要内容包括: 实验与习题两大部分, 用于巩固数据结构的理论知识, 提高实践应用能力。

关键词： 翻转课堂   数据结构   线上课堂   实验教学  

摘要： 针对数据结构课程知识点多且独立、概念相对抽象、实验复杂等特点,提出注重学生自主学习,加强师生互动,利于学生长远发展的数据结构课程翻转课堂教学改革,构建系统、全面、易理解的数据结构知识体系,培养学生创新能力和动手能力。教学实践结果表明,该教学模式激发了学生的学习兴趣,提高了学生自主学习能力,学生更容易理解和接受课程内容。

关键词： 高职   数据结构   教学体会  

摘要： 计算机专业是当前比较热门专业,很多高职学生选择填报此专业。但是,传统的教学模式忽略了数据结构在此专业的重要性,严重影响了学生后期的专业发展。如何唤起这门课程的重要性、激发学生对于数据结构的学习兴趣、加强学生的动手能力与完成数据结构课程教学目标是亟待解决的问题。本文从这门课的意义和讲授这门课程时的一些教学体会等方面来强调数据结构在计算机专业中的重要性。

关键词： ACM竞赛模式   数据结构   实践教学  

摘要： 针对数据结构实践教学中存在的一些问题,阐述了在数据结构实践教学中引入ACM竞赛模式的必要性,提出采用开源软件hustoj构建数据结构实践教学平台及基于该平台的实验内容设计、分组实验的策略、定期数据结构实验测试等实践教学改革措施,为同类课程的实践教学改革提供参考。

关键词： 业务类型   不动产   数据整合   权利主体   对应关系   存储方式   数据字段   数据结构  

摘要： 本文从原房产登记类型出发，梳理原房产规范登记类型及不规范登记类型情况下，权利主体、权利客体、权利、登记及档案数据的存储方式及与不动产登记类型的对应关系，并探讨由登记类型一数据结构一数据表—数据字段的存量房产登记数据向不动产登记数据库的转换思路。

关键词： BAS系统   PLC软件组态   数据结构   解耦   效率  

摘要： 本文提出一种基于接口特征的地铁BAS系统组态数据建模方法,以实现机电设备数据模型的建立,并架起各功能模块间的桥梁,规范软件组态中各数据接口,实现数据流各环节解耦,提高地铁车站BAS软件组态的的适应性、复用性与可维护性,最大限度避免了外部设备接口变动对逻辑组态产生的影响。

关键词： algorithms   sequence of real numbers   maximum-sum segment  

摘要： A linear-time constructible data structure for a real number sequence supporting O(1)-time queries of the maximal local maximum-sum segment of any contiguous subsequence containing any specific position is proposed, where a local maximum-sum segment is a segment whose maximum-sum segment is itself.

关键词： traffic rate   acceleration   traffic network   algorithms   Data structures   Queue   hardware architecture   long slide   Network analysers   Network analysis  

摘要： As the sheer amount of computer generated data continues to grow exponentially, new bottlenecks are unveiled that require rethinking our traditional software and hardware architectures. In this paper we present five algorithms and data structures (long queue emulation, lockless bimodal queues, tail early dropping, LFN tables, and multiresolution priority queues) designed to optimize the process of analyzing network traffic. We integrated these optimizations on R-Scope, a high performance network appliance that runs the Bro network analyzer, and present benchmarks showcasing performance speed ups of 5X at traffic rates of 10 Gbps. (C) 2018 Elsevier B.V. All rights reserved.

关键词： 数据结构   算法分析   软件工具  

ISBN: (纸本)9787111606604

关键词： Agriculture   Atmospheric science   Biology and life sciences   Carbon sequestration   Climate change   Climatology   Conifers   Earth sciences   Ecology   Ecology and environmental sciences   Ecosystems   Engineering and technology   Environmental engineering   Eukaryota   Forestry   Forests   Mathematical and statistical techniques   Mathematics   Organisms   Physical sciences   Plants   Research and analysis methods   Research Article   Research assessment   Research errors   Statistical methods   Statistics (mathematics)   Terrestrial environments   Test statistics   Trees  

摘要： This paper investigates the consequences of ignoring the clustered data structure on allometric models. Clustered data, in the form of multiple trees sampled from multiple forest stands is commonly used to develop biomass allometric models. Of 102 reviewed papers published between 2012 and 2016 that reported biomass allometric models, 84 (82%) have used a clustered sampling design. However, in as many as 80% of these, the clustered data structure was ignored, potentially violating the independence assumption in ordinary least squares methods. The consequences of ignoring clustered data structure were empirically validated using two clustered biomass datasets (of 110 and 220 trees, with the cluster size of 5 and 10 trees respectively). We showed that when Intraclass Correlation Coefficient (ICC) was higher than zero, ignoring the clustered data structure returned underestimated standard errors, affecting further the confidence interval and t-test results. The underestimation level depended on ICC (which shows the variance proportion that was caused by the forest stand) and on cluster size (the number of trees sampled from one forest stand). We also showed that using first-order autocorrelation tests, such as the traditional Durbin-Watson statistic, to detect the autocorrelation due to clustered structure could be misleading as the test may show lack of autocorrelation even though ICC is different from zero. In conclusion, when ICC is higher than zero, ignoring the clustered data structure yields over-confident biomass predictions (due to underestimated confidence interval) and/or incorrect research conclusions (due to overestimated evidence against null hypothesis in t-test). Therefore, using a modelling approach that accounts for the hierarchical structure of the data is highly recommended when any form of clustering can be identified, even if the autocorrelation is not significant.