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关键词： 区块链   可扩展性   数据结构   数据库  

摘要： 区块链技术在物联网等大交易量场景中的应用面临的突出问题之一是可扩展性。文中分析了区块链可扩展性问题的来源、影响与主要解决方案。针对区块链的可扩展性,引入区块指针和状态更新池的概念,提出了区块数据的双子链模型,并对双子链模型解决可扩展性问题的可能性和安全性进行了讨论。

关键词： C++语言  

ISBN: (纸本)9787564182236

摘要： 本书在基本数据结构的基础上, 围绕一些常用的高级数据结构, 结合大量实战例题, 深入分析“数据结构是如何服务于算法的”, 这也是本书编写的一个核心理念: 学习数据结构是为了“用好”数据结构。本次修订除了改错, 主要是更换全书的程序代码为 (C++) 。

关键词： data structure   industrial ecology   input-output analysis (IOA)   life cycle assessment (LCA)   material flow analysis (MFA)   systems modeling  

摘要： Modern society depends on the use of many diverse materials. Effectively managing these materials is becoming increasingly important and complex, from the analysis of supply chains, to quantifying their environmental impacts, to understanding future resource availability. Material stocks and flows data enable such analyses, but currently exist mainly as discrete packages, with highly varied type, scope, and structure. These factors constitute a powerful barrier to holistic integration and thus universal analysis of existing and yet to be published material stocks and flows data. We present the Unified Materials Information System (UMIS) to overcome this barrier by enabling material stocks and flows data to be comprehensively integrated across space, time, materials, and data type independent of their disaggregation, without loss of information, and avoiding double counting. UMIS can therefore be applied to structure diverse material stocks and flows data and their metadata across material systems analysis methods such as material flow analysis (MFA), input-output analysis, and life cycle assessment. UMIS uniquely labels and visualizes processes and flows in UMIS diagrams;therefore, material stocks and flows data visualized in UMIS diagrams can be individually referenced in databases and computational models. Applications of UMIS to restructure existing material stocks and flows data represented by block flow diagrams, system dynamics diagrams, Sankey diagrams, matrices, and derived using the economy-wide MFA classification system are presented to exemplify use. UMIS advances the capabilities with which complex quantitative material systems analysis, archiving, and computation of material stocks and flows data can be performed.

关键词： Data complexity   Data networks   Big data   Clustering  

摘要： 'Big' high-dimensional data are commonly analyzed in low-dimensions, after performing a dimensionality reduction step that inherently distorts the data structure. For a similar analysis, clustering methods are also often used. These methods introduce a bias as well, either by starting from the assumption of a particular, often geometric, property of the clusters, or by using iterative schemes to enhance cluster contours, with consequences that are hard to control. The goal of data analysis should, however, be to encode and detect structural data features at all scales and densities simultaneously, without assuming a parametric form of data point distances, or modifying them. Here, we propose a novel approach that directly encodes data point neighborhood similarities as a sparse graph. Our non-iterative framework permits a transparent interpretation of data, without altering the original data dimension and metric. Several natural and synthetic data applications demonstrate the efficacy of our novel method. (C) 2019 Published by Elsevier Ltd.

关键词： Multi-task learning   hierarchical structure   unsupervised learning   structural similarity  

摘要： Unsupervised multi-task learning exploits the shared knowledge to improve performances by learning related tasks simultaneously. In this paper, we propose an unsupervised multi-task learning method with hierarchical data structure. It strengthens similarities between instances in the same cluster, and increases diversities of instances by utilizing instances from related clusters. Firstly, we introduce Representative Dual Features (RepDFs) that possess representative capabilities in the feature space and the sample space for each cluster concurrently. Secondly, we explore hierarchical structural similarities between clusters in related tasks from the topological perspective: 1) feature basis matrix, which learns compact representations for features in the feature space;and 2) sample refined matrix, which preserves local structures in the sample space. Thirdly, we adopt RepDFs to measure correlations between clusters and incorporate hierarchical structural similarities to conduct knowledge transfer among tasks. Experimental results on real-world data sets demonstrate the effectiveness and superiority of the proposed method over existing multi-task clustering methods. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： 《数据结构》   二叉树   粒子群优化计算  

摘要： 《数据结构》课程中的二叉树是一个非常重要的概念,但在实际教学过程中,教师一般只注重讲解其理论,而忽视其应用。这样导致学生学习积极性不高,影响教学效果。本文通过二叉树在粒子群优化计算中的应用来说明二叉树的重要作用,使学生加深对二叉树的原理的理解,提高学生学习积极性。

关键词： 数据结构   计算机科学   控制数据   计算机程序  

摘要： 人才培养成果是评估一所高校的首要标准,教学能力是评估一名教师的核心能力。教学工作需要政策上的支持,更需要教师自身的努力,其中包括不断加深对教学内容的理解,与时俱进对教学方法的探索。过去10多年来,我们发现无论是985高校还是地方院校,都有一批热爱教学的教师,在教学实践中积累了许多有价值的心得。为此,本刊拟开辟"师道之声"栏目,登载系列文章,希望以此有益于浓郁重视教学的氛围,促进整体教学水平的提高。本期以北京大学李晓明老师的文章作为首篇,同时鼓励大家踊跃投稿。

关键词： 微信平台   算法与数据结构   教学改革  

摘要： 《算法与数据结构》课程的理论知识较为抽象,编程实践操作也具有一定的难度和工作量,学生普遍认为该课程难以理解和应用。针对应用型独立学院的学生特点,首先分析课程存在问题,然后分别从培养计划、课堂教学和实践教学三个方面进行探讨,最后借助微信公众平台开发和设计辅助教学内容。结果表明,基于微信平台的混合传统模式的教学手段,提高了学生的学习兴趣、主动性和学习成绩,达到了提高教学效果的预期目标。

关键词： 课程教学   工程教育   实践教学   教学案例  

摘要： 数据结构与算法课程作为计算机与信息类专业的基础课程,传统教学过程中存在重视理论教学,忽视实践教学的问题。为适应工程认证要求,对数据结构与算法课程实践教学环节进行研究与改革。将原有以理论教学为主的教学方式,转换为以理论教学为基础、实践教学为核心的教学体系,从而锻炼和培养学生的工程实践能力。围绕实践教学环节,以兰州交通大学服务大交通行业的特色为切入点,结合数据结构与算法课程特点,构建6～8个科学可行的实践教学案例,填补现阶段数据结构与算法课程实践教学环节无标准化、科学化、系统化教学案例的空白。