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关键词： 数据结构   计算思维   MOOC   教学改革  

摘要： 针对教育信息化的新形势,分析数据结构课程教学现状,讨论计算思维与数据结构的关系,利用MOOC资源开展混合教学模式的方案,对基于计算思维的数据结构课程MOOC教学模式进行深入探讨。

关键词： 立德树人   课程思政   C语言   数据结构   软件工程  

摘要： 加强思想政治教育是实现"立德树人"的重要途径。为提高计算机类专业学生的思想认识,建立正确的世界观、人生观和价值观,从历史进程、新时代、个人成才三个维度分析了实施课程思政的可行性;设计了计算机类专业课程思政的目标;以C语言、数据结构、软件工程三门课程为例探讨了计算机类课程思政的具体实施方法。

关键词： PBL   自主学习   分组讨论   数据结构实践  

摘要： 首先描述了该教学模式的提出背景,并对现在数据结构实践教学存在的困难进行剖析,提出了基于问题的教学模式在数据结构实践教学中的应用研究,并根据具体的实践过程和学生评价对传统教学法和PBL教学法进行比较总结。通过比较发现基于问题的教学法可以改善数据结构实践课堂效果,提高教学内容的深度和广度,提高就业质量,为社会、公司、企业培养高素质的应用人才提供了可行性。

关键词： 数据结构   可视化   动画形式  

摘要： 许多学生在学习数据结构时,由于其中包含许多抽象复杂且较为枯燥的概念,使得他们通常对这些概念难以理解,有的学生甚至出现厌学情绪。数据结构可视化通过将数据结构及与其相关的算法以动画形式展现出来,从而帮助学生乃至广大的计算机爱好者更加容易地学习数据结构。

关键词： Data refinement   Algorithm verification   Code generation  

摘要： We consider the problem of formally verifying an algorithm in a proof assistant and generating efficient code. Reasoning about correctness is best done at an abstract level, but efficiency of the generated code often requires complicated data structures. Data refinement has been successfully used to reconcile these conflicting requirements, but usability calls for automatic tool support. In the context of Isabelle/HOL, two frameworks for automating data refinement have been proposed (Lammich, in: Blazy, Paulin-Mohring, Pichardie (eds) ITP 2013, LNCS, vol 7998, Springer, Heidelberg, pp 84-99, 2013;Lochbihler, in: Blazy, Paulin-Mohring, Pichardie (eds) ITP 2013, LNCS, vol 7998, Springer, Heidelberg, pp 116-132, 2013). In this paper, we present and compare the two frameworks and their underlying ideas in depth. Thereby, we identify the challenges of automating data refinement, highlight the similarities and differences of the two approaches, and show their strengths and limitations both from the implementer's and the user's perspectives. A case study demonstrates how to combine both frameworks, benefiting from the strengths of each.

关键词： 数据结构  

ISBN: (纸本)9787564184070

摘要： 本书提供一系列采用C++程序设计语言实现的基础数据结构及其基本算法的实验练习题，主要分为算法分析、线性表、受限线性表、树、图、排序和搜索共7个基础实验单元，最后一个单元是综合实验。

摘要： In the big data era, most of the data generated every day are high dimensional such as text and image data. Learning compact representations from the input data can help in dealing with the high dimensionality and visualization of the data. These re- presentations can be learned to map the input space into a latent space in which, e. g., complex relationships present in the data become more explicit, while preserving the structure and geometrical properties of the data. In the case of discrete input features such as text data, embedding algorithms try to learn a vector space representation of inputs while preserving certain aspects of similarity or relatedness. Training these embeddings is time-consuming and requires a large corpus. This makes it difficult to train task-specific embeddings due to insufficient data in the downstream tasks which has given rise to pre-trained models and embeddings. In this thesis, we first study dimensionality reduction methods and propose Unit Ball Embedding (UBE), a spherical representation learning method that learns the structure of manifolds and maximizes their separability, which makes it suitable for both clustering and visualization. We then generalize the algorithm and apply it to learn word embeddings, taking into account the contexts of individual words. Our word embedding solution learns a better distribution of words in the latent space by pushing unrelated words away from each other. We investigate and address the naive procedure of negative sampling in Word2Vec and exploit the full capacity of negative examples. We also analyze frequency-based and spectral word embedding methods and show how the idea of negative context can be used in these types of algorithms. We propose EigenWord, a spectral word embedding with an intuitive formulation that makes use of negative examples, and theoretically show that it has an optimal closed- form solution. Finally, we tackle the Word Sense Disambiguation (WSD) problem and propose a mul

关键词： 智慧教学模式   蓝墨云班课   教学设计  

摘要： 本研究通过基于大数据技术的高校智慧教学模式的研究与实践,试图提出一种改变高校传统教学存在的"以教为中心"的教学现象的智慧教学模式,提倡"以学生为中心""以解决问题任务为导向""以计算思维、创新能力"的智慧教学模式,培养出有思想、会分析、能判断、懂逻辑、主动探索最优方案的创新型高技能人才,提高学生自主学习精神和解决实际问题的能力。

关键词： 留学生教育   数据结构   课程教学   教学方法  

摘要： 文章认真分析了留学生群体的特点,分析了数据结构教学的特点、留学生学习的实际情况,以及在数据结构课程教学过程中遇到的问题。针对存在的问题,通过对教学管理方法、教学模式、师资力量建设等方面进行改进,运用多种教学方法,积极提高教学水平,从而贯彻教育部计算机课程相关教学理念。

关键词： Array range query   Range minimum query   Data structures   Linear space   Constant time  

摘要： We revisit the range minimum query problem and present a new O(n)-space data structure that supports range minimum queries in O(1) time. The goal is to construct a static data structure that efficiently supports range minimum queries on a given list A[0 : n - 1] of n items drawn from a totally ordered set. Each range minimum query consists of an input pair of indices (i, j) for which the minimum element of the subarray A[i : j] must be returned. Although previous data structures exist whose asymptotic bounds match ours, our goal is to introduce a new solution that is simple, intuitive, and practical without increasing asymptotic costs for query time or space. We analyze our new data structure theoretically and practically, the latter through an evaluation of its performance relative to implementations of four of the top range minimum query data structures. (C) 2018 Elsevier B.V. All rights reserved.