基于案例驱动的面向对象技术课程教学改革与实践

面向对象技术是一门理论性和实践性都很强的专业课程,结合教学实践的体会,提出了基于案例驱动的面向对象技术课程教学模式改革。首先分析了目前面向对象技术课程教学中遇到的问题,即教学中存在内容陈旧、教学方法单一、评价体系不够完善等。然后提出"案例驱动"的教学模式,阐述该教学模式的特点,并对比分析了与以往教学模式的差别。最后探讨了案例的选取,在课程教学过程中案例的应用,以及与之相适应的课程考核方式。     

自顶向下还是自底向上?——论计算机网络原理课程教学模式

计算机网络原理是计算机专业的核心课程,具有很强的实践性。课程教学中采取的教学模式决定课程的教学质量。本文分析该课程"自顶向下"和"自底向上"两种经典的教学模式,并在此基础上详细阐述该课程的教学模式改革实践,希望对该课程教学人员有一定的参考价值。     

高职C语言程序设计课程实践教学体系的设计

C语言程序设计是计算机类相关各专业的技术基础课,该课教学质量的高低,对后续相关课程的教学与实践有很大影响。本文阐述了在课程建设过程中,如何通过"任务驱动"教学模式对课程进行教学改革,构建阶梯递进的C语言程序设计课程实践教学体系。     

《计算机辅助设计》课程教学模式探索

在《计算机辅助设计》课程教学中,理论教学与实践教学严重脱节,该门课程实践性强的特点得不到体现,教学效果不理想。根据多年的教学研究和教学实践,从教学内容、教学模式等方面出发,对计算机应用专业《计算机辅助设计》课程的教学进行一系列探索,采用"以学生为中心"的教学模式,强调学习过程中学生的参与和思考,激发学生自主学习的积极性,提高教学质量。     

“C语言程序设计”理论与实践一体化教学的几点实践

分析目前高校"C语言程序设计"课程传统教学模式中存在的问题,并结合学校对该课程进行理论与实践教学一体化改革的实际情况,探讨新的C语言教学模式。     

“计算机操作系统”课程教学创新实践

传统的教学模式已不能适应现代"计算机操作系统"课程的教学需求。本文在该课程以往教学实践和经验的基础上,对"操作系统"课程的教学实践活动进行了总结,并提出了建立长效"实践教学链"的新的教学模式,以培养本科生学习"操作系统"课程的兴趣,从而提高他们的实践创新能力。     

“算法分析与设计”任务驱动教学模式改革与实践

本文分析了"算法分析与设计"课程教学中存在的问题,利用"任务驱动"教学方法,引入ACM/ICPC在线评测平台,结合教学实践,从任务设计、课堂教学和课程考核等方面探讨了一种注重过程和实践的教学模式。     

信息论与编码的教学改革探讨

针对信息论与编码课程在授课过程中存在的问题,探讨该课程教学理念与教学方法的改革思路,尝试以培养学生自主学习能力为主的教学模式,提出切实可行的实施方案。这种方案在教学过程中的实践取得了较好的效果。     

基于CDIO模式的嵌入式系统教学研究与探讨

嵌入式系统课程是一门理论与实践相结合的课程,该课程特别注重学生实践能力的培养,文章首先阐述嵌入式系统教学的特点,结合CDIO(做中学)的新型教学模式,通过设计多个实践课程,探讨实践的过程中学习理论知识、理论知识指导实践的教学模式。     

软件工程一体化教学模式探讨与实践

软件工程是计算机科学与技术专业的一门专业核心课程,在分析该门课程传统教学过程中存在问题的基础上,提出一种集成"项目驱动+范型对照+案例复现+团队实践"的一体化教学模式。教学实践表明,该模式的实施已取得良好的效果。     

大数据环境下的数据安全性探讨

随着互联网的高速发展,特别是近年来云计算、物联网等新兴技术的出现,社交网络等服务的广泛应用,人类社会的数据的规模正快速地增长,大数据时代已经到来。如何获取,分析大数据已经成为广泛的问题。但随着带来的数据的安全性必须引起高度重视。本文从大数据的概念和特征说起,阐述大数据面临的安全挑战,并提出大数据的安全应对策略。     

One-to-many data transformations through data mappers

The optimization capabilities of RDBMSs make them attractive for executing data transformations. However, despite the fact that many useful data transformations can be expressed as relational queries, an important class of data transformations that produce several output tuples for a single input tuple cannot be expressed in that way.

To overcome this limitation, we propose to extend Relational Algebra with a new operator named data mapper. In this paper, we formalize the data mapper operator and investigate some of its properties. We then propose a set of algebraic rewriting rules that enable the logical optimization of expressions with mappers and prove their correctness. Finally, we experimentally study the proposed optimizations and identify the key factors that influence the optimization gains.     

Efficient data reduction in multimedia data

As the amount of multimedia data is increasing day-by-day thanks to cheaper storage devices and increasing number of information sources, the machine learning algorithms are faced with large-sized datasets. When original data is huge in size small sample sizes are preferred for various applications. This is typically the case for multimedia applications. But using a simple random sample may not obtain satisfactory results because such a sample may not adequately represent the entire data set due to random fluctuations in the sampling process. The difficulty is particularly apparent when small sample sizes are needed. Fortunately the use of a good sampling set for training can improve the final results significantly. In KDD’03 we proposed EASE that outputs a sample based on its ‘closeness’ to the original sample. Reported results show that EASE outperforms simple random sampling (SRS). In this paper we propose EASIER that extends EASE in two ways. (1) EASE is a halving algorithm, i.e., to achieve the required sample ratio it starts from a suitable initial large sample and iteratively halves. EASIER, on the other hand, does away with the repeated halving by directly obtaining the required sample ratio in one iteration. (2) EASE was shown to work on IBM QUEST dataset which is a categorical count data set. EASIER, in addition, is shown to work on continuous data of images and audio features. We have successfully applied EASIER to image classification and audio event identification applications. Experimental results show that EASIER outperforms SRS significantly. Surong Wang received the B.E. and M.E. degree from the School of Information Engineering, University of Science and Technology Beijing, China, in 1999 and 2002 respectively. She is currently studying toward for the Ph.D. degree at the School of Computer Engineering, Nanyang Technological University, Singapore. Her research interests include multimedia data processing, image processing and content-based image retrieval. Manoranjan Dash obtained Ph.D. and M. Sc. (Computer Science) degrees from School of Computing, National University of Singapore. He has worked in academic and research institutes extensively and has published more than 30 research papers (mostly refereed) in various reputable machine learning and data mining journals, conference proceedings, and books. His research interests include machine learning and data mining, and their applications in bioinformatics, image processing, and GPU programming. Before joining School of Computer Engineering (SCE), Nanyang Technological University, Singapore, as Assistant Professor, he worked as a postdoctoral fellow in Northwestern University. He is a member of IEEE and ACM. He has served as program committee member of many conferences and he is in the editorial board of “International journal of Theoretical and Applied Computer Science.” Liang-Tien Chia received the B.S. and Ph.D. degrees from Loughborough University, in 1990 and 1994, respectively. He is an Associate Professor in the School of Computer Engineering, Nanyang Technological University, Singapore. He has recently been appointed as Head, Division of Computer Communications and he also holds the position of Director, Centre for Multimedia and Network Technology. His research interests include image/video processing & coding, multimodal data fusion, multimedia adaptation/transmission and multimedia over the Semantic Web. He has published over 80 research papers.     

Compression-based data mining of sequential data

The vast majority of data mining algorithms require the setting of many input parameters. The dangers of working with parameter-laden algorithms are twofold. First, incorrect settings may cause an algorithm to fail in finding the true patterns. Second, a perhaps more insidious problem is that the algorithm may report spurious patterns that do not really exist, or greatly overestimate the significance of the reported patterns. This is especially likely when the user fails to understand the role of parameters in the data mining process. Data mining algorithms should have as few parameters as possible. A parameter-light algorithm would limit our ability to impose our prejudices, expectations, and presumptions on the problem at hand, and would let the data itself speak to us. In this work, we show that recent results in bioinformatics, learning, and computational theory hold great promise for a parameter-light data-mining paradigm. The results are strongly connected to Kolmogorov complexity theory. However, as a practical matter, they can be implemented using any off-the-shelf compression algorithm with the addition of just a dozen lines of code. We will show that this approach is competitive or superior to many of the state-of-the-art approaches in anomaly/interestingness detection, classification, and clustering with empirical tests on time series/DNA/text/XML/video datasets. As a further evidence of the advantages of our method, we will demonstrate its effectiveness to solve a real world classification problem in recommending printing services and products. Responsible editor: Johannes Gehrke     

Fuzzy data mining for time-series data

Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In the past, many approaches based on regression, neural networks and other mathematical models were proposed to analyze the time series. In this paper, we attempt to use the data mining technique to analyze time series. Many previous studies on data mining have focused on handling binary-valued data. Time series data, however, are usually quantitative values. We thus extend our previous fuzzy mining approach for handling time-series data to find linguistic association rules. The proposed approach first uses a sliding window to generate continues subsequences from a given time series and then analyzes the fuzzy itemsets from these subsequences. Appropriate post-processing is then performed to remove redundant patterns. Experiments are also made to show the performance of the proposed mining algorithm. Since the final results are represented by linguistic rules, they will be friendlier to human than quantitative representation.     

Scattered data interpolation of Radon data

Linear combinations of translates of a given basis function have long been successfully used to solve scattered data interpolation and approximation problems. We demonstrate how the classical basis function approach can be transferred to the projective space ℙ ^d−1. To be precise, we use concepts from harmonic analysis to identify positive definite and strictly positive definite zonal functions on ℙ ^d−1. These can then be applied to solve problems arising in tomography since the data given there consists of integrals over lines. Here, enhancing known reconstruction techniques with the use of a scattered data interpolant in the “space of lines”, naturally leads to reconstruction algorithms well suited to limited angle and limited range tomography. In the medical setting algorithms for such incomplete data problems are desirable as using them can limit radiation dosage.     

数据竞争的规制：数据的利用与数据保护的协调

自互联网出现以来,数据保护一直是个难题。当社交媒体网站在数字市场上大展拳脚的那一刻,对用户数据和信息的保护让决策者们不得不保持警惕。在数字经济时代的背景下,数据逐渐成为企业提升竞争力的重要要素,围绕着数据展开的市场竞争越来越多。数字经济时代,企业对数据资源的重视与争夺,将网络平台权利与用户个人信息保护、互联网企业之间有关数据不正当竞争的纠纷和冲突,推上了风口浪尖。因此,如何协调和把握数据的合理利用和保护之间的关系,规制不正当竞争行为,以求在数字经济快速发展的洪流中,占据竞争优势显得尤为重要。文章将通过分析数据的二元性,讨论数据在数字经济时代的价值,并结合反不正当竞争法和实践案例,进一步讨论数据利用和保护的关系。     

Test data regeneration: generating new test data from existing test data

Existing automated test data generation techniques tend to start from scratch, implicitly assuming that no pre‐existing test data are available. However, this assumption may not always hold, and where it does not, there may be a missed opportunity; perhaps the pre‐existing test cases could be used to assist the automated generation of additional test cases. This paper introduces search‐based test data regeneration, a technique that can generate additional test data from existing test data using a meta‐heuristic search algorithm. The proposed technique is compared to a widely studied test data generation approach in terms of both efficiency and effectiveness. The empirical evaluation shows that test data regeneration can be up to 2 orders of magnitude more efficient than existing test data generation techniques, while achieving comparable effectiveness in terms of structural coverage and mutation score. Copyright © 2010 John Wiley & Sons, Ltd.