Photoshop软件课几种教学模式的探讨

当前大多数中职学校的Photoshop这门课程的实体教学模式还不完善,对其教学模式的探讨有助于课程的顺利开展。结合自身的教学实践和一些基本理论,阐述了案例法、行为导向法以及任务驱动法如何运用到课程的教学当中,并对其应用效果作了相应的评价。这3种方法结果:每种教学模式都有其运用方法和效果。这3种教学模式应该因地制宜地运用,将会大大提高课堂的效率。     

基于平行坐标法的可视数据挖掘

数据挖掘和数据可视化技术的结合形成可视数据挖掘。通过可视化技术的运用,数据挖掘可以增加其数据的针对性和结果的可信度。平行坐标法是数据可视化的代表方法之一,该文在平行坐标法的基础上,探讨了在其中实现可视化数据挖掘的基本方法,是进一步开发可视数据挖掘系统的初步研究。     

考虑决策者心理行为的软件质量评价方法

针对软件质量评价方法中缺乏考虑决策者心理行为的问题,提出一种基于区间二元语义的交互式多准则决策（TODIM）软件质量评价方法。首先,通过区间二元语义表征专家对软件质量的评价信息;其次,利用主观赋权法和逼近理想解排序法（TOPSIS）分别计算软件质量属性的主客观权重,并在此基础上通过组合赋权法获取软件质量属性的综合权重;然后,为更好地刻画专家在软件质量评价过程中的心理行为,将TODIM引入软件质量评价中。利用TODIM方法对高速铁路调度系统的助理调度员终端进行软件质量评价,结果表明铁路软件供应商提供的第3款助理调度员终端软件的占优度值最高,质量最优。将该方法与后悔理论方法以及II类偏好序结构排序法（PROMETHEE-II）进行对比分析,所得结果表明三种方法在质量最优软件的选取上具有一致性,然而三者的总体排序有所差异,说明所构建方法在描述多准则的交互关系和决策者的心理行为上具有较强的优越性。     

换热网络综合方法的研究进展

随着对合理、经济利用能源、资源要求的不断提高,换热网络综合引起了人们的高度重视。本文对过程工业非常重要的过程集成问题——换热网络综合,总结了过程热集成常见的3种方法:夹点设计法、数学规划法以及人工智能法。并分别对这3种方法在发展过程中的研究内容和设计方法及其取得的研究成果进行了讨论。最后从工业应用角度对不同的方法做了比较及评价。     

基于改进熵值法的MCAI软件评价模型研究

本文用标准化方法对熵值法进行改进,将改进后的熵值法与层次分析法结合,建立基于改进熵值法的MCAI软件评价模型。该评价模型解决了层次分析法评价模型中对MCAI软件评价指标赋权完全依赖专家对评价指标的主观判断的问题,实现了对MCAI软件评价指标的固有信息与评价者的主观判断信息的量化与综合,对MCAI软件评价指标进行客观赋权,使得对MCAI软件评价既客观又科学,是对MCAI软件评价方法的创新。通过该评价模型的运用,确保MCAI软件的开发朝向用户需求的方向进行,提高了MCAI软件的开发质量和开发效率。     

基于模糊综合评价的课堂教学质量数据挖掘

本文从应用模糊综合评价法得到教师的课堂教学质量的评估等级和量化成绩,应用模糊聚类算法确定主关键条件属性集,使用模糊数据挖掘出评估数据库中教师课堂教学质量评估等级同评估指标之间的规则知识,以一个应用实例为对象建立的课堂教学质量模糊数据挖掘验证了该方法的可行性.     

数据挖掘软件现状研究

数据挖掘是当前计算机应用的一个热点研究方向。经过近十年的发展,数据挖掘软件得到了长足的进步,许多成熟的数据挖掘软件被开发出来并投入实用。文章对当前数据挖掘软件的现状进行了研究,并着重分析了四种常用的数据挖掘软件。通过该文的研究指出了当前数据挖掘软件的成果和不足,并进一步指明了今后发展的方向。     

通讯软件录音功能实现方法浅析

Skype、MSN、QQ等即时消息软件给我们的生活和工作带来了很多便利,但它们都没有设计录音功能。本文就通讯软件的录音功能实现,分析了Mixer control法、VAC法、Windows钩子法这三种方法的实现原理,并说明了各种方法的优缺点,为通讯软件录音功能的实现提供了参考,特别是VAC方法和Windows钩子法,对于其它相关应用也有借鉴意义。     

数据挖掘研究

数据挖掘是指借助数据挖掘算法从大量的数据中搜寻有价值的信息,包括问题定义、数据获取、数据预处理、建立模型、模型评价和模型应用等步骤。常用的数据挖掘方法有关联规则法、决策树法、模糊集法、粗糙集法、神经网络法和遗传算法。同时,数据挖掘过程中的道德和法律问题也值得关注。     

建立启发式评价函数h(n)的一般方法

本文给出了在启发式图搜索策略中建立有效的启发式评价函数h(n)的两个一般方法:相似问题逼近法和函数逼近法,并对这两种方法进行了讨论。     

Bayesian updating of optimal release time for software systems

In this paper, a Bayesian method dealing with software reliability growth with consideration of the learning effect is proposed to determine an optimal release time for software systems with regard to the testing cost and experts’ prior judgments. Such an approach is able to devise an appropriate software-debugging scheme which has the best arrangement of available resources and personnel with a minimal software testing cost when lacking sufficient information for decision making. Past research on software reliability emphasized the estimation of the number of cumulative software errors or the software reliability with respect to a specific time period, yet it neglected the determination of software release time with consideration of the software testing cost, meaning that existing approaches are not entirely practical. Accordingly, the proposed method is concerned with the evaluation of the software testing cost incurred during the testing period based on experts’ prior judgments and the software testing data collected within a given duration, and is thus characterized by its practicality as well as meaningfulness with consideration of the learning effect. Finally, a numerical example is given to verify the effectiveness of the proposed approach, and sensitivity and risk analyses are performed on this example.

软件质量评价体系及其实现

近年来,随着国际竞争的不断升级,“质量是软件的生命”这一口号已深入人心。软件质量评价作为软件质量保证的重要手段,已逐渐被开发商、用户所重视。世界上有许多机构开始进行质量评价的形式化手段研究,并推出了如DIN66285,ISO9126等软件质量评价标准。本文就是对这一领域的研究,主要分析了软件质量的科学评价方法,总结了得出的一些算法与心得,在国内首次提出了评价模型分级的设想,并在计算机上加以实现。     

软件可靠性专家系统(SRES)中经验模型的奇异性问题与参数估计方法

本文作者以“一切让数据说明问题”为原则,采用人工智能技术,开发出软件可靠性专家系统ＳＲＥＳ（ＳｏｆｔｗａｒｅＲｅｌｉｂｉｌｉｔｙＥｘｐｅｒｔＳｙｓｔｅｍ）根据统一的标准,通过统一组软件可靠性模型ＳＲＭ（ＳｏｆｔｗａｒｅＲｅｌｉａｂｉｌｉｔｙＭｏｄｅｌｓ）的拟合结果,经过推理,得出一个“最适”模型,推荐给用户,作为估测该软件系统的一个“标准模型”同时为软件开发方和使用方所接受,以解决软件可靠性模型应     

Automated classification of software change messages by semi-supervised Latent Dirichlet Allocation

ContextTopic models such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have demonstrated success in mining software repository tasks. Understanding software change messages described by the unstructured nature-language text is one of the fundamental challenges in mining these messages in repositories.ObjectiveWe seek to present a novel automatic change message classification method characterized by semi-supervised topic semantic analysis.MethodIn this work, we present a semi-supervised LDA based approach to automatically classify change messages. We use domain knowledge of software changes to make labeled samples which are added to build the semi-supervised LDA model. Next, we verify the cross-project analysis application of our method on three open-source projects. Our method has two advantages over existing software change classification methods: First of all, it mitigates the issue of how to set the appropriate number of latent topics. We do not have to choose the number of latent topics in our method, because it corresponds to the number of class labels. Second, this approach utilizes the information provided by the label samples in the training set.ResultsOur method automatically classified about 85% of the change messages in our experiment and our validation survey showed that 70.56% of the time our automatic classification results were in agreement with developer opinions.ConclusionOur approach automatically classifies most of the change messages which record the cause of the software change and the method is applicable to cross-project analysis of software change messages.     

软件质量保障技术

文章从软件工程方法、软件质量国际标准体系、软件可靠性和软件构件技术等方面介绍了现有的软件质量保障技术,同时分析了用数据挖掘方法研究软件质量的可行性,并提出了具体的解决方案。     

计算机软件质量模糊综合评价方法

软件产品质量是软件企业的生命 .本文通过对软件质量特性的分析 ,提出了软件质量的综合评价指标体系 ,并讨论怎样根据该指标体系收集有关数据 ,采用二级模糊综合评判法处理所得数据 ,得出定量评价结果 .     

面向对象的软件集成技术研究

提出了一种软件集成策略,该策略将软件集成分为界面集成、数据集成和代码集成三个方面,提出了API函数调用法、逐级菜单搜索法等界面集成方法。设计了数据集成类,它将MFC中有关文件操作的类封装在一起,并能实现文件定位、文件段落删改、插入等功能。按照上述策略在Visual C++集成开发环境下实现了LINGO 7.0和自己编写的优化功能软件的集成,结果表明该方法是有效的。     

软件系统层次的数据挖掘方法

论文在软件数据中挖掘聚类模式的研究基础上,进一步提出了在软件层次上的数据挖掘方法。对于解决软件工程中项目代价的估算和评测具有重要的参考价值。首先收集不同类型软件数据,接着根据Halstead软件科学从它们中间抽取不同的特征,以此来标识不同的软件;然后将这些软件归为不同的类别,对于同一类中的软件可以认为它们具有相似的软件代价或相似的结构,可以用于病毒特征检测和预测,对于在不同类中的软件可以发现二者存在差异的决定“相异因素”;最后给出了对5414个实际软件系统挖掘的实验结果。结果表明这种软件层次的数据挖掘方法是可行而有效的。     

Resource oriented selection of rule-based classification models: An empirical case study

The amount of resources allocated for software quality improvements is often not enough to achieve the desired software quality. Software quality classification models that yield a risk-based quality estimation of program modules, such as fault-prone (fp) and not fault-prone (nfp), are useful as software quality assurance techniques. Their usefulness is largely dependent on whether enough resources are available for inspecting the fp modules. Since a given development project has its own budget and time limitations, a resource-based software quality improvement seems more appropriate for achieving its quality goals. A classification model should provide quality improvement guidance so as to maximize resource-utilization. We present a procedure for building software quality classification models from the limited resources perspective. The essence of the procedure is the use of our recently proposed Modified Expected Cost of Misclassification (MECM) measure for developing resource-oriented software quality classification models. The measure penalizes a model, in terms of costs of misclassifications, if the model predicts more number of fp modules than the number that can be inspected with the allotted resources. Our analysis is presented in the context of our Rule-Based Classification Modeling (RBCM) technique. An empirical case study of a large-scale software system demonstrates the promising results of using the MECM measure to select an appropriate resource-based rule-based classification model. Taghi M. Khoshgoftaar is a professor of the Department of Computer Science and Engineering, Florida Atlantic University and the Director of the graduate programs and research. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence applications, computer security, computer performance evaluation, data mining, machine learning, statistical modeling, and intelligent data analysis. He has published more than 300 refereed papers in these areas. He is a member of the IEEE, IEEE Computer Society, and IEEE Reliability Society. He was the general chair of the IEEE International Conference on Tools with Artificial Intelligence 2005. Naeem Seliya is an Assistant Professor of Computer and Information Science at the University of Michigan - Dearborn. He recieved his Ph.D. in Computer Engineering from Florida Atlantic University, Boca Raton, FL, USA in 2005. His research interests include software engineering, data mining and machine learnring, application and data security, bioinformatics and computational intelligence. He is a member of IEEE and ACM.