云计算在生物技术领域的应用

随着基因组测序等生物学技术的发展,海量生物学数据不断产生,生物计算对计算能力和存储量的要求也越来越高.云计算所具有的高速的计算能力、高存储量以及使用方便等特点能够很好地满足生物研究的需求,融合云计算成为生物计算发展的趋势.总结了可用于生物技术领域的云平台以及云计算在生物学研究中的应用,并指出了云计算在生物技术领域的发展前景及面临的挑战.     

基于云计算的电子商务解决方案研究

云计算技术的出现,给电子商务的快速发展提供新的增长点.总结了云计算的概念和三种计算形式后,详细探讨了基于云计算环境的电子商务解决方案,主要有,从云计算关键技术实现、标准建立和安全策略三个方面给出了电子商务系统构建的技术应用方案;探讨了基于电子商务云服务的虚拟电子商务系统构建的管理策略.最后,简要探讨了基于云计算的电子商务应用中的的研究问题和思路.     

带赔偿的云计算服务拍卖机制研究

云计算提供了按需获取、按使用付费的IT资源获取模式,由于存在大量的云计算服务提供商,以市场化的形式来组织云计算资源的分配与获取是一种合理的方式.以面向市场的云计算服务架构为基础,研究了云计算用户和服务提供商之间的匹配问题.采用拍卖方式实现具体的匹配过程,并将服务失败时的赔偿问题纳入考虑,结合服务提供商的违约率提出了两种带赔偿的云计算服务拍卖机制一在逆向一级密封拍卖上增加赔偿因子,分析了其二人博弈和多人博弈的均衡;同时,研究了服务权再分配对该机制的影响.     

企业云计算应用中的安全风险防范

随着云计算的发展,越来越多的企业将存储和计算需求付诸于云端.但是对云计算的安全问题不容忽视,成为当前的一个研究热点.在企业云计算应用环境中,云计算的安全问题包括信息的存储和处理以及传输的保密性、完整性和可用性,对近年来企业云计算应用中的安全风险相关的研究成果进行总结.     

云计算环境中的身份认证模型

云计算继承和融合了众多技术,并结合具体应用进行了突破性创新,已成为当前研究的重点和热点.身份认证与资源授权是确保云计算安全的前提,其主要内容涉及云用户与云服务之间以及云平台中不同系统之间的访问与控制.在对云计算信息基础架构进行简要介绍的基础上,针对云计算环境中统一身份认证的特点和要求,综合分析了SAML2.0、OAuth2.0和OpenID2.0等技术规范的功能特点,提出了一种开放标准的云计算身份认证模型,为云计算中逻辑安全域的形成与管理提供了参考.     

基于云计算的大规模定制客户需求响应服务平台

正确响应客户需求是大规模定制的关键.云计算作为一种新型IT服务模式,可以为大规模定制客户需求响应提供有效的服务支持.在讨论大规模定制需求响应过程以及云计算特点基础上,分析大规模定制需求响应过程中的支持服务需求以及大规模定制需求响应服务的云计算应用需求,提出大规模定制需求响应SaaS服务平台的概念框架.最后,讨论大规模定制需求响应云服务平台系统的基本思路.     

一种基于大数据处理的异构私有云系统

云计算和大数据已成为IT领域的研究热点,如何将云计算在数据存储和数据处理方面的优势应用于大数据领域具有重要的实际应用价值.开源的云平台OpenStack可方便地从硬件管理方面构建私有云,其存储模块Swift能够支持PB级的大数据存储.开源的云平台Hadoop在数据处理方面具有很强的优势,但在支持超大数据存储方面存在不足.通过对OpenStack中的存储模块Swift和Hadoop中的文件处理模块HDFS的比较分析,提出了将Swift和Hadoop的MapReduce技术结合来构建企业处理大数据的私有云计算系统方案.分析结果显示该方案是可行的,这种异构的私有云系统可以整合不同云计算平台各自的优势进行高效的大数据处理.     

基于云计算的信息服务通用模型研究

概括了云计算技术的普遍特征,分析了云计算技术在信息服务业的典型应用案例及其局限性,继而针对性得提出了一种基于云计算的信息服务通用模型,并通过量化分析论证了该模型的可行性.     

云计算服务用户持续使用的理论模型

通过梳理IS持续使用理论和服务管理中用户满意度研究的发展脉络,借鉴其成果并结合云计算信息系统的特点,提出了云计算服务用户满意度与持续使用的理论模型,并经过了实证检验.研究结果表明,云计算信息系统的系统质量和服务质量会影响用户的满意度,并且满意度与感知有用性一起影响用户的持续使用意图.使用IT知识存量和组织嵌入作为调节变量,分析了在不同的条件下,服务质量和系统质量等外生因素与用户满意度之间关系的变化.定义了云计算信息系统的系统质量、服务质量、用户感知有用性的测量维度和方法,这对云计算提供商有的放矢的提高系统质量和服务质量,提高云计算用户的有用性感知和满意度,进而促使其持续使用有直接的借鉴意义.     

基于改进共生优化算法的云计算资源调度优化

针对云计算资源调度模型具有复杂性和不确定性,导致资源分配合理性较差,存在资源浪费的问题,提出一种改进的共生优化算法对云计算资源调度模型进行优化.针对共生算法存在局部搜索能力较差以及在迭代后期易早熟陷入局部最优的问题,通过镜像初始化以及牛顿局部增强策略进行改进,并进行数值仿真实验.最后以改进后的共生优化算法对云计算资源调度模型进行优化求解,实验结果表明,改进后的共生优化算法有效提降低了云计算所需时间和能耗花费,提高了资源利用率.     

职校生数学成绩影响因素的累积LOGISTIC模型分析

利用累积Logistic模型对影响职校生数学成绩的因素进行了实证分析,研究结论显示,影响职校生数学成绩的主要因素是学生的计算能力、学习主动性以及家长对学生学习的关注,这与职业学校生源的复杂性和职业学校数学教育的特殊性有关.ROC分析表明,利用计算能力、学习主动性、家长的关注三个因素诊断学生数学成绩是合适的,提示教师要特别关注学生计算能力、学习能力的培养,督促家长关注孩子的学习.     

基于“动态生成”的大学数学课堂教学

"动态生成"教学观的建立旨在摆脱课堂教学中以教师为中心、强调知识传授的传统教学模式的缺陷,从根本上正确理解课堂教学的复杂性和动态性,构建充满生命活力的大学数学课堂教学生态环境.在用动态生成的视角审视当前大学数学课堂教学中存在的问题的基础上,文章对在课堂教学中如何有效地进行动态生成提出了一些具体的策略:更新教学观念,精心预设弹性化的数学课堂教学方案;根据学生课堂反馈情况及时调整预设,并及时捕捉可利用的动态资源,为学生的生成创造可能的机会;加强教学研究,不断提高课堂教学智慧.     

Teachers’ responses to student mathematical thinking in Chinese elementary mathematics classrooms

Recent research on teachers’ use of student mathematical thinking (SMT) and recommendations for effective mathematics instruction claim that how teachers respond to SMT has great impact on student mathematical learning in the classroom. This study examined some Chinese mathematics teachers’ responses to student in-the-moment mathematical thinking that emerged during whole class discussion. The findings of this study revealed that the majority of Chinese elementary mathematics teachers in the data involved the whole group of students to make sense of in-the-moment SMT. They either invited students to digest SMT involved in the instance or provided an extension of the instance to further develop student mathematical understanding.     

Students’ experiences with mathematics teaching and learning: listening to unheard voices

This study documents students’ views about the nature of mathematics, the mathematics learning process and factors within the classroom that are perceived to impact upon the learning of mathematics. The participants were senior secondary school students. Qualitative and quantitative methods were used to understand the students’ views about their experiences with mathematics learning and mathematics classroom environment. Interviews of students and mathematics lesson observations were analysed to understand how students view their mathematics classes. A questionnaire was used to solicit students’ views with regards to teaching approaches in mathematics classes. The results suggest that students consider learning and understanding mathematics to mean being successful in getting the correct answers. Students reported that in the majority of cases, the teaching of mathematics was lecture-oriented. Mathematics language was considered a barrier in learning some topics in mathematics. The use of informal language was also evident during mathematics class lessons.     

Lasso变量选择的分布式算法

Lasso是机器学习中比较常用的一种变量选择方法,适用于具有稀疏性的回归问题.当样本量巨大或者海量的数据存储在不同的机器上时,分布式计算是减少计算时间提高效率的重要方式之一.本文在给出Lasso模型等价优化模型的基础上,将ADMM算法应用到此优化变量可分离的模型中,构造了一种适用于Lasso变量选择的分布式算法,证明了...     

On Providing Quality of Service in Grid Computing through Multi-objective Swarm-Based Knowledge Acquisition in Fuzzy Schedulers

Nowadays, Grid computing is increasingly showing a service-oriented tendency and as a result, providing quality of service (QoS) has raised as a relevant issue in such highly dynamic and non-dedicated systems. In this sense, the role of scheduling strategies is critical and new proposals able to deal with the inherent uncertainty of the grid state are needed in a way that QoS can be offered. Fuzzy rule-based schedulers are emerging scheduling schemas in Grid computing based on the efficient management of grid resources imprecise state and expert knowledge application to achieve an efficient workload distribution. Given the diverse and usually conflicting nature of the scheduling optimization objectives in grids considering both users and administrators requirements, these strategies can benefit from multi-objective strategies in their knowledge acquisition process greatly. This work suggests the QoS provision in the grid scheduling level with fuzzy rule-based schedulers through multi-objective knowledge acquisition considering multiple optimization criteria. With this aim, a novel learning strategy for the evolution of fuzzy rules based on swarm intelligence, Knowledge Acquisition with a Swarm Intelligence Approach (KASIA) is adapted to the multi-objective evolution of an expert grid meta-scheduler founded on Pareto general optimization theory and its performance with respect to a well-known genetic strategy is analyzed. In addition, the fuzzy scheduler with multi-objective learning results are compared to those of classical scheduling strategies in Grid computing.     

Investigating teachers’ appraisal of unexpected moments and underlying values: an exploratory case in the context of changing mathematics classroom discourse

This article provides an exploratory case study that examines what one teacher indicated as unexpected as she worked to become more purposeful about her classroom discourse practices. We found that she highlighted three areas as being unexpected: (1) aspects of lesson enactment; (2) characteristics of student learning and (3) her own intentionality or purposefulness. We interpret these instances through the lens of the systemic functional linguistics (SFL) appraisal framework in order to understand how she evaluates the instances she highlights and connect these instances to the literature on values in the teaching and learning of mathematics.     

Partially adaptive robust estimation of regression models and applications

This paper provides an accessible exposition of recently developed partially adaptive estimation methods and their application. These methods are robust to thick-tailed or asymmetric error distributions and should be of interest to researchers and practitioners in data mining, agent learning, and mathematical modeling in a wide range of disciplines. In particular, partially adaptive estimation methods can serve as robust alternatives to ordinary regression analysis, as well as machine learning methods developed by the artificial intelligence and computing communities.Results from analysis of three problem domains demonstrate application of the theory.     

Rule learning: Ordinal prediction based on rough sets and soft-computing

This work promotes a novel point of view in rough set applications: rough sets rule learning for ordinal prediction is based on rough graphical representation of the rules. Our approach tackles two barriers of rule learning. Unlike in typical rule learning, we construct ordinal prediction with a mathematical approach, rough sets, rather than purely rule quality measures. This construction results in few but significant rules. Moreover, the rules are given in terms of ordinal predictions rather than as unique values. This study also focuses on advancing rough sets theory in favor of soft-computing. Both theoretical and a designed architecture are presented. The features of our proposed approach are illustrated using an experiment in survival analysis. A case study has been performed on melanoma data. The results demonstrate that this innovative system provides an improvement of rule learning both in computing performance for finding the rules and the usefulness of the derived rules.     

A bicriteria flowshop scheduling with a learning effect

In many situations, a worker’s ability improves as a result of repeating the same or similar tasks; this phenomenon is known as the learning effect. In this paper the learning effect is considered in a two-machine flowshop. The objective is to find a sequence that minimizes a weighted sum of total completion time and makespan. Total completion time and makespan are widely used performance measures in scheduling literature. To solve this scheduling problem, an integer programming model with n² + 6n variables and 7n constraints where n is the number of jobs is formulated. Because of the lengthy computing time and high computing complexity of the integer programming model, the problem with up to 30 jobs can be solved. A heuristic algorithm and a tabu search based heuristic algorithm are presented to solve large size problems. Experimental results show that the proposed heuristic methods can solve this problem with up to 300 jobs rapidly. According to the best of our knowledge, no work exists on the bicriteria flowshop with a learning effect.