控制性学习条件下网络外语教学环境创建刍议

网络教学环境的创建及其在外语教学中的运用越来越成为外语界关注的焦点。在控制环境下建构多媒体网络教学环境的手段，充分调动学生的主动性和参与性，从而有助于提高学习者的语言水平及语用能力，加速其文化及社会知识的建构。     

深度视觉语音生成研究进展与展望

为了进一步推进深度学习技术驱动的视觉语音生成相关科学问题的研究进展,阐述了视觉语音生成的研究意义与基本定义,并深入剖析了该领域面临的难点与挑战;在此基础上,介绍了目前视觉语音生成研究的现状与发展水平,基于生成框架的区别对近期主流方法进行了梳理、归类和评述;最后探讨视觉语音生成研究潜在的问题和可能的研究方向。     

用卷积神经网络分类最大稳定极值区域实现汉字区域定位

获取对应笔画级连通区的最大稳定极值区域,实施形态学闭操作融合相距较近的最大稳定极值区域,融合后最大稳定极值区域对应的单个汉字区域;利用灰度共生矩阵描述最大稳定极值矩形区域的纹理信息,将其作为卷积神经网络的输入,卷积神经网络对最大稳定极值区域进行分类,过滤非汉字部分;利用最大稳定极值区域颜色直方图的Bhattacharyya距离等特征对最大稳定极值区域进行聚类,同一类最大稳定极值区域组合得到汉字文本候选区域;再次利用卷积神经网络对候选文本区域进行分类,过滤非文本部分,剩余的就是定位到的汉字文本区域。实验结果表明,该算法对于汉字区域定位具有良好的效果。     

稀疏贝叶斯学习框架下的扩展目标雷达关联成像

传统的关联成像方法未考虑复杂扩展目标的结构信息,在高分辨成像时的应用受到限制,为此提出一种自适应结构配对稀疏贝叶斯学习方法。该方法在稀疏贝叶斯学习的框架内针对扩展目标建立一种结构配对层次化高斯先验模型,然后采用变分贝叶斯期望-最大化算法交替进行目标重构和参数优化。该方法将某一信号分量的重构与周围信号分量联系起来,并能在迭代过程中自适应地调整表征各信号分量相关性的参数。实验结果表明,该方法针对扩展目标可以有效地进行高分辨成像。     

作业名层次化聚类算法预测作业运行时间

预测作业的运行时间有益于提升系统的调度性能,而聚类有助于训练出更好的预测模型。传统的聚类算法很难将相似的作业名聚类,为了将相似的作业更好地聚类,通过分析其组成成分的语义重要性,构建字母-结构-数字的作业名层次化聚类算法。以两台超级计算机的真实数据为例,实验结果发现,应用此算法聚类后的数据训练模型的预测精度相较传统方法有一定的提升,整体预测精度为70%～80%。     

如何提高在线学习者的自我调节能力

与传统课堂教学相比,网络教学环境独有的特点对学习者提出了更高的要求:要求学习者不仅要掌握知识、技能,更要学会学习,管理自己的能力,进行有意义、高效的学习,使其努力指向目标并最终实现目标。本文结合师范生公共必修课《现代教育技术》在线网络课程的教学实践,有效跟踪学习者的学习行为活动,分析学习者的学习状态、学习风格,以及对学习行为的自我调节能力,总结摸索出一些培养并提高学习者自我调节能力的指导性策略,希望能对广大网络学习者有一定的借鉴意义。     

Undergraduate disarmament and nonproliferation education: gaps,opportunities, and new approaches

ABSTRACT

Despite living in a nuclear-weapon state, young Americans are generally ill-informed about weapons of mass destruction (WMD) and their means of control. The result is both widespread apathy toward nonproliferation and disarmament decision making among the general public and a looming personnel crisis within government sectors that enact policy in these domains. Considering that 67 percent of high school graduates in the United States go on to pursue a bachelor’s degree, exposing more undergraduates to nonproliferation and disarmament issues could contribute to addressing both of these challenges. The present study analyzes how these issues are already being taught at select US colleges and universities and explores ways to introduce them to more students that align with current priorities in higher education, such as interdisciplinary learning, digital humanities, and data-science learning. It also proposes concrete steps that the WMD policy community can take to help institutions of higher education integrate these topics more broadly into their curricula. The anticipated result is greater support for education in this important issue area across different stakeholders in academia, as well as increased engagement with these critical issues among a more diverse population of young people.     

On valuing appreciating human assets in services

This paper derives optimal policies for when to hire, train, and lay off employees in service organizations when worker productivity appreciates deterministically or stochastically under constant, monotonic, and cyclic service demand. The model, an extension of the machine replacement problem from the engineering economy literature, uses infinite horizon linear programming to produce optimal personnel planning strategies and dual prices that characterize the workforce's economic value. The effect of employee attrition is also considered. In its valuation of human assets, unlike pure accounting approaches that only try to measure, this paper proposes an approach to capture the interaction between firm decisions pertaining to human resources and human resource value. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

新疆高校预科汉语教学中有关问题的调查与思考

课时的安排,教材的使用,课程的合理设置,是保证教学任务顺利实施的前提。长期以来,新疆各高校预科汉语教学对此一直没有一个统一的认识,严重制约了该学科的发展。作者对全疆部分院校民族预科学生进行了问卷调查,其分析结果为新疆预科教学改革提供一定的参考。     

Sequential learning versus no learning in Bayesian regression models

We consider two regression models: linear and logistic. The dependent variable is observed periodically and in each period a Bayesian formulation is used to generate updated forecasts of the dependent variable as new data is observed. One would expect that including new data in the Bayesian updates results in improved forecasts over not including the new data. Our findings indicate that this is not always true. We show there exists a subset of the independent variable space that we call the “region of no learning.” If the independent variable values for a given period in the future are in this region, then the forecast does not change with any new data. Moreover, if the independent variable values are in a neighborhood of the region of no learning, then there may be little benefit to wait for the new data and update the forecast. We propose a statistical approach to characterize this neighborhood which we call the “region of little learning.” Our results provide insights into the trade‐offs that exist in situations when the decision maker has an incentive to make an early decision based on an early forecast versus waiting to make a later decision based on an updated forecast. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 532–548, 2014