基于自监督任务的多源无监督域适应法

针对多源聚合下同时对齐域不变特征较困难而造成分类精度不高的问题, 提出基于自监督任务的多源无监督域适应法. 该方法引入旋转、水平翻转和位置预测3个自监督辅助任务, 通过伪标签性、语义信息的一致性对无标签数据进行自适应的对齐优化. 构建新的优化损失函数, 减少多域公共类别的分类差异. 针对类别不均衡的问题, 基于少样本大权重的原则, 定义动态权重参数, 提高模型的分类性能. 在公开的Office-31、Office-Caltech10 2种基准数据集上, 与现有的主流方法进行实验对比. 实验结果表明, 在类别均衡、不均衡2种情况下, 分类精度最高可以提高6.8%.     

具有隐私保护的车联网空间众包任务分配方法

为了解决传统车联网空间众包中集中式空间众包服务器不可信和易遭受攻击给用户隐私带来极大威胁的问题,提出区块链架构下具有隐私保护的车联网空间众包任务分配方法. 基于区块链技术,设计分布式可信的车联网空间众包系统. 采用多密钥全同态加密算法实现任务分配,支持对不同车辆用户 (密钥) 的密文数据进行任务分配,降低隐私泄露的可能性. 实验分析表明,采用该方法能够有效地保护用户隐私信息,任务分配的计算时间开销与现有研究方法相比下降了34.3%,提高了任务分配的效率.     

移动边缘计算中资源受限的串行任务卸载策略

边缘计算通过在靠近用户的网络边缘侧部署计算和存储资源,使用户可将高延迟、高耗能应用程序卸载到网络边缘侧执行,从而降低应用延迟和本地能耗. 已有的卸载研究通常假设卸载的任务之间相互独立,且边缘服务器缓存有执行任务所需的所有服务. 然而,在真实场景中,任务之间往往存在依赖关系,且边缘服务器因其有限的存储资源只能缓存有限的服务. 为此,提出一种在边缘服务器计算资源和服务缓存有限的约束下,权衡时延和能耗（即成本）的依赖性任务卸载方法. 首先,松弛研究问题中的约束将其转换为凸优化问题;采用凸优化工具求最优解,并用解计算卸载任务的优先级. 然后,按照优先级将任务卸载到成本最小的边缘服务器,若多个依赖任务卸载到不同的边缘服务器,为了使总成本最小,则采用改进粒子群算法求解边缘服务器的最佳传输功率. 最后,为了验证所提方法的有效性,基于真实数据集进行了充分的实验. 实验结果表明,所提方法与其他方法相比能够降低总成本8%~23%.

     

任务驱动法在UG数控加工中的应用

"任务驱动"教学法是一种建立在建构主义教学理论基础上的教学法。通过在UG数控加工课程教学中应用任务驱动教学模式:资讯—决策—计划—实施—检查—评价,达到使学生更好地学习知识、实际应用知识的目的。     

利用Windows的任务计划实现Sybase数据库的自动备份

Sybase的dump命令可以把网络数据库以较小的空间完整地保存下来,而Sybase的iSQL命令行工具能够实现与服务器的连接,向服务器发送T-SQL命令,并允许接受输入文件,然后执行T-SQL命令。Windows 2003 Server的任务计划,可以根据预定的时间进度表,定期或定时地执行所规定的任务,这些任务可以是任何一种可执行文件。     

高职院校计算机专业实训教学的问题及对策

该文针对当前实训教学中存在的众多问题,提出了在高职院校计算机专业实施实训教学应注意的几个关键问题,包括:实训教学的目的、项目的选择、师资条件、教学的组织、辅助资源的建立和考核方法,并给出了可行性实施方案。     

Motion Understanding: Task-Directed Attention and Representations that Link Perception with Action

This short paper outlines my position on a future direction for computational research on visual motion understanding. The direction combines motion perception, visual attention, action representation and computational vision. Due the breadth of literature in these areas, the paper cannot present a comprehensive review of any one topic. The review is a selective one, a selection that attempts to make some particular points. I claim that task-directed attentive processing is a largely unexplored dimension in the computational motion field. I recount in the context of motion understanding a past argument that in order to make vision systems general, attention is one of the components of the strategy. No matter how sophisticated the methods become for extracting motion information from image sequences, it will not be possible to achieve the goal of human-like performance without integrating the optimization of processing that attention provides. Virtually all past surveys of computational models of motion processing completely ignore attention. However, the concept has crept into work over the years in a variety of ways. A second claim is that the biology of attention offers some interesting insights to guide future development. Many computational authors had previously commented that too little is known about how biological vision systems use task-directed attention in motion processing; this is no longer true. Here, I briefly summarize biological evidence that attentive processing affects all aspects of visual perception including motion, and again emphasize that this paper does not do justice to the breadth and depth of the field. New findings provide a critical link between the perception of visual actions and their execution. Together these findings point to a strategy for motion understanding closely related to that presented more than two decades ago.     

适用于应用模式的网格结点选择方法研究

网格环境中的任务调度面临着海量的计算和通信资源环境,所以调度者需要考虑资源的选择问题.传统的资源选择方法一般只考虑计算能力或通信能力的最大化,没有考虑资源的通信模式与应用的匹配问题.本文在Remos研究的基础上,提出了一个完整的基于应用通信模式的网格结点选择算法.算法使用子图同构的辨识方法来判断网格结点的连通关系是否与应用模式相匹配,在满足通信模式约束的前提下,再用贪婪算法来选择计算和通信均较优化的结点.在通用算法的基础上,特别考虑了基于Master-Slave和All-to-All应用模式简化结点选择算法.最后,利用仿真方法,将本文的算法和随机选择法进行了比较,结果表明本文算法选择的网格结点不但满足应用的通信约束,而且性能较优.     

Efficient Task Scheduling for Many Task Computing with Resource Attribute Selection

Many Task Computing（MTC）is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput（with work-stealing）is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.