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中国人事科学研究院王通讯:时代急需大数据人才

文 | 王通讯 来自光明日报 (作者系中国人事科学研究院原院长) 微软公司的一位副总裁说:大数据与“云计算”就像一枚钢镚儿的两个面,相辅相成。大数据相当于储有海量信息的信息库;“云计算”相当于计算机和操作系统。大数据与“云计算”二者结合起来,将给世界带来一场深刻的管理技术革命。当然,人才工作也包括在内。 A.让人才培养不再凭直觉 人才培养要靠教育与培训。但是以往一个很大的弊端是,教师不知道学生和学员是不是真正把课听懂了。如果问一声大家懂了吗?一般回答都会说懂了。这里就掩盖了有的学生、学员因为羞于回答不懂而带

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职校云教室建设部署腾创NComputing软硬件一体化终端方案

新型信息化时代的互联网+、大数据、云计算、物联网大背景下,教育信息化的2.0时代已然到来,信息的优化、整合成为当务之急,“云教室”概念顺应而生。云计算教室所有的教学资源都存储在“云端”,通俗讲就是用一个大型资源库取代了每一台电脑有限的存储空间,简化了主机这一功能。只要拥有一个NComputing云终端,老师就可随时随地备课、辅导学生,学生则可以随时随地享受到原来在教室才能进行的听课、答疑。云端教学方案的出现,将有效降低教育信息化投入和维护成本,提高电教和IT资源的利用率,解决教育数据资源的共享和安全问题,降低能耗,符合绿色环保的可持续化社会发展趋势。同时,对教育行业正带来深刻而积极的影响。

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FogROS2 使用 ROS 2 的云和雾机器人的自适应和可扩展平台

FogROS 2: An Adaptive and Extensible Platform for Cloud and Fog Robotics Using ROS 2 Abstract— Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run modern robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power on demand, but tapping into that power from a robot is non-trivial. In this paper, we present FogROS2, an easy-to-use, open-source platform to facilitate cloud and fog robotics that is compatible with the emerging Robot Operating System 2 (ROS 2) standard. FogROS 2 provisions a cloud computer, deploys and launches ROS 2 nodes to the cloud computer, sets up secure networking between the robot and cloud, and starts the application running. FogROS 2 is completely redesigned and distinct from its predecessor to support ROS 2 applications, transparent video compression and communication, improved performance and security, support for multiple cloud-computing providers, and remote monitoring and visualization. We demonstrate in example applications that the performance gained by using cloud computers can overcome the network latency to significantly speed up robot performance. In examples, FogROS 2 reduces SLAM latency by 50%, reduces grasp planning time from 14s to 1.2s, and speeds up motion planning 28x. When compared to alternatives, FogROS 2 reduces network utilization by up to 3.8x. FogROS2, source, examples, and documentation is available at github.com/BerkeleyAutomation/FogROS2.

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