Automator是苹果公司为他们的Mac OS X系统开发的一款软件。 只要通过点击拖拽鼠标等操作就可以将一系列动作组合成一个工作流,从而帮助你自动的(可重复的)完成一些复杂的工作。Automator还能横跨很多不同种类的程序,包括:查找器、Safari网络浏览器、iCal、地址簿或者其他的一些程序。它还能和一些第三方的程序一起工作,如微软的Office、Adobe公司的Photoshop或者Pixelmator等。

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Deep learning has penetrated all aspects of our lives and brought us great convenience. However, the process of building a high-quality deep learning system for a specific task is not only time-consuming but also requires lots of resources and relies on human expertise, which hinders the development of deep learning in both industry and academia. To alleviate this problem, a growing number of research projects focus on automated machine learning (AutoML). In this paper, we provide a comprehensive and up-to-date study on the state-of-the-art AutoML. First, we introduce the AutoML techniques in details according to the machine learning pipeline. Then we summarize existing Neural Architecture Search (NAS) research, which is one of the most popular topics in AutoML. We also compare the models generated by NAS algorithms with those human-designed models. Finally, we present several open problems for future research.

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Robotic systems for retail have gained a lot of attention due to the labor-intensive nature of such business environments. Many tasks have the potential to be automated via intelligent robotic systems that have manipulation capabilities. For example, empty shelves can be replenished, stray products can be picked up or new items can be delivered. However, many challenges make the realization of this vision a challenge. In particular, robots are still too expensive and do not work out of the box. In this paper, we discuss a work-in-progress approach for enabling power-on-and-go robots in retail environments through a combination of active, physical sensors and passive, artificial sensors. In particular, we use low-cost hardware sensors in conjunction with machine learning techniques in order to generate high-quality environmental information. More specifically, we present a setup in which a standard monocular camera and Bluetooth low-energy yield a reliable robot system that can immediately be used after placing a couple of sensors in the environment. The camera information is used to synthesize accurate 3D point clouds, whereas the BLE data is used to integrate the data into a complex map of the environment. The combination of active and passive sensing enables high-quality sensing capabilities at a fraction of the costs traditionally associated with such tasks.

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Robotic systems for retail have gained a lot of attention due to the labor-intensive nature of such business environments. Many tasks have the potential to be automated via intelligent robotic systems that have manipulation capabilities. For example, empty shelves can be replenished, stray products can be picked up or new items can be delivered. However, many challenges make the realization of this vision a challenge. In particular, robots are still too expensive and do not work out of the box. In this paper, we discuss a work-in-progress approach for enabling power-on-and-go robots in retail environments through a combination of active, physical sensors and passive, artificial sensors. In particular, we use low-cost hardware sensors in conjunction with machine learning techniques in order to generate high-quality environmental information. More specifically, we present a setup in which a standard monocular camera and Bluetooth low-energy yield a reliable robot system that can immediately be used after placing a couple of sensors in the environment. The camera information is used to synthesize accurate 3D point clouds, whereas the BLE data is used to integrate the data into a complex map of the environment. The combination of active and passive sensing enables high-quality sensing capabilities at a fraction of the costs traditionally associated with such tasks.

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