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首页> 外文会议>International Conference on Augmented Cognition >Human Machine Interactions: Velocity Considerations
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Human Machine Interactions: Velocity Considerations

机译：人机相互作用：速度考虑

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Measuring change is increasingly a computational task, but understanding change and its implications are fundamentally human challenges. Successful human/machine teams for streaming data analysis effectively balance data velocity with people's capacity to ingest, reason about, and act upon the data. Computational support is critical to aiding humans with finding what is needed when it is needed. This is particularly evident in supporting complex sensemaking, situation awareness, and decision making in streaming contexts. Herein, we conceptualize human/machine teams as interacting streams of data, generated from the interactions that are core to the human/machine team activity. These streams capture the relative velocities of the human and machine activities, which allows the machine to balance the capabilities of the two halves of the system. We review the known challenges in handling interacting streams that have been distilled in computational systems. And we use this perspective to understand some of the open challenges to designing effective human/machine systems that support the disparate velocities of humans and machines.
机译：衡量变化越来越多地是计算任务，但了解变化及其影响是基本上的人类挑战。成功的人类/机器团队用于流媒体数据分析，有效地平衡数据速度与人们摄取的能力，理由，以及对数据作用。计算支持对于解释人类来说是一种在需要时找到所需的东西。这尤其明显，在支持复杂的传感，情况意识和流媒体上下文中的决策方面尤为​​明显。在此，我们将人类/机器团队概念化为与人类/机器团队活动的核心的交互生成的数据相互作用。这些流捕获了人员和机器活动的相对速度，这使得机器能够平衡系统的两半的能力。我们审查了处理在计算系统中蒸馏的交互流方面的已知挑战。我们使用此视角来了解一些开放的挑战，以设计支持人类和机器的不同速度的有效人机系统。

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