Teachable interfaces can empower end-users to attune machine learning systems to their idiosyncratic characteristics and environment by explicitly providing pertinent training examples. While facilitating control, their effectiveness can be hindered by the lack of expertise or misconceptions. We investigate how users may conceptualize, experience, and reflect on their engagement in machine teaching by deploying a mobile teachable testbed in Amazon Mechanical Turk. Using a performance-based payment scheme, Mechanical Turkers (N = 100) are called to train, test, and re-train a robust recognition model in real-time with a few snapshots taken in their environment. We find that participants incorporate diversity in their examples drawing from parallels to how humans recognize objects independent of size, viewpoint, location, and illumination. Many of their misconceptions relate to consistency and model capabilities for reasoning. With limited variation and edge cases in testing, the majority of them do not change strategies on a second training attempt.
Naval Research Laboratory（美国海军研究实验室）；University of Wisconsin–La Crosse（威斯康星大学拉克罗斯分校）；University of Washington（华盛顿大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
Perceived Performance of Webpages In the Wild: Insights from Large-scale Crowdsourcing of Above-the-Fold QoE
显然，没有人喜欢体验质量（QoE）差的网页。被感知为慢或快是Web应用程序整体感知QoE的关键要素。虽然已经投入了大量的努力来优化Web应用程序（无论是在工业界还是学术界），但在表征网页加载过程的哪些方面真正影响人类最终用户对页面“速度”的感知方面，并没有太多的工作。在本文中，我们提出了“SpeedPerception”，一个大规模的网络性能众包框架，专注于了解以上的倍（ATF）网页内容的感知加载性能。我们的最终目标是创建免费的开源基准数据集，以推进对人类如何感知网页加载过程的系统分析。在我们使用互联网零售商500强（IR 500）网站（https：//github.com/pahammad/speedperception）进行的“SpeedPerception”研究的第1阶段中，我们发现，当比较两个网页的速度时，常用的导航指标（如“onLoad”和“Time To First Byte（TTFB）”）无法（匹配不到60%）代表大多数人的感知。我们提出了一个简单的基于3变量的机器学习模型，可以更好地解释大多数最终用户的选择（具有$87 \pm 2\%$准确性）。此外，我们的研究结果表明，最终用户评估网页的相对感知速度所需的时间远远小于其“visualComplete”事件的时间。
Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach
University of Alabama（阿拉巴马大学）；University of Maryland, College Park（马里兰大学学院公园分校）；Stony Brook University（石溪大学）；University of Florida（佛罗里达大学）；Johns Hopkins University（约翰霍普金斯大学）
