A machine learning approach for lighting perception analysis via crowdsourcing

Minoda, Yuki; Ohama, Iku; Muramoto, Eiichi

doi:10.1145/2800835.2800969

Cited by 1 publication

(1 citation statement)

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“…Because crowdsourcing can deliver large quantities of data quickly and machine learning can identify often unseen groupings in the data, it is natural to combine the two. For example, Minoda, et al [9] used Amazon mTurk to collect preferred lighting levels of images within a room from a wide range of users of different ethnic backgrounds. Their study was able to determine estimated age and ethnicity of users based on their responses.…”

Section: Introductionmentioning

confidence: 99%

Toward Predicting Secure Environments for Wearable Devices

Walter

Riley

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Wearable devices have become more common for the average consumer. As devices need to operate with low power, many devices use simplified security measures to secure the data during transmission. While Bluetooth, the primary method of communication, includes certain security measures as part of the format, they are insufficient to fully secure the connection and the data transmitted. Users must be made aware of the potential security threats to the information communicated by the wearable, as well as be empowered and engaged to protect it. In this paper, we propose a method of identifying insecure environments through crowdsourced data, allowing wearable consumers to deploy an application on their base system (e.g., a smart phone) that alerts when in the presence of a security threat. We examine two different machine learning methods for classifying the environment and interacting with the users, as well as evaluating the potential uses for both algorithms.

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Section: Introductionmentioning

confidence: 99%