Daniel Perry
University of Washington
22 Papers
74 Citations
Daniel Perry is an academic researcher from University of Washington. The author has contributed to research in topics: Usability & Thermostat. The author has an hindex of 11, co-authored 22 publications. Previous affiliations of Daniel Perry include University of California, Berkeley.
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Papers
How people use thermostats in homes: A review
TL;DR: A review of the current state of thermostats, evaluating their effectiveness in providing thermal comfort and energy savings, and identifying areas for further improvement or research is provided in this paper, where the authors suggest research needed to design and especially test with users, that can provide more comfortable and economical indoor environments.
374
VizDeck: self-organizing dashboards for visual analytics
Alicia Key,Bill Howe,Daniel Perry,Cecilia Aragon +3 more
- 20 May 2012
TL;DR: VizDeck automatically recommends appropriate visualizations based on the statistical properties of the data and adopts a card game metaphor to help organize the recommended visualizations into interactive visual dashboard applications in seconds with zero programming.
189
Usability of residential thermostats: Preliminary investigations
TL;DR: In this article, the usability of residential thermostats has been investigated and a measurement protocol was developed to quantitatively distinguish usability among five different types of thermostat models, including programmable, programmable and non-programmable.
105
Energy efficiency and the misuse of programmable thermostats: The effectiveness of crowdsourcing for understanding household behavior
TL;DR: This paper used Amazon Mechanical Turk, an online crowdsourcing service, to investigate thermostat settings and behavior in households and found that 57% of households were occupied nearly all the time, limiting the potential energy savings.
Statistical affect detection in collaborative chat
Michael Brooks,Katie Kuksenok,Megan K. Torkildson,Daniel Perry,John J. Robinson,Taylor Jackson Scott,Ona Anicello,Ariana Zukowski,Paul W. R. Harris,Cecilia Aragon +9 more
- 23 Feb 2013
TL;DR: A pipeline of natural language processing and machine learning techniques that can be used to build automated classifiers of affect in chat logs are presented and can successfully identify many commonly occurring types of affect.
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