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Elyse McManus

dblp:144/5355 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 44% User interface design and tools · 44% Usability and user experience research · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Ubiquitous computing and smart environments
smart home		 0.212014
Practical trigger-action programming in the smart home · CHI 2014
User interface design and tools › end-user development
trigger-action programming		 0.212014
Practical trigger-action programming in the smart home · CHI 2014
Usability and user experience research › usability evaluation
usability testing		 0.112014
Practical trigger-action programming in the smart home · CHI 2014

Methods — techniques the papers use, named apart from their topics

usability test · 0.2online study · 0.2dataset analysis · 0.2
YearPublicationVenuePosition
2014 Practical trigger-action programming in the smart home
abstract
We investigate the practicality of letting average users customize smart-home devices using trigger-action ("if, then") programming. We find trigger-action programming can express most desired behaviors submitted by participants in an online study. We identify a class of triggers requiring machine learning that has received little attention. We evaluate the uniqueness of the 67,169 trigger-action programs shared on IFTTT.com, finding that real users have written a large number of unique trigger-action interactions. Finally, we conduct a 226-participant usability test of trigger-action programming, finding that inexperienced users can quickly learn to create programs containing multiple triggers or actions.
Blase Ur, Elyse McManus, Melwyn Pak Yong Ho, Michael L. Littman
 CHI2