83 citations · 128 across the 9 of their papers we have counts for
9 papers
Sasha: Creative Goal-Oriented Reasoning in Smart Homes with Large Language Models
Evan King, Haoxiang Yu, Sangsu Lee +1
Smart home assistants function best when user commands are direct and well-specified (e.g., "turn on the kitchen light"), or when a hard-coded routine specifies the response. In mo…
iDML: Incentivized Decentralized Machine Learning
Haoxiang Yu, Hsiao-Yuan Chen, Sangsu Lee +3
With the rising emergence of decentralized and opportunistic approaches to machine learning, end devices are increasingly tasked with training deep learning models on-devices using…
"Get ready for a party": Exploring smarter smart spaces with help from large language models
Evan King, Haoxiang Yu, Sangsu Lee +1
The right response to someone who says "get ready for a party" is deeply influenced by meaning and context. For a smart home assistant (e.g., Google Home), the ideal response might…
Guidelines for Artifacts to Support Industry-Relevant Research on Self-Adaptation
Danny Weyns, Ilias Gerostathopoulos, Barbora Buhnova +16
Artifacts support evaluating new research results and help comparing them with the state of the art in a field of interest. Over the past years, several artifacts have been introdu…
Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices
Haoxiang Yu, Jie Hua, Christine Julien
With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the "smartness" of those devices to address daily n…
Personal Data Gentrification
Juan Luis Herrera, Javier Berrocal, Jose Garcia-Alonso +5
We live in an era in which the most valued services are not paid for in money, but in personal data. Every day, service providers collect the personal information of billions of in…