Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Building a sensor network of mobile phones
Proceedings of the 6th international conference on Information processing in sensor networks
IEEE Pervasive Computing
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Virtual trip lines for distributed privacy-preserving traffic monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
The pothole patrol: using a mobile sensor network for road surface monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
Micro-Blog: sharing and querying content through mobile phones and social participation
Proceedings of the 6th international conference on Mobile systems, applications, and services
Participatory sensing: applications and architecture
Proceedings of the 8th international conference on Mobile systems, applications, and services
Darwin phones: the evolution of sensing and inference on mobile phones
Proceedings of the 8th international conference on Mobile systems, applications, and services
ParkNet: drive-by sensing of road-side parking statistics
Proceedings of the 8th international conference on Mobile systems, applications, and services
Improving energy efficiency of location sensing on smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
MoVi: mobile phone based video highlights via collaborative sensing
Proceedings of the 8th international conference on Mobile systems, applications, and services
A survey of mobile phone sensing
IEEE Communications Magazine
Enabling micro-entertainment in vehicles based on context information
Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Mobile apps: it's time to move up to CondOS
HotOS'13 Proceedings of the 13th USENIX conference on Hot topics in operating systems
Text text revolution: a game that improves text entry on mobile touchscreen keyboards
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
PreHeat: controlling home heating using occupancy prediction
Proceedings of the 13th international conference on Ubiquitous computing
Accurate and privacy preserving cough sensing using a low-cost microphone
Proceedings of the 13th international conference on Ubiquitous computing
Detecting driver phone use leveraging car speakers
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Demo: Tracking transit with EasyTracker
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
The Journal of Machine Learning Research
Medusa: a programming framework for crowd-sensing applications
Proceedings of the 10th international conference on Mobile systems, applications, and services
CrowdHelp: application for improved emergency response through crowdsourced information
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
BlueEye: a system for proximity detection using bluetooth on mobile phones
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Proceedings of the 4th Conference on Wireless Health
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A growing number of mobile apps are exploiting smartphone sensors to infer user behavior, activity, or context. Inference requires training using labeled ground truth data. Obtaining labeled data for new apps is a "chicken-egg" problem. Without a reasonable amount of labeled data, apps cannot provide any service. But until an app provides useful service it is not worth installing and has no opportunity to collect user data. This paper aims to address this problem. Our intuition is that even though users are different, they exhibit similar patterns on certain sensing dimensions. For instance, different users may walk and drive at different speeds, but certain speeds will indicate driving for all users. These common patterns could be used as "seeds" to model new users through semi-supervised learning. We prototype a technique to automatically extract the commonalities to seed personalized inference models for new users. We evaluate the proposed technique through example apps and real world data.