Brian Y. Lim

Mobile phones allow people to keep in touch with others and be easily reachable. However, the increasingly intimate use of smartphones also risks more social disruptions (e.g., in meetings and movie theatres) and work interruptions. This is because current smartphones are not smart enough to comprehensively understand the context of where its owner is, what he is doing, what is socially appropriate, and with whom he can be connected to then, etc.

Therefore, we have developed Laκsa, a mobile app to automatically infer the user’s context for social availability. It uses the rich sensors in smartphones (e.g., GPS, microphone, accelerometer, calendar) together with sophisticated machine learning algorithms to infer contextual cues, such as whether the user is in an impromptu conversation at the office, on an evening run outdoors, or at home listening to music. With this, Laκsa can provide contextually relevant features such as automatically silencing or activating the phone’s ringer in an intelligent and appropriate manner.

Laκsa is also intelligible to communicate with users. Using algorithms to provide explanations, Laκsa helps users to understand what it knows and how it makes inferences, and enables users to share such situational and social understanding with friends and family. Hence, Laκsa uses location and activity to connect (κ) users for social awareness.

Publications

We have published several research papers on using Laκsa to investigate the design of intelligible visualizations of context-awareness and to evaluate the usefulness of intelligilibility.

1. Lim, B. Y., Dey, A. K. 2012.
   Evaluating Intelligibility Usage and Usefulness in a Context-Aware Application
   CMU-HCII Technical Report.
2. Lim, B. Y., Dey, A. K. 2011.
   Investigating Intelligibility for Uncertain Context-Aware Applications.
   In Proceedings of the 13th international conference on Ubiquitous computing (UbiComp ’11). ACM, New York, NY, USA, 415-424. DOI=10.1145/2030112.2030168
3. Lim, B. Y., Dey, A. K. 2011.
   Design of an Intelligible Mobile Context-Aware Application.
   In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI ’11). ACM, New York, NY, USA, 157-166. DOI=10.1145/2037373.2037399

05-344 Applied Machine Learning

Project for course Applied Machine Learning. I used the dataset provided from Kasteren et al. 2008 about activity recognition in a smart home to train a classifer using a decision tree. To test the classifier, I developed this simulator using a Java game engine (Golden T Game Engine).

References

• Golden T Game Engine. www.goldenstudios.or.id . Retrieved 16 December 2008.
• Kasteren, T.L.M., Noulas, A. K., Englebienne, G., Kröse, B.J.A. Accurate Activity Recognition in a Home Setting. In Proc. Ubicomp 08. Seoul, South Korea, 2008.

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Spreadsheet program I did in my 1st semester (Fall 2003) in Cornell for the course COMS 211 Computers and Programming. Coding it was fun since I drew a lot of ideas from ShapeShifter to add extra features, but that took a lot of time.

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[flickr style=”display:inline; float:left; padding-right:10px”]photo:4292632691[/flickr]
My pet project for about 2 years while I was busy in the army, after conceiving an idea ot develop it on May 1, 2001. It is a graphing program, written in Java, which has a main feature of allowing users to enter equations with parameters which can be adjusted to see how the graph would change. ShapeShifter is open sourced under the GPL.
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A program I wrote for the National Junior College Science Department (but unfortunately not used, in the end), meant to allow easy updating of a webpage by just filling out fields in the program and generating the webpage. It writes the content to XML files and then uses XSLT to create the XHTML file.
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