• Have a higher GPA than others who do not participate in a similar experience (Penniston, 2011);
• Are 6.9% more likely to graduate with honors (Penniston, 2011);
• Enhance their self-confidence as a direct result of their experience (81% reported via their Student Evalutions, 2011); and
  
• Strengthen their leadership skills development (84% reported via their Student Evalutions, 2011).

Visit The Shriver Center's website to learn more about Service-Learning.  See the attached Fall 2012 Service-Learning Chart to view available service sites (on and off campus, some that have transportation provided).  To secure a placement, you can call The Shriver Center at (410) 455-2493 to set an appointment with a Service-Learning Staff Member.  You may also visit us at The Shriver Center's table at Involvement Fest on Wednesday, 9/5 from 12-3pm.

I am interested in diplomacy, foreign service, international development, and intelligence. Career paths within those fields have a huge spectrum of possibilities, all of which tend to be competitive. I am particularly looking forward to the NSEP service requirement because I will have a guaranteed position within an agency. From that I will be able to better determine my interests and long term goals.

Read more about Coco's research by visiting the link below...

This Friday, August 31, new and returning students are invited to the Office of Undergraduate Education's Open House.

Held from Noon to 2 p.m. in the Academic IV Building (Room 114), the event is a chance to talk about First Year Seminars, The New Student Book Experience, Undergraduate Research, and an Introduction to an Honors University.

MS Thesis Defense

Predicting the Activities of Mobile Phone Users
with Hidden Markov Models

Amey Sane

9:00am Tuesday, 28 August 2012, ITE 325b, UMBC

Mobile phones are ubiquitous and increasingly capable, with sophisticated sensors, network access, significant storage and processing power and access to a wide range of application data. They can improve the range and quality of their services by acquiring and using models of their context, including the activities in which their users are engaged. This thesis explores the use of supervised machine learning techniques for predicting a smartphone user's activities from available sensor data. We have specifically concentrated on applying classifiers and ensembles using hidden markov models for activity recognition. Our classifiers predict a user's current activity from among a set of conceptual activity classes such as sleeping, traveling, playing, working, and chatting/watching TV. We have experimented with and evaluated the effectiveness of different approaches on data collected on Android smartphones by university faculty and students.

Committee: Drs. Tim Finin (chair), Anupam Joshi and Yelena Yesha