forecasting presence and availability joe tullio cs8803
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Forecasting Presence and Availability
Joe Tullio
CS8803
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Overview
Why do this? Survey of projects
Precursors/influences Coordinate Awarenex/Work rhythms Learning locations using GPS “Lighter” applications
Augur Current incarnation Evaluation/future plans
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Motivation
Why do this kind of prediction?
Why now?
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Precursors
Media spaces (CRUISER system) Portholes Beard et al – assigned priorities to events
Priority was accorded a level of transparency So meeting scheduling involved overlaying calendars
Worked well enough in the lab, but saw less success in the workplace. Why?
Automatic meeting scheduling tools IM status – focus on current state of availability
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Coordinate (Horvitz et al)
Preceded by Priorities Prioritize incoming notifications Relay to a mobile device if important enough
Location was first determined by idle time Later added input from other sources Calendar, vision, audio levels
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Coordinate (continued)
Intent: Answer broad range of queries“When will X return?”
“When will X be available?”
“Will X attend the meeting?”
“When will X have access to a desktop machine?”
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Coordinate (continued)
Method: collect lots of dataCalendar, computer activity, devices used, email
contents, meeting information, 802.11 location tracking
Estimates of attendance augmented with hand-labeling when necessary
Employee directory establishes professional relationships between users
Construct custom Bayesian networks appropriate to the query
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Example
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Rhythm modeling (Begole et al)
Idea: people exhibit rhythms in their day-to-day work
Capture those rhythms by recording email, IM, phone activity, computer use
Visualize them and attempt to build models representing them
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Example
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Building the models
Expectation maximizationDiscover transitions in activity
Cluster similar periods of inactivity
Refine
Label transitions through simple matchingAround 12 or 1 is lunch
Recurring transitions named after calendar events, if they exist
Location changes named after location, duh
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Other visualizations
Compressed
Gradient
Probabilities
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Privacy
How much to display, and to whom? Ideas:
Expose more over time to simulate familiarization
Expose only what is needed to answer a given question
But how to explain or give context?
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Location Modeling Using GPS (Ashbrook and Starner) Location modeling as opposed to availability Uses?
Encourage serendipitous meetings Intelligent interruption Meeting scheduling
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Step 1: find places
Can’t just give people raw GPS coordinates
Define a place as any location where one spends time t
t chosen arbitrarily here
Places become locationsUse a clustering algorithm to group nearby
places
Also concept of sublocationsRun clustering alg. On points within locations
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Example
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Adding time
All these locations are time-stamped, so…Can identify order of places visited and predict
transitions between places
Markov model – one for each location, transitions to every other location
Currently can predict where one will go next, but not when
Can variance in arrival/departure indicate importance?
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Machine learning
Most of these projects require a large corpus of data with discernable patterns of activityWhat happens when those patterns deviate or
change?
Incorporate learning or user interaction
Broaden classes in accordance with their current fit to the data
Coordinate – include more cases that are ‘relevant’
Rhythms/GPS – Weigh recent data more heavily
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Predicting interruptibility using sensors
Hudson et al
Goal: determine good time to interrupt
Method: record people in their offices(A/V)Self-report interruptibility using ESM (~2/hr)
Manually code situations (602 hours)
Hypothesize which sensors would provide the most information about interruptibility
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Results
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Building models
Simple 2-class classification problem
Try:Decision trees (78.1%)
Naïve Bayes (75.0%)
Adaboost w/decision stumps (76.9%)
Support-vector machines (77.8%)
Predictions improve when tested per-subject as opposed to across subjects
First few sensors account for most of the accuracy:Phone, talk, # of guests, sitting, writing, keyboard