Computation
for the
Corridor
Bringing Social Software to Social
Spaces
Max Van Kleek
Research Qualifying Examination
January 2005
motivation
why public spaces and
circulation routes?
High traffic public spaces
harbor the
greatest opportunity for
chance encounters
that can lead to:
new acquaintances
unplanned meetings
informal collaborations
One of the primary ways
people build networks of
casual acquaintances
within their organization
knowledge workers need to collaborate today
more than ever before
Knowledge worker :
highly skilled participants of an economy where information and
its manipulation are the commodity and the activity.
Examples: researchers, engineers, designers, architects
product developers, resource planners, legal counselors,
financial consultants, teachers, clerks
Contrast with: makers of physical goods or services
P. Drucker (1959)
knowledge workers need to collaborate today
more than ever before
“Coming Age of Social Transformation”
(socioeconomic theory by Peter Drucker, 1959)
Knowledge workers equipped with increasingly
specialized skillsets, while facing broad challenging
problems
Collaborations form around exchanging expertise/
sharing of skillsets; lets workers achieve goals
more efficiently
Collaborations more like “consulting sessions”:
Spontaneous, small, loose-knit and short-term;
Among “familiar strangers”
knowledge workers need to collaborate today
more than ever before
But how do knowledge workers find collaborators?
out of context !
through friends of one’s supervisor
casual social acquaintences
in line at the cafeteria
waiting for the elevator
at the water cooler
IBM - “method to this madness”
knowledge discovery server (2000)
automated “knowledge management”
Informal meetings occur frequently at work
Steelcase Workplace Index Survey - (2002)
977 full-time employees at various “knowledgedriven” companies
– spent 50% of day working away from desks
– Remaining time was spent “collaborating, and
holding impromptu meetings in secondary spaces,
such as hallways, enclaves and water coolers”
– 64% preferred standing, reclining or leaning while
engaged in impromptu meetings than meeting at
their desks
– wished employers provided a greater range of
seating products and meeting spaces that were
conducive to such meetings.
Social proximity correlates with physical proximity
and likelihood of mutual collaboration
– R. Kraut, C. Egido “Patterns for Contact and
Communication in Scientific Research
Collaboration”, ACM CSCW 1988.
Background
Other recent trends breakdown in sense of
community
– Overcrowding
– Telecommuting
– Disjoint workspaces
R. Kraut, C. Egido “Patterns for Contact and
Communication in Scientific Research
Collaboration”, ACM CSCW 1988.
Meeting new people is important
“Strength of Weak Ties”
(sociological theory by Mark Granovetter, 1973):
personal: more opportunities come from one’s “weak
ties” than close friends; “personal success” correlated
with size of social network
organizational: networks of weak ties facilitate
knowledge flow across cliques within organizations;
promotes cohesiveness and organizational memory
yet today, organizations remain
poorly socially connected
How well do you know your CSAIL colleagues?
(In-house validation survey 1 faculty, 1 staff, 8 students, sept 2003)
How many people are you acquainted with on this
floor? On neighboring floors? On other floors?
a) 10-75%: mean 52%; b) < 10%; c) 0-1%,
How often do you like to share links (not-directlywork-related items) with lab colleagues. How do
you do it?
Most: Several times a day. A couple: a few times a
week. One: once a month
1: Word of mouth; 2: IM; 3: e-mail.
How do you disseminate information to the entire
lab? How often?
Rarely, email all-ai, “inappropriate”; Paper posters
social software to the rescue?
Supplement “accidental” f2f interactions with various software
Instant messaging
Portholes
IBM Knowledge Discovery Server
Ambient Awareness
Shared Media Spaces
Montage
But… the desktop is the wrong place for
social software
Desktop : Work Context
Competes with productivity
for focus of attention
Information overload
Too many distractions already
Ties users to their desks
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Workers need to take breaks regularly for their own
health
MIT RSI guidelines: 1-2 mins every 15 minutes
5-10 minutes every two hours
Provide a good opportunity for social activity
Social software belongs
in social spaces.
applications
k:info: billboard/
screen saver
k:info, the “smart” billboard
(user)
I think the
User wants
Online sources
cameras, microphones
motion sensors
You are all
wrong. She
wants to
know about
free
I think the
User wants
Breaking
News #32
It’s
Monday.
Give user a
break!
other perceptual inputs
interaction history
knowledge sources and
recommenders
display
schedule
serendipity:
making k:info
more social
skinni:
an ‘information kiosk’
architecture
ontogen: an
ontology language
for metaglue
interfaces
distinctive touch:
QuickTime™ and a
identifying users
TIFF (LZW) decompressor
are needed to see this picture.
by gesture
>> min(times)
1.0290
1.0310
0.4060
2.2330
1.5410
1.0930
0.5180
0.3540
1.1390
1.3990
2.4711
1.3355
0.9367
0.5284
1.4328
1.5674
2.9120
1.7490
1.3070
0.6900
1.8130
1.9180
1.1927
1.6609
4.3841
2.1382
0.3453
2.2351
>> mean(times)
1.3309
1.1913
0.9380
2.4735
>> max(times)
2.0140
1.3330
1.5030
2.8690
>> std(times)
1.2539
0.3209
0.3313
0.7660
>> mean(mean(times))
1.4206
>> vstd(times)
0.6467
< 5 seconds
training
doodles
feature
extraction
build a
model/train
classifier
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trained
classifier
“max!”
building a dt classifier
feature extraction
dean rubine: specifying gestures by example
13 unistroke features: 11 geometric, 2 time
dt extractor
rubine extr
description
distfv
f8
total euclidean distance traversed by a stroke
bboxfv2
f3
dimension of bounding box of a stroke
f4fv
f4
stroke bounding box aspect ratio
startendfv
f5
euclidean distance between start and end pts of a
stroke
f1fv
f1,f2,f6,f7
sine and cosine of start and end angles of a
stroke
f9fv
f9
sum of angle traversed by the stroke
f10fv
f10
sum of absolute angle traversed by the stroke
(“curviness”)
f11fv
f11
sum of squared angles traversed by stroke
(“jagginess”)
f12fv
f12
max instantaneous velocity within a stroke
f13fv
f13
total time duration of a stroke
training set: hiragana character set
train set:
45 classes
10 ex each
1-5 strokes
test set:
45 classes
5 ex each
strokewise ML simplest multistroke
generative model
represent each class
as separate sequences
of states, each representing
a stroke.
Each state thus has an
associated parametric
distribution over
the values of that stroke’s
feature vector
strictly encodes our
previous assumption that
strokes of the same class
always arrive in the same
order... otherwise, we’d
need an HMM.
strokewise ML - 1 gaussian per stroke
performance with individual features
features
l-o-o performance
test performance
distfv
0.5422
0.3467
bboxfv2
0.8067
0.6756
f4fv
0.5156
0.4267
startendfv
0.6067
0.4400
f1fv
0.8889
0.7511
f9fv
0.6511
0.5822
f10fv
0.5467
0.4222
f11fv
0.4311
0.3333
f12fv
0.2756
0.1289
f13fv
0.6378
0.4178
strokewise ML - 1 gaussian per stroke
performance with multiple features
features
l-o-o performance
test performance
f9fv, bboxfv2, startendfv
0.9733
0.9156
f1fv, startendfv, f9fv, distfv 0.9644
0.8889
f1fv, f9fv, distfv, startendfv 0.9778
0.9644
0.9711
0.8800
all combined: distfv,
bboxfv2, f4fv, startendfv,
f1fv, f9fv, f10fv, f12fv,
f13fv
fisher linear discriminant - (1-dimensional)
OVA (one-versus-all):
train C FLD binary classifiers on the fvs
evaluate each one on the test point
+1 if it gets the label right, 0 otherwise / (C*N)
features
l-o-o performance
test performance
distfv
0.9422
0.8311
bboxfv2
0.9356
0.8711
f4fv
0.8444
0.7556
startendfv
0.9600
0.880
f1fv
0.9244
0.9022
f9fv
0.8711
0.7644
f10fv
0.8467
0.5778
f11fv
0.7867
0.5556
f12fv
0.8467
0.7067
f13fv
0.9333
0.8400
fisher linear discriminant combined features
(warning: figures are a bit misleading; we’ll
describe why in the next section)
features
l-o-o performance
test performance
bbox2, f12fv, startendfv
0.9822
0.8489
f1fv, f9fv, startendfv
0.9533
0.9733
f1fv, f12fv, distfv,
startendfv, bboxfv2
0.9867
0.9156
f1fv, f9fv, f11fv, distfv,
startendfv
0.9778
0.9644
support vector machines OSU SVM Toolkit for Matlab [ http://www.ece.osu.edu/~maj/osu_svm/ ]
training took too long - no l-o-o
features
linear k test perf.
quad k test perf
distfv
0.9790
0.9790
bboxfv2
0.9789
0.9789
f4fv
0.9784
0.9784
startendfv
0.9778
0.9778
f1fv
0.9831
0.9831
f9fv
0.9778
0.9778
f10fv
0.9778
0.9778
f11fv
0.9778
0.9778
f12fv
0.9774
0.9775
f13fv
0.9778
0.9778
all combined
0.9923
0.9923
comparison
k-nearest-neighbors - simple, most sensitive
to choice of feature extractors
sequential ML - simple to estimate, strictly
requires stroke order
fisher linear discriminant (1d) - performed well
support vector machines (lin, quad kernel) outperformed other methods, took significant
training time
rejection
knn - greedily chooses k nearest neighbors
strokewise ML - chooses largest log likelihood
 choose thresholds empirically using l-o-o
validation (in theory, tricky in practice soft thresholds difficult to manage)
FLD and SVMs - gauge ‘specificity’ of discriminants
by measuring performance as follows:
+1 iff all C FLDs/SVMs are correct
0 otherwise
=> “strict criterion”
Speech-based
interaction
Searching for specific items via existing
SKINNI GUI too slow;
Allow users to simply ask the kiosk to
find what they are looking for:
“Where is Patrick Winston’s Office?”
“How do I get to the Kiva Seminar Room?”
“When is the Theory of Computation Seminar?”
Challenges:
Speech
Understood
Speech
Error
Speech
Overall
Touchscreen
Best
3s
10s
3s
4s
Worst
9s
25s
25s
19s
Avg
5.22s
16.78s
9.11s
7.33s
S.D.
0.92s
5.13s
6.24s
3.18s
What causes fragmented / localized awareness and
a breakdown in sense of community?
Lack of regular interpersonal contact
Such as when working remotely
Inadequate channels of social communication
Overcrowding
QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture.
QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture.
Re-designing the workplace
Recent trends in “creative workplace” designs have
increased allocation to transient spaces and
avenues
Moving away from static office-block layout from
1950’s that maximized personal territory
ickTime™ and a TIFF (Uncompressed) dec ompres sor are needed to s ee this pic ture.
QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture.
.. to semi-private arrangements with an emphasis on
small-group meeting areas for spontaneous meetings,
lounges, tea kitchens, and intersections for major circulation
routes throughout the building
Ok-net
Extends physical architecture by providing
digital services to these spaces to
improve social well-connectedness
information dissemination
n-way social communications
augment face-to-face encounters
awareness across time + physical distance