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Computation for the Corridor
Bringing Social Software to Social Spaces
Max Van KleekResearch Qualifying ExaminationJanuary 2005
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motivationwhy public spaces and
circulation routes?
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High traffic public spaces harbor thegreatest opportunity for chance encountersthat can lead to:
new acquaintances unplanned meetings
informal collaborations
One of the primary wayspeople build networks of casual acquaintances within their organization
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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)
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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
“Coming Age of Social Transformation” (socioeconomic theory by Peter Drucker, 1959)
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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”
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Steelcase Workplace Index Survey - (2002)977 full-time employees at various “knowledge-driven” 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.
Informal meetings occur frequently at work
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– R. Kraut, C. Egido “Patterns for Contact and Communication in Scientific Research Collaboration”, ACM CSCW 1988.
Social proximity correlates with physical proximityand likelihood of mutual collaboration
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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.
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“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
Meeting new people is important
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yet today, organizations remain poorly socially connected
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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-directly-work-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 month1: 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
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social software to the rescue?
Instant messagingPortholesIBM Knowledge Discovery ServerAmbient AwarenessShared Media SpacesMontage
Supplement “accidental” f2f interactions with various software
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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
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
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Social software belongs in social spaces.
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applications
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k:info: billboard/screen saver
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(user)
knowledge sources andrecommenders
I think the User wants
I think the User wants
BreakingNews #32
It’s Monday.
Give user a break!
You are all wrong. She
wants to know about
free
cameras, microphones motion sensors
other perceptual inputs
Online sources
interaction history displayschedule
k:info, the “smart” billboard
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serendipity:making k:infomore social
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skinni:an ‘information kiosk’
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architecture
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ontogen: an ontology language for metaglue
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interfaces
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distinctive touch:identifying usersby gesture
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>> min(times)
1.0290 0.4060 1.5410 0.5180 0.3540 1.1390 1.39901.0310 2.2330 1.0930
>> mean(times)
1.3309 0.9380 2.4711 0.9367 0.5284 1.4328 1.5674 1.1913 2.4735 1.3355
>> max(times)
2.0140 1.5030 2.9120 1.3070 0.6900 1.8130 1.9180 1.3330 2.8690 1.7490
>> std(times) 1.2539 0.3313 1.1927 4.3841 2.1382 0.3453 2.2351 0.3209 0.7660 1.6609
>> mean(mean(times))
1.4206 >> vstd(times)
0.6467
< 5 seconds
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QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
trainingdoodles
featureextraction
build amodel/train
classifier
building a dt classifier
trainedclassifier
“max!”
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feature extractiondean rubine: specifying gestures by example13 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
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training set: hiragana character set
train set:45 classes10 ex each1-5 strokes
test set:45 classes5 ex each
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strokewise ML -
simplest multistrokegenerative model
represent each class as separate sequencesof states, each representinga stroke.
Each state thus has an associated parametric distribution overthe values of that stroke’sfeature vector
strictly encodes our previous assumption that strokes of the same class always arrive in the same order... otherwise, we’d need an HMM.
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strokewise ML - 1 gaussian per stroke
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
performance with individual features
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strokewise ML - 1 gaussian per stroke
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
all combined: distfv, bboxfv2, f4fv, startendfv, f1fv, f9fv, f10fv, f12fv, f13fv
0.9711 0.8800
performance with multiple features
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fisher linear discriminant - (1-dimensional)OVA (one-versus-all):train C FLD binary classifiers on the fvsevaluate 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
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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
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support vector machines -
OSU SVM Toolkit for Matlab [ http://www.ece.osu.edu/~maj/osu_svm/ ]
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
training took too long - no l-o-o
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comparisonk-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
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rejection
knn - greedily chooses k nearest neighborsstrokewise ML - chooses largest log likelihood
choose thresholds empirically using l-o-ovalidation (in theory, tricky in practice -soft thresholds difficult to manage)
FLD and SVMs - gauge ‘specificity’ of discriminantsby measuring performance as follows:
+1 iff all C FLDs/SVMs are correct0 otherwise=> “strict criterion”
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Speech-basedinteraction
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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?”
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Challenges:
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Speech Understood
Speech
Error
Speech Overall
Touchscreen
Best 3 s 10s 3s 4 s
Worst 9s 25s 25s 19s
Avg 5.22s 16.78s 9.11s 7.33s
S.D. 0.92s 5.13s 6.24s 3.18s
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What causes fragmented / localized awareness anda breakdown in sense of community?
Lack of regular interpersonal contact
Such as when working remotely
Inadequate channels of social communication
Overcrowding
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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
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Re-designing the workplace
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.. to semi-private arrangements with an emphasis onsmall-group meeting areas for spontaneous meetings,lounges, tea kitchens, and intersections for major circulation routes throughout the building
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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