physiological data modeling contest
DESCRIPTION
Physiological Data Modeling Contest. An ICML-2004 Workshop, July 8, 2004. Revelation of Contexts. Context 1 – Annotation 3004 Context 2 – Annotation 5102. Watching TV. Sleep. Revelation of Channels. Explanation of Channels. gsr_average Measures sweat (conductivity across the skin) - PowerPoint PPT PresentationTRANSCRIPT
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Physiological Data Modeling Contest
An ICML-2004 Workshop, July 8, 2004
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Revelation of Contexts
• Context 1 – Annotation 3004
• Context 2 – Annotation 5102
Watching TV
Sleep
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Revelation of Channels
characteristic1 agecharacteristic2 handednesssensor1 gsr_averagesensor2 heat_flux_averagesensor3 near_body_temp_averagesensor4 pedometersensor5 skin_temp_averagesensor6 longitudinal_accelerometer_SADsensor7 longitudinal_accelerometer_averagesensor8 transverse_accelerometer_SADsensor9 transverse_accelerometer_average
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Explanation of Channels
• gsr_average– Measures sweat (conductivity across the skin)– Unit: micro Siemens
• heat_flux_average– Measures heat lost to the environment– Unit: Watts/meter^2
• near_body_temp_average– Measures temperature near armband– Unit: degrees Centigrade
• pedometer– Measures number of steps– Unit: steps
• skin_temp_average– Measures temperature of skin in contact with armband– Unit: degrees Centigrade
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Explanation of Channels
• longitudinal_accelerometer_SAD– Measures Sum of Absolute Differences of vertical acceleration– Unit: g
• longitudinal_accelerometer_average– Measures average of vertical acceleration– Unit: g
• transverse_accelerometer_SAD– Measures Sum of Absolute Differences of horizontal acceleration– Unit: g
• transverse_accelerometer_average– Measures average of horizontal acceleration– Unit: g
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Revelation of Set Distributions
Training Set Distribution
Test Set Distribution
Number of Men 12 21Number of Women 7 9Number of Men Sessions 1380 1635Number of Women Sessions 38 78Number of Sessions with TV 67 72Number of Sessions without TV 976 994Number of TV Minutes 4483 5813Number of non-TV Minutes 168037 191802Number of Sessions with Sleep 236 244Number of Sessions without Sleep 902 942Number of Minutes with Sleep 98532 103666Number of Minutes withouth Sleep 76717 95225
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Brown – Zodiac
• Summary of Approaches
– Approach 1: No Chars
– Approach 2: 1 Char
– Approach 3: 2 Chars
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Brown – Zodiac – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .7 0 .9C ontext 1 (w atc h ing T V ) 0 .6 0 .7C ontext 2 (s leep) 0 .7 0 .7O verall 0 .666666667 0 .766666667
• Approach 1: No Chars
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Brown – Zodiac – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .7 0 .9 0 .6697C ontext 1 (w atc h ing T V ) 0 .6 0 .7 0 .3430C ontext 2 (s leep) 0 .7 0 .7 0 .8007O verall 0 .666666667 0 .766666667 0 .6045
• Approach 1: No Chars
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Brown – Zodiac – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .85 0 .8C ontext 1 (w atc h ing T V ) 0 .6 0 .7C ontext 2 (s leep) 0 .7 0 .7O verall 0 .716666667 0 .733333333
• Approach 2: 1 Char
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Brown – Zodiac – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .85 0 .8 0 .6236C ontext 1 (w atc h ing T V ) 0 .6 0 .7 0 .3430C ontext 2 (s leep) 0 .7 0 .7 0 .8007O verall 0 .716666667 0 .733333333 0 .5891
• Approach 2: 1 Char
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Brown – Zodiac – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .925C ontext 1 (w atc h ing T V ) 0 .6 0 .7C ontext 2 (s leep) 0 .7 0 .7O verall 0 .75 0 .775
• Approach 3: 2 Chars
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Brown – Zodiac – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .925 0 .6217C ontext 1 (w atc h ing T V ) 0 .6 0 .7 0 .3430C ontext 2 (s leep) 0 .7 0 .7 0 .8007O verall 0 .75 0 .775 0 .5885
• Approach 3: 2 Chars
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Zo d ia c
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re
Zodiac 1 : D ec is ion Tree
Zodiac 2 : Log is t ic R egres s ion
Zodiac 3 : S V M
Brown – Zodiac – Summary
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Daimler Chrysler Research And Technology India
• Summary of Approaches
– Approach 1: Raw Data
– Approach 2: PCA
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Daimler Chrysler Research And Technology India – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .6 0 .6C ontext 1 (w atc h ing T V ) 0 .8 0 .8C ontext 2 (s leep) 0 .9 0 .9O verall 0 .766666667 0 .766666667
• Approach 1: Raw Data
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Daimler Chrysler Research And Technology India – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .6 0 .6 0 .4645C ontext 1 (w atc h ing T V ) 0 .8 0 .8 0 .6763C ontext 2 (s leep) 0 .9 0 .9 0 .8761O verall 0 .766666667 0 .766666667 0 .6723
• Approach 1: Raw Data
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Daimler Chrysler Research And Technology India – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .6 0 .6C ontext 1 (w atc h ing T V ) 0 .85 0 .85C ontext 2 (s leep) 0 .92 0 .92O verall 0 .79 0 .79
• Approach 2: PCA
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Daimler Chrysler Research And Technology India – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .6 0 .6 0 .4645C ontext 1 (w atc h ing T V ) 0 .85 0 .85 0 .6253C ontext 2 (s leep) 0 .92 0 .92 0 .7859O verall 0 .79 0 .79 0 .6252
• Approach 2: PCA
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D a im le rC h ry s le r R e s e a rc h A n d T e c h n o lo g y In d ia , B a n g a lo re
0.6763
0.8761
0.4645
0.6723
0.6253
0.7859
0.4645
0.6252
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
1.0000
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re D C R TI 1 : R aw D ata
D C R TI 2 : P C A
Daimler Chrysler Research And Technology India – Summary
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O v e ra ll S c o re
0.6723
0.6252
0.6045
0.5891 0.5885
0.5400
0.5600
0.5800
0.6000
0.6200
0.6400
0.6600
0.6800
D C R TI 1 : R aw D ata D C R TI 2 : P C A Zodiac 1 : D ec is ion Tree Zodiac 2 : Log is t icR egres s ion
Zodiac 3 : S V M
Sco
reLeader Board
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National Library of Medicine
• Summary of Approaches
– Approach 1: mDSB 1
– Approach 2: mDSB 2
– Approach 3: Simple Bayesian
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National Library of Medicine – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9C ontext 1 (w atc h ing T V ) 0 .75 0 .8C ontext 2 (s leep) 0 .75 0 .8O verall 0 .8 0 .833333333
• Approach 1: mDSB 1
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National Library of Medicine – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9 0 .6697C ontext 1 (w atc h ing T V ) 0 .75 0 .8 0 .7049C ontext 2 (s leep) 0 .75 0 .8 0 .7130O verall 0 .8 0 .833333333 0 .6959
• Approach 1: mDSB 1
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National Library of Medicine – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9C ontext 1 (w atc h ing T V ) 0 .75 0 .8C ontext 2 (s leep) 0 .75 0 .8O verall 0 .8 0 .833333333
• Approach 2: mDSB 2
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National Library of Medicine – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9 0 .6697C ontext 1 (w atc h ing T V ) 0 .75 0 .8 0 .7011C ontext 2 (s leep) 0 .75 0 .8 0 .7036O verall 0 .8 0 .833333333 0 .6915
• Approach 2: mDSB 2
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National Library of Medicine – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9C ontext 1 (w atc h ing T V ) 0 .8 0 .75C ontext 2 (s leep) 0 .8 0 .75O verall 0 .833333333 0 .8
• Approach 3: Simple Bayesian
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National Library of Medicine – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9 0 .6697C ontext 1 (w atc h ing T V ) 0 .8 0 .75 0 .7208C ontext 2 (s leep) 0 .8 0 .75 0 .8938O verall 0 .833333333 0 .8 0 .7614
• Approach 3: Simple Bayesian
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N a tio n a l L ib ra ry o f M e d ic in e
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
1.0000
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re
N LM 1: m D S B 1
N LM 2: m D S B 2
N LM 3: S im ple B ay es ian
National Library of Medicine – Summary
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O v e ra ll S c o re
0.7614
0.6959 0.69150.6723
0.62520.6045
0.5891 0.5885
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
N LM 3: S im pleB ay es ian
N LM 1: m D S B 1 N LM 2: m D S B 2 D C R TI 1 : R awD ata
D C R TI 2 : P C A Zodiac 1 :D ec is ion Tree
Zodiac 2 :Log is t ic
R egres s ion
Zodiac 3 : S V M
Sco
reLeader Board
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University of Texas Austin – Saurabh Amin
• Summary of Approach
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University of Texas Austin – Saurabh Amin – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .51 0 .65C ontext 1 (w atc h ing T V ) 0 .689 0 .75C ontext 2 (s leep) 0 .689 0 .8O verall 0 .629333333 0 .733333333
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University of Texas Austin – Saurabh Amin – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .51 0 .65 0 .7591C ontext 1 (w atc h ing T V ) 0 .689 0 .75 0 .7000C ontext 2 (s leep) 0 .689 0 .8 0 .3065O verall 0 .629333333 0 .733333333 0 .5885
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S a u ra b h A m in
0.7000
0.3065
0.7591
0.5885
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re A m in
University of Texas Austin – Saurabh Amin – Summary
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O v e ra ll S c o re
0.7614
0.6959 0.69150.6723
0.62520.6045
0.5891 0.5885 0.5885
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
N LM 3:S im ple
B ay es ian
N LM 1: m D S B1
N LM 2: m D S B2
D C R TI 1 : R awD ata
D C R TI 2 : P C A Zodiac 1 :D ec is ion Tree
Zodiac 2 :Log is t ic
R egres s ion
A m in Zodiac 3 :S V M
Sco
reLeader Board
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CMU – Informedia
• Summary of Approaches
– Approach 1: 50/50
– Approach 2: All Negative
– Approach 3: Semi-Supervised
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CMU – Informedia – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9C ontext 1 (w atc h ing T V ) 0 .75 0 .75C ontext 2 (s leep) 0 .8711 0 .85O verall 0 .857033333 0 .833333333
• Approach 1: 50/50
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CMU – Informedia – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9 0 .6342C ontext 1 (w atc h ing T V ) 0 .75 0 .75 0 .7314C ontext 2 (s leep) 0 .8711 0 .85 0 .9096O verall 0 .857033333 0 .833333333 0 .7584
• Approach 1: 50/50
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CMU – Informedia – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9C ontext 1 (w atc h ing T V ) 0 .7625 0 .75C ontext 2 (s leep) 0 .8834 0 .85O verall 0 .8653 0 .833333333
• Approach 2: All Negative
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CMU – Informedia – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9 0 .6324C ontext 1 (w atc h ing T V ) 0 .7625 0 .75 0 .7310C ontext 2 (s leep) 0 .8834 0 .85 0 .8988O verall 0 .8653 0 .833333333 0 .7541
• Approach 2: All Negative
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CMU – Informedia – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9C ontext 1 (w atc h ing T V ) 0 .75 0 .75C ontext 2 (s leep) 0 .8711 0 .85O verall 0 .857033333 0 .833333333
• Approach 3: Semi-Supervised
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CMU – Informedia – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9 0 .6361C ontext 1 (w atc h ing T V ) 0 .75 0 .75 0 .7375C ontext 2 (s leep) 0 .8711 0 .85 0 .9125O verall 0 .857033333 0 .833333333 0 .7620
• Approach 3: Semi-Supervised
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In fo rm e d ia
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
1.0000
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re
In fo rm edia 1 : S V M B as eline
In form edia 2 : A ll N eg.
In form edia 3 : F ilte r
CMU – Informedia – Summary
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O v e ra ll S c o re
0.7620 0.7614 0.7584 0.7541
0.6959 0.69150.6723
0.62520.6045 0.5891 0.5885 0.5885
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
Sco
reLeader Board
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University of Porto – João Gama
• Summary of Approach
• Approach: Ultra Fast Forrest of Trees
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University of Porto – João Gama – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 0 .85C ontext 1 (w atc h ing T V ) 0 0 .825C ontext 2 (s leep) 0 0 .85O verall #D IV /0 ! 0 .841666667
• Approach: Ultra Fast Forrest of Trees
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University of Porto – João Gama – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 0 .85 0 .6248C ontext 1 (w atc h ing T V ) 0 0 .825 0 .6922C ontext 2 (s leep) 0 0 .85 0 .8357O verall #D IV /0 ! 0 .841666667 0 .7176
• Approach: Ultra Fast Forrest of Trees
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G a m a
0.6922
0.8357
0.6248
0.7176
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re G am a: U F F T
University of Porto – João Gama – Summary
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O v e ra ll S c o re
0.7620 0.7614 0.7584 0.75410.7176
0.6959 0.69150.6723
0.62520.6045 0.5891 0.5885 0.5885
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
Sco
reLeader Board
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Laboratoire de Recherche en Informatique
• Summary of Approaches
– Approach 1: Voting Procedure
– Approach 2: Weighted Voting Procedure
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Laboratoire de Recherche en Informatique – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9C ontext 1 (w atc h ing T V ) 0 .8 0 .8C ontext 2 (s leep) 0 .85 0 .85O verall 0 .85 0 .85
• Approach 1: Voting Procedure
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Laboratoire de Recherche en Informatique – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9 0 .6069C ontext 1 (w atc h ing T V ) 0 .8 0 .8 0 .6875C ontext 2 (s leep) 0 .85 0 .85 0 .8187O verall 0 .85 0 .85 0 .7044
• Approach 1: Voting Procedure
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Laboratoire de Recherche en Informatique – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9C ontext 1 (w atc h ing T V ) 0 .8 0 .8C ontext 2 (s leep) 0 .85 0 .85O verall 0 .85 0 .85
• Approach 2: Weighted Voting Procedure
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Laboratoire de Recherche en Informatique – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9 0 .5825C ontext 1 (w atc h ing T V ) 0 .8 0 .8 0 .6854C ontext 2 (s leep) 0 .85 0 .85 0 .8275O verall 0 .85 0 .85 0 .6985
• Approach 2: Weighted Voting Procedure
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L a b o ra to ire d e R e c h e rc h e e n In fo rm a tiq u e , U n iv e rs ité P a ris -S u d
0.6875
0.8187
0.6069
0.70440.6854
0.8275
0.5825
0.6985
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re LR I 1 : V ot ing
LR I 2 : W eighted V ot ing
Laboratoire de Recherche en Informatique – Summary
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O v e ra ll S c o re
0.7620 0.7614 0.7584 0.75410.7176 0.7044 0.6985 0.6959 0.6915
0.67230.6252
0.6045 0.5891 0.5885 0.5885
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
Sco
reLeader Board
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Smart Signal
• Summary of Approach
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Smart Signal – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .925C ontext 1 (w atc h ing T V ) 0 .6 0 .825C ontext 2 (s leep) 0 .6 0 .85O verall 0 .7 0 .866666667
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Smart Signal – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .925 0 .6614C ontext 1 (w atc h ing T V ) 0 .6 0 .825 0 .7498C ontext 2 (s leep) 0 .6 0 .85 0 .8684O verall 0 .7 0 .866666667 0 .7598
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S m a rt S ig n a l
0.7498
0.8684
0.6614
0.7598
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
1.0000
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re S m art S igna l
Smart Signal – Summary
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Leader Board
?
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Summary: Gender Prediction
G e n d e r
0.7591
0.6697 0.6697 0.6697 0.6697 0.66140.6361 0.6342 0.6324 0.6248 0.6236 0.6217 0.6069
0.5825
0.4645 0.4645
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
Sco
re
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Summary: Context1 (3004 – Watching TV)
C o n te x t 1 (T V )
0.7498 0.7375 0.7314 0.7310 0.7208 0.7049 0.7011 0.7000 0.6922 0.6875 0.6854 0.6763
0.6253
0.3430 0.3430 0.3430
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
Sco
re
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Summary: Context2 (5102 – Sleeping)
C o n te x t 2 (s le e p )
0.9125 0.9096 0.8988 0.8938 0.8761 0.86840.8357 0.8275 0.8187 0.8007 0.8007 0.8007 0.7859
0.7130 0.7036
0.3065
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
1.0000
Sco
re
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O v e ra ll S c o re
0.7620 0.7614 0.7598 0.7584 0.75410.7176 0.7044 0.6985 0.6959 0.6915
0.67230.6252
0.6045 0.5891 0.5885 0.5885
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
Sco
reSummary: Overall ResultsSummary: Overall Results
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Awards
• Honorable Mention: Gender– Saurabh Amin (0.7591)
• Honorable Mention: Context 1 (watching TV)– Smart Signal (0.7498)
• Honorable Mention: Context 2 (sleep)– Informedia 3: Semi-Supervised (0.9125)
• Overall Winner:– Informedia 3: Semi-Supervised (0.7620)
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