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Physical Cyber Social Computing: An early 21st century approach to Computing for Human Experience

Published on Nov 20, 20133575 Views

Amit Sheth

The proper role of technology to improve human experience has been discussed by visionaries and scientists from the early days of computing and electronic communication. Technology now plays an increa

WIMS 2013 - Madrid

Related categories

Data MiningSemantic Web

Chapter list

Physical-Cyber-Social Computing: An early 21st century approach to Computing for Human Experience00:00
Computing for Human Experience - 100:32
Computing for Human Experience - 201:54
Computing for Human Experience - 302:03
Computing for Human Experience - 403:15
Computing for Human Experience - 503:42
Computing for Human Experience - 605:56
Computing for Human Experience - 706:16
Imagine the role of computational techniques in solving big challenges07:50
Grand challenges in the real-world are complex08:41
What has changed now? - 110:51
What has changed now? - 214:08
What has notchanged?15:27
Consider an example of Mark, who is diagnosed with hypertension15:48
Search based approach - 115:59
Search based approach - 217:11
Search based approach - 317:45
Search based approach - 418:08
Search based approach - 518:17
There are many suggestions but less insights that Mark can understand and take action18:26
Solution engine based approach18:44
Search vs. solution engine19:47
What do we need to help Mark?19:55
Physical-Cyber-Social Computing20:48
PCS Computing - 120:53
PCS Computing - 222:16
PCS Computing - 322:18
PCS Computing - 422:30
PCS Computing - 523:25
PCS Computing - 623:31
Increasingly, real-world events are ...23:32
PCS computing operators24:29
Let’s take progressive steps from existing computing paradigms toward PCS computing…27:32
Physical-Cyber Systems - 127:38
Physical-Cyber Systems - 228:05
Physical-Cyber Systems - 328:36
Cyber-Social Systems29:01
Physical-Cyber-Social Systems - 129:53
Physical-Cyber-Social Systems - 229:57
Physical-Cyber-Social Systems - 330:34
Physical-Cyber-Social Systems - 430:41
Physical-Cyber-Social Systems - 530:46
What if?31:28
PCS Computing - 431:42
PCS Computing - 532:06
PCS Computing - 632:11
PCS Computing - 732:19
PCS Computing - 832:37
PCS Computing - 933:03
PCS Computing - 1033:07
PCS Computing - 1133:10
PCS Computing - 1233:26
PCS Computing - 1333:38
PCS Computing - 1433:59
PCS Computing: Scenario of high Blood Pressure (BP) - 134:11
PCS Computing: Scenario of high Blood Pressure (BP) - 234:13
PCS Computing: Scenario of high Blood Pressure (BP) - 334:25
PCS Computing: Scenario of high Blood Pressure (BP) - 434:33
PCS Computing: Scenario of high Blood Pressure (BP) - 535:12
PCS Computing: Scenario of high Blood Pressure (BP) - 635:18
PCS Computing: Scenario of high Blood Pressure (BP) - 735:31
PCS Computing: Scenario of high Blood Pressure (BP) - 835:54
PCS Computing Operators36:06
Homo Digitus (Quantified Self)36:40
The Patient of the Future36:47
Primary challenge is to bridge the gap between data and knowledge37:23
What if we could automate this sense making ability?37:33
Making sense of sensor data37:37
The key ingredient is prior knowledge37:41
Perception Cycle* - 137:57
Perception Cycle* - 238:28
Perception Cycle* - 338:31
Perception Cycle* - 438:36
Semantic Web technology is used to integrate sensor data with prior knowledge on the Web43:46
Prior knowledge on the Web - 144:14
Prior knowledge on the Web - 244:41
Explanation - 144:46
Explanation - 245:16
Explanation - 345:58
Discrimination - 146:09
Discrimination - 246:56
Discrimination - 347:01
Discrimination - 447:04
Risk Score: from Data to Abstraction and Actionable Information47:21
Use of OWL reasoneris resource intensive50:14
Intelligence at the edge50:24
Efficient execution of machine perception51:09
Evaluation on a mobile device - 151:27
Evaluation on a mobile device - 251:53
Semantic Perception for smarter analytics: 3 ideas to takeaway51:58
Application of semantic perception to healthcare…52:29
kHealth52:33
Our Motivation52:34
kHealth - Knowledge-enabled Healthcare52:38
Cardiology Background Knowledge52:40
kHealthKit for the application for reducing ADHF readmission52:50
Explanation in kHealth53:05
Focus in kHealth53:12
kHealth Summary54:01
Pre-clinical usability trial54:26
Other Potential Applications54:36
Demos54:46
PCS Computing for Asthma55:32
Asthma: Severity of the problem55:33
Specific Aims55:40
Massive Amount of Data to Actions56:10
Asthma Example of Actionable Information57:01
PCS Computing Challenges57:24
PCS Computing: Asthma Scenario - 157:39
PCS Computing: Asthma Scenario - 257:45
PCS Computing: Asthma Scenario - 358:01
PCS Computing: Asthma Scenario - 458:05
Personal, Public Health, and Population Level Signals for Monitoring Asthma58:07
Asthma Early Warning Model58:43
Health Signal Extraction to Understanding59:04
PCS Computing for Parkinson’s Disease01:00:31
Parkinson’s Disease (PD): Severity of the problem01:00:34
Massive Amount of Data to Actions - 101:00:41
Massive Amount of Data to Actions - 201:00:58
Symptoms to possible manifestations in sensor observations01:01:04
Feature extraction (accelerometer)01:01:39
Feature extraction (audio)01:01:57
Feature extraction (Compass)01:02:02
Evaluation: run classification algorithms on carefully crafted features from knowledgeof PD01:02:04
Knowledge Based Analytics of PD dataset01:02:09
PCS Computing for Traffic Analytics01:03:19
Traffic management: Severity of the problem01:03:21
Massive Amount of Data to Actions (traffic data analytics)01:03:23
Heterogeneity leading to complementary observations - 101:03:37
Heterogeneity leading to complementary observations - 201:03:37
Heterogeneity leading to complementary observations - 301:03:42
Heterogeneity leading to complementary observations - 401:04:21
Heterogeneity leading to complementary observations - 501:04:28
Heterogeneity leading to complementary observations - 601:04:29
Heterogeneity leading to complementary observations - 701:04:30
Heterogeneity leading to complementary observations - 801:04:33
Heterogeneity leading to complementary observations - 901:04:34
Uncertainty in a Physical-Cyber-Social System01:04:35
Modeling Traffic Events01:05:01
Modeling Traffic Events: Pictorial representation01:05:03
Combining Data and Knowledge Graph - 101:05:17
Combining Data and Knowledge Graph - 201:05:18
Combining Data and Knowledge Graph - 301:05:19
Combining Data and Knowledge Graph - 401:05:20
Combining Data and Knowledge Graph - 501:05:21
Combining Data and Knowledge Graph - 601:05:46
Combining Data and Knowledge Graph - 701:05:48
Declarative knowledge from ConceptNet501:05:49
Three Operations: Complementing graphical model structure - 101:05:51
Three Operations: Complementing graphical model structure - 201:05:52
Three Operations: Complementing graphical model structure - 301:05:52
Three Operations: Complementing graphical model structure - 401:05:54
Three Operations: Complementing graphical model structure - 501:05:55
Enriched Probabilistic Models using ConceptNet501:05:55
PCS Computing for Intelligence - 101:06:42
PCS Computing for Intelligence - 201:06:44
PCS Computing for Intelligence - 301:06:45
Scenario: Physical Cyber Social Threat01:06:46
Scenario (along with the knowledge required to answer them) - 101:06:47
Scenario (along with the knowledge required to answer them) - 201:06:48
PCS computing for detecting threats - 101:06:49
PCS computing for detecting threats - 201:06:50
PCS computing for soldier health monitoring - 101:06:51
PCS computing for soldier health monitoring - 201:06:52
PCS computing for soldier health monitoring - 301:06:53
CPS Current State of Art: Limitations01:06:54
Conclusions - 101:07:35
Conclusions - 201:09:02
A bit more on this topic01:09:19
Kno.e.sis resources01:09:21
Acknowledgements01:09:31
Physical Cyber Social Computing01:09:38
Kno.e.sis in 201201:09:43
http://knoesis.org01:09:49