Computing For Human Experience - Revision history

Admin at 01:41, 11 November 2023

2023-11-11T01:41:19Z

Admin: /* References */

2023-11-11T01:38:40Z

‎References

Admin: /* An Illustrative Example */

2023-11-11T01:38:05Z

‎An Illustrative Example

Admin: /* Semantic Computing as a Starting Point */

2023-11-11T01:34:48Z

‎Semantic Computing as a Starting Point

Jibril: Undo revision 10147 by Jibril (talk)

2017-05-24T17:24:14Z

Undo revision 10147 by Jibril (talk)

Jibril at 17:23, 24 May 2017

2017-05-24T17:23:10Z

Jibril at 19:29, 11 May 2016

2016-05-11T19:29:05Z

Jibril at 19:27, 11 May 2016

2016-05-11T19:27:06Z

Jibril at 19:26, 11 May 2016

2016-05-11T19:26:38Z

Jibril: Added YouTube video for 2010 NCSA Director’s Seminar under the heading "Related"

2016-05-11T19:25:03Z

Added YouTube video for 2010 NCSA Director’s Seminar under the heading "Related"

@@ Line 1: / Line 1: @@
 ===Semantics empowered Sensors, Services, and Social Computing on ubiquitous Web===
-In his influential paper “The Computer for the 21st Century,” Mark Weiser talked about making machines fit the human environment instead of forcing humans to enter the machine’s environment<ref> M. Weiser, “The Computer for the 21st Century,” ACM SIGMOBILE Mobile Computing and Communications Rev., vol. 3, no. 3, 1999, pp. 3–11.</ref>.He noted, “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” Weiser’s vision, outlined two decades ago, led to ubiquitous computing. Now, we must again rethink the relationship and interactions between humans and machines—this time, including a variety of technologies, including computing technologies; communication, social-interaction, and Web technologies; and embedded, fixed, or mobile sensors and devices.
+In his influential paper “The Computer for the 21st Century,” Mark Weiser talked about making machines fit the human environment instead of forcing humans to enter the machine’s environment M. Weiser, “The Computer for the 21st Century,” ACM SIGMOBILE Mobile Computing and Communications Rev., vol. 3, no. 3, 1999, pp. 3–11.He noted, “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” Weiser’s vision, outlined two decades ago, led to ubiquitous computing. Now, we must again rethink the relationship and interactions between humans and machines—this time, including a variety of technologies, including computing technologies; communication, social-interaction, and Web technologies; and embedded, fixed, or mobile sensors and devices.
 {| border = "1" width="250" align="right"
@@ Line 26: / Line 26: @@
 ===Elevating Abstractions That Machines Understand===
-Perception is a key aspect of human intelligence and experience. Elevating machine perception to a level closer to that of human perception will be a key enabler of CHE. In 1968, Richard Gregory described perception as a hypothesis over observation<ref>R. Gregory, “Perceptual Illusions and Brain Models,” Proc. Royal Soc. London B, vol. 171, 1968, pp. 279–296.</ref>. Such hypothesis building comes naturally to people as an (almost) entirely subconscious activity. Humans often interpret the raw sensory observation before recognizing a conscious thought. On the other hand, hypothesis building is often cumbersome for machines. Nevertheless, to integrate human and machine perception, the convergence must occur at this abstraction level, often termed situation awareness. Therefore, regardless of the source, this integration requires a shared framework for communicating and comparing situation awareness.
+Perception is a key aspect of human intelligence and experience. Elevating machine perception to a level closer to that of human perception will be a key enabler of CHE. In 1968, Richard Gregory described perception as a hypothesis over observation R. Gregory, “Perceptual Illusions and Brain Models,” Proc. Royal Soc. London B, vol. 171, 1968, pp. 279–296. Such hypothesis building comes naturally to people as an (almost) entirely subconscious activity. Humans often interpret the raw sensory observation before recognizing a conscious thought. On the other hand, hypothesis building is often cumbersome for machines. Nevertheless, to integrate human and machine perception, the convergence must occur at this abstraction level, often termed situation awareness. Therefore, regardless of the source, this integration requires a shared framework for communicating and comparing situation awareness.
-Perceptual hypotheses represent the semantics, or meaning, of observation. Beginning with raw observation, we find such meaning by leveraging background knowledge of the interaction between observation and possible causes to determine the most likely hypothesis. Previous experience, schooling, and personality account for much of the background knowledge in a single mind. Machines also must leverage background knowledge for effective perception.<ref>R. Bajcsy, “Active Perception,” Proc. IEEE, vol. 76, no. 8, 1988, pp. 996–1005.</ref>So, effective perception requires a framework for representing background knowledge.
+Perceptual hypotheses represent the semantics, or meaning, of observation. Beginning with raw observation, we find such meaning by leveraging background knowledge of the interaction between observation and possible causes to determine the most likely hypothesis. Previous experience, schooling, and personality account for much of the background knowledge in a single mind. Machines also must leverage background knowledge for effective perception.R. Bajcsy, “Active Perception,” Proc. IEEE, vol. 76, no. 8, 1988, pp. 996–1005. So, effective perception requires a framework for representing background knowledge.
 ===From Perception to Semantics===
@@ Line 104: / Line 104: @@
 * the capture of comprehensive personal information (for example, [http://research.microsoft.com/en-us/projects/mylifebits/ MyLifebits]),
-* the collection of massive amounts of interlinked curated data (for example, [http://linkeddata.org/ Linked Data]<ref> C. Bizer, T. Heath, and T. Berners-Lee, [http://linkeddata.org/ Linked Data] – The Story So Far, International Journal on Semantic Web & Information Systems, 5:2,1-22, July 2009.</ref> ),
+* the collection of massive amounts of interlinked curated data (for example, [http://linkeddata.org/ Linked Data] C. Bizer, T. Heath, and T. Berners-Lee, [http://linkeddata.org/ Linked Data] – The Story So Far, International Journal on Semantic Web & Information Systems, 5:2,1-22, July 2009. ),
 * data and information contributed by a community of volunteers (for example, Wikipedia) and the record of social discourse of millions of users on a vast number of topics (for example, Facebook and Twitter), and
 * the collection of observations from sensors in, on, or around humans; around the earth (for example, Hewlett-Packard’s Central Nervous System for the Earth [[http://www.hpl.hp.com/news/2009/oct-dec/cense.html CeNSE]] initiative) and in space.
@@ Line 110: / Line 110: @@
-Semantic computing over such a bewildering variety of data is made possible largely by an agreement on what the data means, represented in a manner that’s formal or informal; explicit or implicit; or static (through a deliberate, expert-driven process), periodic, or dynamic (for example, mining Wikipedia to extract a targeted taxonomy). The key forms in which such agreements are modeled include formal ontologies, folksonomies, taxonomies, vocabularies, and dictionaries. Recently we have started to make rapid strides in creating models and background knowledge from human collaborations (exemplified by hundreds of expert created ontologies), by selectively extracting or mining the Web for facts (as demonstrated by Voquette/Semangix <ref>A. Sheth, C. Bertram, D. Avant, B. Hammond, K. Kochut, and Y. Warke, [http://www.knoesis.org/library/resource.php?id=00193 “Semantic Content Management for Enterprises and the Web,”] IEEE Internet Computing, July-August 2002, pp. 80–87.</ref>) or scientific literature, and by harvesting community created content (as demonstrated by [http://www.mpi-inf.mpg.de/yago-naga/yago/ Yago]  and [https://www.taxonom.com/taxonom Taxonom.com]). This has involved large-scale use of statistical, machine-learning and natural language processing techniques.  Along with domain-specific or thematic conceptual models, temporal and spatial models (ontologies) have taken their rightful place for capturing meaning, especially as we seek to go from keywords and documents to objects, and then to relationships and events.
+Semantic computing over such a bewildering variety of data is made possible largely by an agreement on what the data means, represented in a manner that’s formal or informal; explicit or implicit; or static (through a deliberate, expert-driven process), periodic, or dynamic (for example, mining Wikipedia to extract a targeted taxonomy). The key forms in which such agreements are modeled include formal ontologies, folksonomies, taxonomies, vocabularies, and dictionaries. Recently we have started to make rapid strides in creating models and background knowledge from human collaborations (exemplified by hundreds of expert created ontologies), by selectively extracting or mining the Web for facts (as demonstrated by Voquette/Semangix A. Sheth, C. Bertram, D. Avant, B. Hammond, K. Kochut, and Y. Warke, [http://www.knoesis.org/library/resource.php?id=00193 “Semantic Content Management for Enterprises and the Web,”] IEEE Internet Computing, July-August 2002, pp. 80–87.) or scientific literature, and by harvesting community created content (as demonstrated by [http://www.mpi-inf.mpg.de/yago-naga/yago/ Yago]  and [https://www.taxonom.com/taxonom Taxonom.com]). This has involved large-scale use of statistical, machine-learning and natural language processing techniques.  Along with domain-specific or thematic conceptual models, temporal and spatial models (ontologies) have taken their rightful place for capturing meaning, especially as we seek to go from keywords and documents to objects, and then to relationships and events.
 Such models and associated background knowledge have provided powerful ways to automatically extract semantic metadata or semantically annotate any type of data to associate meaning with the data. A variety of semantic computations aided by pattern extraction, inferencing, logic- and rule-based reasoning, and so on, then provide a range of applications including semantic search, browsing, integration, and analysis. Such applications can lead to insights, decision support, and situational awareness. [http://twitris.knoesis.org/ Twitris] is one such application for extraction of social signals from Twitter.

@@ Line 149: / Line 149: @@
 <br />
 {{#ev:youtube|inNzfPIUs-E}}
 NCSA Director’s Seminar, University of Illinois, Urbana-Champaign
 <br /><br />

@@ Line 118: / Line 118: @@
 To get an idea of some of the capabilities I’ve described, consider a scenario in which a farmer observes an unfamiliar disease on his crop and seeks information to manage it (see Figure 2). He clicks a picture, tags it with keywords “crop” and “blight,” and sends a message seeking more information: “Looks bad, but I don’t know what it is. Any help would be great.”
-[[Image:Figure02.jpg|frame|Figure 2. An example embodying some of the computing for human experience (CHE) promises—helping a farmer in his natural context. The farmer sends a message requesting help with an unknown crop disease. The CHE system analyzes the message, contacts appropriate sources, and returns actionable information, while requiring minimal involvement or technology consciousness from the farmer.]]
+<html><center><img src="https://i.ibb.co/SrDvs62/Figure02.jpg"></center></html>
 A CHE system would analyze the image to help identify the exact crop (for example, sweet corn) and the disease. It would also use location coordinates and related contextual and background domain knowledge, including local weather and soil conditions (for example, to determine whether the disease is northern corn leaf blight). It would analyze the farmer’s message to extract the information-seeking intent and detect an unfavorable sentiment associated with the content.

@@ Line 92: / Line 92: @@
 ===Semantic Computing as a Starting Point===
-At the center of the approach to achieving CHE, as we see it, is semantic computing.  Importance of semantics in dealing with data heterogeneity has been recognized since 1980s, when we saw emergence of conceptual or semantic models. Starting 1990s through early 2000s we saw the use of conceptual models or ontologies for semantic metadata extraction and annotations, for faceted or semantic search, and subsequently semantic analytics <ref>J. Heflin, and J. Hendler. A Portrait of the Semantic Web in Action. IEEE Intelligent Systems 16, 2 (Mar. 2001), 54-59.</ref><ref>A. Sheth and C. Ramakrishnan, [http://www.knoesis.org/library/resource.php?id=00149 Semantic (Web) Technology In Action: Ontology Driven Information Systems for Search, Integration and Analysis], IEEE Data Engineering Bulletin, Special issue on Making the Semantic Web Real, U. Dayal, H. Kuno, and K. Wilkinson, Eds. December 2003.</ref>. Figure 1 shows a contemporary architecture for semantic computing (simplified for brevity). It has four key components: data or resources, models and knowledge, semantic annotation, and semantic analysis or reasoning.
+At the center of the approach to achieving CHE, as we see it, is semantic computing.  Importance of semantics in dealing with data heterogeneity has been recognized since 1980s, when we saw emergence of conceptual or semantic models. Starting 1990s through early 2000s we saw the use of conceptual models or ontologies for semantic metadata extraction and annotations, for faceted or semantic search, and subsequently semantic analytics J. Heflin, and J. Hendler. A Portrait of the Semantic Web in Action. IEEE Intelligent Systems 16, 2 (Mar. 2001), 54-59.A. Sheth and C. Ramakrishnan, [http://www.knoesis.org/library/resource.php?id=00149 Semantic (Web) Technology In Action: Ontology Driven Information Systems for Search, Integration and Analysis], IEEE Data Engineering Bulletin, Special issue on Making the Semantic Web Real, U. Dayal, H. Kuno, and K. Wilkinson, Eds. December 2003. Figure 1 shows a contemporary architecture for semantic computing (simplified for brevity). It has four key components: data or resources, models and knowledge, semantic annotation, and semantic analysis or reasoning.
 [[Image:Figure1.jpg|frame|Figure 1. A contemporary architecture for semantic computing. This architecture supports semantic search and browsing, question answering, and situational awareness. To do this, it analyzes any form of Web, social, or sensor data by extracting metadata resulting in comprehensive semantic annotation. This process is aided by conceptual models and knowledge and by a variety of information-retrieval, statistical, and AI (machine learning and natural-language processing) techniques, at the Web scale. Semantic analysis supported by mining, inferencing and reasoning over annotations support applications.]]
 Whereas semantic computing started with primarily enterprise and then Web data, including business and scientific data and literature, it has expanded to include any type of data (structured, semistructured, unstructured, and multimodal) and massive amounts of Web-accessible resources, including services, sensor, and social data. Here are some impressive examples:

← Older revision		Revision as of 17:24, 24 May 2017
Line 1:		Line 1:
−	===Semantics empowered Sensors, Services, and Social Computing on ubiquitous Web=== ~~[[#Citation\|<sup>[References]</sup>]]~~	+	===Semantics empowered Sensors, Services, and Social Computing on ubiquitous Web===

	In his influential paper “The Computer for the 21st Century,” Mark Weiser talked about making machines fit the human environment instead of forcing humans to enter the machine’s environment<ref> M. Weiser, “The Computer for the 21st Century,” ACM SIGMOBILE Mobile Computing and Communications Rev., vol. 3, no. 3, 1999, pp. 3–11.</ref>.He noted, “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” Weiser’s vision, outlined two decades ago, led to ubiquitous computing. Now, we must again rethink the relationship and interactions between humans and machines—this time, including a variety of technologies, including computing technologies; communication, social-interaction, and Web technologies; and embedded, fixed, or mobile sensors and devices.		In his influential paper “The Computer for the 21st Century,” Mark Weiser talked about making machines fit the human environment instead of forcing humans to enter the machine’s environment<ref> M. Weiser, “The Computer for the 21st Century,” ACM SIGMOBILE Mobile Computing and Communications Rev., vol. 3, no. 3, 1999, pp. 3–11.</ref>.He noted, “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” Weiser’s vision, outlined two decades ago, led to ubiquitous computing. Now, we must again rethink the relationship and interactions between humans and machines—this time, including a variety of technologies, including computing technologies; communication, social-interaction, and Web technologies; and embedded, fixed, or mobile sensors and devices.

← Older revision		Revision as of 17:23, 24 May 2017
Line 1:		Line 1:
−	===Semantics empowered Sensors, Services, and Social Computing on ubiquitous Web===	+	===Semantics empowered Sensors, Services, and Social Computing on ubiquitous Web=== [[#Citation\|<sup>[References]</sup>]]

	In his influential paper “The Computer for the 21st Century,” Mark Weiser talked about making machines fit the human environment instead of forcing humans to enter the machine’s environment<ref> M. Weiser, “The Computer for the 21st Century,” ACM SIGMOBILE Mobile Computing and Communications Rev., vol. 3, no. 3, 1999, pp. 3–11.</ref>.He noted, “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” Weiser’s vision, outlined two decades ago, led to ubiquitous computing. Now, we must again rethink the relationship and interactions between humans and machines—this time, including a variety of technologies, including computing technologies; communication, social-interaction, and Web technologies; and embedded, fixed, or mobile sensors and devices.		In his influential paper “The Computer for the 21st Century,” Mark Weiser talked about making machines fit the human environment instead of forcing humans to enter the machine’s environment<ref> M. Weiser, “The Computer for the 21st Century,” ACM SIGMOBILE Mobile Computing and Communications Rev., vol. 3, no. 3, 1999, pp. 3–11.</ref>.He noted, “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” Weiser’s vision, outlined two decades ago, led to ubiquitous computing. Now, we must again rethink the relationship and interactions between humans and machines—this time, including a variety of technologies, including computing technologies; communication, social-interaction, and Web technologies; and embedded, fixed, or mobile sensors and devices.

@@ Line 141: / Line 141: @@
 <br />
 {{#ev:youtube|inNzfPIUs-E}}
-''2010 NCSA Director’s Seminar, University of Illinois, Urbana-Champaign''
+NCSA Director’s Seminar, University of Illinois, Urbana-Champaign
 ''[http://knoesis.org/amit/ Amit Sheth] is the director of [http://knoesis.org/ Kno.e.sis Center] and the Center of Excellence on Knowledge-Enabled Human-Centered Computing (Knucomp) at Wright State University. He’s also the university’s LexisNexis Ohio Eminent Scholar and an IEEE Fellow.''
 [[User talk:Amit Sheth|© 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.]]

@@ Line 138: / Line 138: @@
 '''Related''':  [http://knoesis.org/?q=node/1803 Keynote on  Computing for Human Experience] ([http://knoesis.org/sites/default/files/ASWC-Sheth-CHE-02-03-09.pptx presentation], [http://knoesis.org/resources/library-resources/files/download/ASWC2008.wmv video])<br>
 A version of this article in [http://www.computer.org/portal/web/csdl/doi/10.1109/MIC.2010.4 IEEE Internet Computing (Jan-Feb 2010, vision issue)]
 ''[http://knoesis.org/amit/ Amit Sheth] is the director of [http://knoesis.org/ Kno.e.sis Center] and the Center of Excellence on Knowledge-Enabled Human-Centered Computing (Knucomp) at Wright State University. He’s also the university’s LexisNexis Ohio Eminent Scholar and an IEEE Fellow.''
 [[User talk:Amit Sheth|© 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.]]