Our World is Socio-technical
•
1 recomendación•285 vistas
A talk given at the annual Computer Science for High School Teachers event at Victoria University of Wellington. I presented on some basics of the World Wide Web and why it's worth to preserve it, our work on non-expert tools to populate semantically enriched content, a current project to identify NZ native birds based on their calls that involves citizen science and contemporary deep learning using TensorFlow, a project that investigates the impact of online citizen science on the development of science capabilities of primary school children, and my collaboration with Adam Grener from the School of English, Film, Theater and Media Studies at VUW with whom I am working on computational tools for the literature studies.
Recomendados
Recomendados
Más contenido relacionado
La actualidad más candente
La actualidad más candente (20)
Similar a Our World is Socio-technical
Similar a Our World is Socio-technical (20)
Más de Markus Luczak-Rösch
Más de Markus Luczak-Rösch (12)
Último
Último (20)
Our World is Socio-technical
- 1. Our World is Socio-technical Markus Luczak-Roesch | @mluczak Senior Lecturer in Information Systems School of Information Management Victoria University of Wellington
- 2. Humans in the information age • IT project management • Business process design • Application development • User experience design • Business and systems analysis Data insights Information Management
- 3. Today’s road ahead • From Dickens to data science http://www.morguefile.com • Citizen scientists in the classroom • What is the World Wide Web and why should we preserve it?
- 4. The World Wide Web
- 8. The World Wide WebThe Web - a decentralized hypermedia system Basic client-server architecture Hypertext gateway to represent databases as hypertext
- 9. The REST is historyThe REST is historyThe REST is history
- 10. The REST is history The REST is history The REST is history The REST is history
- 11. What is REST? • Representational State Transfer • generic architectural style for network-based systems • architectural style of the WorldWide Web • literature: Roy Thomas Fielding. 2000. Architectural Styles and the Design of Network-Based Software Architectures. Ph.D. Dissertation. University of California, Irvine. AAI9980887.
- 12. REST principles RepresentationalState Transfer 1. client-server 2. stateless 3. caching 4. uniform interface 1. resource identification 2. manipulation of representations 3. self-descriptive messaging 4. Hypermedia 5. layered system 6. code-on-demand
- 13. HTTP • Hypertext Transfer Protocol • transfer data between Web servers and clients • transport protocol is TCP • textbased • literature: R. Fielding, J. Gettys, J. Mogul, H. Frystyk, L. Masinter, P. Leach und T. Berners-Lee. Hypertext Transfer Protocol - HTTP/1.1. RFC 2616, http://www.ietf.org/rfc/rfc2616.txt
- 14. HTTP • stateless • request-response t Client Server Request Response TCP lifecycle Establish connection Terminate connection
- 15. HTTP GET / HTTP/1.1 User-Agent: Mozilla/5.0 … Firefox/10.0.3 Host: markus-luczak.de:80 Accept: */* HTTP/1.1 200 OK Server: Apache/2.0.49 Content-Language: en Content-Type: text/html Content-length: 2990 <!DOCTYPE html> <html xml:lang="en" … Client Server
- 16. Markup languages • most prominent example: HTML (HyperText Markup Language) • to structure Web documents
- 17. What’s the problem with HTML (& XML & databases & …)? Apple <Apple> <Pear>
- 18. Data silos
- 19. The Semantic Web vision “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.“ Berners-Lee, Hendler, and Lassila, 2001.
- 20. Meatadata • Data about data • describe content • in the best case metadata is machine processable §Author §Title §Date §… §Species: Android §Height… Content Metadata
- 21. Web of Linked Data RDF RDF RDF RDF Links
- 23. M. Luczak-Rösch,R. Heese. Linked Data Authoring forNon-Experts.In proceedings of LDOW 2009, co-located with World Wide Web Conference, Madrid, Spain. of the loomp OCA interface elements ubset he ap- esign: e per- d the s. We f text n MS n dif- high- strat- n the extual s, we each (e.g., l con- e pro- p be- A BC Fig. 8. loomp screenshot – authoring with annotation elements 4.2. Effectiveness of OCA design: Usability As the core motivation and purpose for loomp was Fig. 3. Core elements of the loomp Domain Ontology mashup fragment loomp content data data:fragment4 Frankfurt data: fragment3 data:mashup1 data:fragment5 data:fragemet6 data:mashup2 loomp annotation data rdf:type dbp:uri3 dbp:uri1 Frankfurtrdf:label rdf:type M. Luczak- Roesch foaf:name M. Luczak- Roesch rdf:_3 rdf:_1 loomp:contains Ext. Linked Data (e.g. DBpedia) dbp:uri1 Frankfurt/Main dbp:uri2 Frankfurt (Oder) dbp:uri3 city loomp: annotation9 Frankfurt M. Luczak- Roesch loomp vocabulary Country loomp: annotation10 City dbp:uri3 foaf:Person rdf:_2 rdf:_2 rdf:_1 1 2 3 4 external ontology foaf:Person foaf:name Symbolic links (expressing identity) RDF relationships data:resource7 loomp:hasRDFa full-text as XHTML+ RDFa snippet 5 6 7 Fig. 4. Selected elements of the loomp content model with example encoding for content and annotations typed as loomp:Fragment and loomp:Mashup, respectively (cf. Figure 3). ple, the shared fragment4 is linked via rdf:_1 to mashup1 and via rdf:_3 to mashup2. Fragments Markus Luczak-Rösch,Ralf Heese, Adrian Paschke,"FutureContent Authoring",In Nodilities – The Magazineof the Semantic Web, Issue11, pp.17-18, 2010. Populating the Semantic Web
- 24. “Dresden University” correct meaningful 54 1416 “Light and heating are turned off” Hinze, A.,Heese, R., Luczak-Rösch, M., & Paschke, A. (2012). Semanticenrichmentbynon-experts: usabilityof manual annotation tools. In The SemanticWeb–ISWC 2012 (pp. 165- 181). Springer Berlin Heidelberg. Semantics for the rest of us?
- 25. Citizen scientists in the classroom
- 26. What is online citizen science?
- 27. From Galaxy Zoo to the Zooniverse
- 28. The online citizen science workflow n words n words k slices of n words (e.g. n=1,000) ... 1 2 3 k Abel Magwitch Joe Gargery, Mrs Joe Gargery, Pip Joe Gargery, Mrs Joe Gargery, Pip + Lexicon of characters(can alsobe a list of other entitiessuch as placesor phrases) - net - stat Directed network Dynamicnetworkvisualization source texts from Project Gutenberg Matched information • Abel Magwitch • Joe Gargary • Mrs Joe Gargary • Pip Matched information • Abel Magwitch automatic/semi-automatic methodsto detectmatch entities in text, topicdetection, sentiment analysis, part-of-speech tagging) configure n words n words k slices of n words (e.g. n=1,000) ... 1 2 3 k Abel Magwitch Joe Gargery, Mrs Joe Gargery, Pip Joe Gargery, Mrs Joe Gargery, Pip + Lexicon of characters(can alsobe a list of other entitiessuch as placesor phrases) - - Directed network Dynamicnetworkvisualization source texts from Project Gutenberg Matched information • Abel Magwitch • Joe Gargary • Mrs Joe Gargary • Pip Matched information • Abel Magwitch automatic/semi-automatic methodsto detectmatch entities in text, topicdetection, sentiment analysis, part-of-speech tagging) configure n words n words k slices of n words (e.g. n=1,000) ... 1 2 3 k Abel Magwitch Joe Gargery, Mrs Joe Gargery, Pip Joe Gargery, Mrs Joe Gargery, Pip + Lexicon of characters(can alsobe a list of other entitiessuch as placesor phrases) - network - statistic Directed network Dynamicnetworkvisualization source texts from Project Gutenberg Matched information • Abel Magwitch • Joe Gargary • Mrs Joe Gargary • Pip Matched information • Abel Magwitch automatic/semi-automatic methodsto detectmatch entities in text, topicdetection, sentiment analysis, part-of-speech tagging) configure n words n words k slices of n words (e.g.n=1,000) ... 1 2 3 k Abel Magwitch Joe Gargery, Mrs Joe Gargery, Pip Joe Gargery, Mrs Joe Gargery, Pip + Lexicon of characters(can alsobe a list of other entitiessuch as placesor phrases) - network visuali - statistics and ot Directed network Dynamicnetworkvisualization source texts from Project Gutenberg Matched information • Abel Magwitch • Joe Gargary • Mrs Joe Gargary • Pip Matched information • Abel Magwitch automatic/semi-automatic methodsto detectmatch entities in text, topicdetection, sentiment analysis,part-of-speech tagging) configure n words n words k slices of n words (e.g. n=1,000) ... 1 2 3 k Abel Magwitch Joe Gargery, Mrs Joe Gargery, Pip Joe Gargery, Mrs Joe Gargery, Pip + Lexicon of characters(can alsobe a list of other entitiessuch as placesor phrases) - network vis - statistics an Directed network Dynamicnetworkvisualization source texts from Project Gutenberg Matched information • Abel Magwitch • Joe Gargary • Mrs Joe Gargary • Pip Matched information • Abel Magwitch automatic/semi-automatic methodsto detectmatch entities in text, topicdetection, sentiment analysis, part-of-speech tagging) configure Online Citizen Science Platform Machine Learning Scientific Results Citizen scientists
- 29. Our project: Detecting NZ native birds using AI • Recorded sound around Zealandia • Segment the recordings and upload them to Zooniverse for people to decide if they have bird calls or not (and if they have, what bird is it) • Use the data from Zooniverse to train and test our AI
- 30. Hanny’s Voorwerp Galaxy Zoo [2007] Green Pea Galaxies Galaxy Zoo [2007] Yellow Balls Milky Way [2009] Circumbinary Planet Ph1b Planet Hunters [2012] Convict Worm Seafloor Explorer [2012] Spanish Flu Operation War Diaries [2014] Serendipitous discoveries through talk
- 31. Upcoming project: Understanding the impact of online citizen science participation on the development of science capabilities of primary age children.
- 32. From Dickens to Data Science
- 33. Dickens and the Serial Novel Form All fourteen of Dickens’scompleted novels were published serially in weekly or monthly installments.
- 34. From “Sketches” to Novels 1836-37 1864-65 “I have endeavoured in the progress of this Tale, to resist the temptation of the current Monthly Number, and to keep a steadier eye upon the general purpose and design.” Preface to Martin Chuzzlewit (1844)
- 35. Managing Characters Dickens’ Working Notes for His Novels. Edited by Harry Stone, U of Chicago Press, 1987.
- 36. TIC approach applied to Victorian novels n words n words k slices of n words (e.g. n=1,000) ... 1 2 3 k Abel Magwitch Joe Gargery, Mrs Joe Gargery, Pip Joe Gargery, Mrs Joe Gargery, Pip + Lexicon of characters(can alsobe a list of other entitiessuch as placesor phrases) - network visualisations - statistics and other measures Directed network Dynamicnetworkvisualization source texts from Project Gutenberg Matched information • Abel Magwitch • Joe Gargary • Mrs Joe Gargary • Pip Matched information • Abel Magwitch automatic/semi-automatic methodsto detectmatch entities in text, topicdetection, sentiment analysis, part-of-speech tagging) configure analyze
- 38. Information-theoretic properties of literature Pickwick Our Mutual Friend
- 39. Not-so-distant Reading Distant Reading “tackles literary problems by scientific means: hypothesis-testing, computational modeling, quantitative analysis....understanding literature not by studying particular texts, but by aggregating and analyzing massive amounts of data.”[1] [1]Shulz, Kathryn. “What is Distant Reading?” New York Times, 24 June2011.
- 40. An new interpretative practice?
- 42. ● At the School of Information Management we focus on ○ humans in the information age ○ data insights ● Topics at the intersection of ○ computer science (e.g. data management, data analytics, software development) ○ behavioral science (e.g. organizational decision making, social networks) ○ social science (organizational processes, inequalities) Thanks to my collaborators • Dr Adam Grener (Dickens project) • Emma Fenton (Dickens project) • Tom Goldfinch (Dickens project) • Isabel Parker (Dickens project) • Victor Anton (NZ bird identification) • Jacob Woods (NZ bird identification) • Dr Dayle Anderson (Citizen science in educ.) • Dr Cathal Doyle (Citizen science in educ.) • …