Web
                Science and the Mind


Sponsored by:
m 
Programme (last updated July 7 2014)


Summer School in Cognitive Sciences 2014

 

WEB SCIENCE AND THE MIND

 

JULY 8 - 18 2014

Universite du Québec à Montréal
Room DS-R510 (street level)

Pavillon J.-A De Sève
320 Sainte-Catherine St. East
Montreal, Canada



Registration: http://www.summer14.isc.uqam.ca/page/inscription.php?lang_id=2


Cognitive Science and Web Science have been converging in the study of cognition
as distributed within and between brains, minds and databases on the web.
 

Twitter Hash Tag for Tweets: #webscimind
Blog for live blogging during talks (questions/comments welcome): http://webscimind.blogspot.ca


The four themes of the Summer Institute are (click to view):

(1) The Social Web

(2) The Data Web

(3) The Extended Mind?

(4) The Global Brain?

 




FULL PROGRAMME


- - - - -     
TUESDAY, JULY 8
    - - - - -

 



9-10am Registration and Welcome

10-11am
Towards a Global Brain: the Web as a Self-organizing, Distributed Intelligence --  VIDEO

FRANCIS HEYLIGHEN, Vrije Universiteit Brussel, ECCO - Evolution, Complexity and Cognition research group

Overview & Readings -- Blog Discussion

 

 

Coffee Break 11am to 11:30am


11:30-12:30pm

Mapping the Brain Connectome --   VIDEO
ALAN EVANS, Montreal Neurological Institute & McGill University, Biomedical Engineering
Overview & Readings
--  Blog Discussion



Lunch time 12:30-2pm

 


2-3pm
Web Impact on Society --  VIDEO
LES CARR
, University of Southampton, Web Science
Overview & Readings
-- Blog Discussion


1 hour break to allow time for 15-minute walk to Notman House, 51 Sherbooke west, for keynote  by the founder of Web Science, Dame Wendy Hall

4-5:30am

Web Science: It's All in the Mind  --   VIDEO
DAME WENDY HALL, University of Southampton, Electronics & Computer Science
Overview & Readings -- Blog Discussion

- - - - -      WEDNESDAY, JULY 9    - - - - -

 

9-10am

Open Science and the Web --  VIDEO
TONY HEY, Microsoft Research Connections
Overview & Readings
-- Blog Discussion



10-11am

Scholarly Big Data: Information Extraction and Data Mining -- (TO COME)

LEE GILES, Pennsylvania State University, Information Sciences and Technology
Overview & Readings --
Blog Discussion

Coffee Break 11am to 11:30am

 

11:30-12:30


New Models of Scholarly Communication for Digital Scholarship --  VIDEO

STEPHEN GRIFFIN University of Pittsburgh, School of Information Science
Overview & Readings -- Blog Discussion

Lunch time 12:30pm to 2pm

 

2-3pm

Transformations in Scholarly Communication in the Digital World --  VIDEO

VINCENT LARIVIERE, Universite de Montreal, Ecole de bibliotheconomie et des sciences de l'information

Overview & Readings -- Blog Discussion

 

3-4pm

Web Impact Metrics for Research Assessment --  VIDEO
KAYVAN KOUSHA, University of University of Wolverhampton, Statistical Cybermetrics
Overview & Readings
-- Blog Discussion

 

Coffee Break 4pm to 4:30pm

 

4:30pm

Summary and discussion of day's sessions


5:30-8pm

Poster Session and Cocktail

 

 


 

 

- - - - -      THURSDAY, JULY 10    - - - - -

 

9-10am

Humanexus: Envisioning Communication and Collaboration -- VIDEO
KATY BORNER, Indiana University, Department of Information and Library Science
Overview & Readings -- Blog Discussion

 

10 am to 11am

Visualizing Dynamic Interactions --  VIDEO

JEAN-DANIEL FEKETE, Institut National de Recherche en Informatique et Automatique (INRIA) Saclay - ile-de-France

Overview & Readings -- Blog Discussion


Coffee Break 11am to 11:30am

 

11:30-12:30

Visual Tools for Interacting with Large Networks -- VIDEO

CHARLES-ANTOINE JULIEN, Mcgill University, School of Information Studies

Overview & Readings -- Blog Discussion


Lunch time 12:30pm to 2pm

 

2-3pm

Collaborative Innovation Networks
PETER GLOOR, MIT Center for Collective Intelligence -- VIDEO
Overview & Readings
-- Blog Discussion

 

3-4pm
Network Ready Research: The Role of Open Source and Open Thinking
-- (TO COME)
CAMERON NEYLON, PLOS (Public Library of Science)
Overview & Readings
-- Blog Discussion

 

Coffee Break 4pm to 4:30pm

 

4:30pm

Summary and discussion of day's sessions

 

 

- - - - -      FRIDAY, JULY 11    - - - - -

 

9-10am

Learning Along with Others -- VIDEO
ROBERT GOLDSTONE, Psychological and Brain Sciences, Indiana University

 Overview & Readings -- Blog Discussion


10-11am

Enculturated Cognition -- (TO COME)
RICHARD MENARY
, University of Macquarie, Philosophy
Overview & Readings
  -- Blog Discussion


Coffee Break 11am to 11:30am

 

11:30-12:30

Social and Semantic Web: Adding the Missing Links -- VIDEO

FABIEN GANDON, INRIA Research Center of Sophia-Antipolis

Overview & Readings --  Blog Discussion
Lunch time 12:30pm to 2pm

 

2pm to 4pm

Optional meetings for students

 

 

Coffee Break 4pm to 4:30pm

 

4:30pm

Summary and discussion of day's sessions

 

 

- - - - -      MONDAY, JULY 14    - - - - -

 

9-10am

Bursts, Cascades, and Time Allocation -- VIDEO
ADILSON MOTTER, Northwestern University, Dynamics of Complex Systems and Networks Group

Overview & Readings -- Blog Discussion

 

10-11am

 Controllability and Observability of Complex Systems -- VIDEO
YANG-YU LIU, Northeastern University, Center for Complex Network Research, Physics Department

Overview & Readings --  Blog Discussion
Coffee Break 11am to 11:30am

 

11:30-12:30
Semantic Web Data Mining -- VIDEO

PETKO VALTCHEV, UQÀM, Informatique
Overview & Readings -- Blog Discussion

Lunch time 12:30pm to 2pm

 

2-3pm

You Can't Hide: Predicting Personal Traits in Social Media -- VIDEO
JENNIFER GOLBECK, University of Maryland

Overview & Readings -- Blog Discussion

3-4pm

Collective Memory in Wikipedia -- VIDEO
SIMON DeDEO, Indiana University, Santa Fe Institute
Overview & Readings -- Blog Discussion


 

Coffee Break 4pm to 4:30pm

 

4:30pm

Summary and discussion of day's sessions

 

 

- - - - -      TUESDAY, JULY 15    - - - - -

 

9-10am

Challenges on the Emerging Web of Data -- VIDEO
JIM HENDLER, Rensselaer Polytechnic Institute, Department of Computer Science

 Overview & Readings -- Blog Discussion

10-11am

Foraging in the World, Mind and Online -- VIDEO
PETER TODD, Indiana University, Department of Psychological and Brain Sciences

Overview & Readings -- Blog Discussion

Coffee Break 11am to 11:30am

 

11:30-12:30
Macrocognition: Situated versus Distributed -- VIDEO

BRYCE HUEBNER, Georgetown University, Department of Philosophy

Overview & Readings -- Blog Discussion

 

Lunch time 12:30pm to 2pm

 

2-3pm

Visual Analytics for Discovering Network Structure Beyond Communities -- VIDEO
TAKASHI NISHIKAWA, Northwestern University, Physics & Astronomy

Overview & Readings  -- Blog Discussion


3-4pm

Analogies between interconnected and clustered networks -- VIDEO
FILIPPO RADICCHI, Indiana University,  School of Informatics and Computing

Overview & Readings -- Blog Discussion

Coffee Break 4pm to 4:30pm

 

4:30pm

Summary and discussion of day's sessions

 


 

- - - - -      WEDNESDAY, JULY 16    - - - - -

 

9-10am

Web Semantics -- VIDEO

HARRY HALPIN, University of Edinburgh, Institute of Communicating and Collaborating Systems, School of Informatics

Overview & Readings  -- Blog Discussion

 

10-11am

Extended Mentality: What It Is and Why It Matters -- VIDEO
MARK ROWLANDS, University of Miami, Department of Philosophy
Overview & Readings
-- Blog Discussion

Coffee Break 11am to 11:30am

 

11:30-12:30

Transactive Memory and Distributed Cognitive Ecologies -- VIDEO

JOHN SUTTON, Macquarie University, Department of Cognitive Science
Overview & Readings
-- Blog Discussion

Lunch time 12:30pm to 2pm

 

2-3pm

Knowledge Mining in Heterogeneous Information Networks -- VIDEO

JIAWEI HAN, University of Illinois at Urbana-Champaign, Department of Computer Science
Overview & Readings
-- Blog Discussion

3-4pm

What is Cognition, and How Could it be Extended? -- VIDEO

ROBERT RUPERT, U Colorado & U Edinburgh, Department of Philosophy
Overview & Readings
  -- Blog Discussion

Coffee Break 4pm to 4:30pm

 

4:30pm

Summary and discussion of day's sessions



    7:30pm -10:00pm Special Satellite Event:

 Animal Rights & Animal Law

Droit animal & les droits des animaux 

Chair/Présidente: Élise Desaulniers   
Desaulniers, É (2013)
Vache à lait: Dix mythes de l'industrie laitière et (avec Sophie Gaillard & Martin Gibert) Manifeste pour une évolution du statut juridique des animaux dans le Code civil du Québec
1.    MARK ROWLANDS, Univ Miami, Philosophy
Animal rights: A no-brainer -- VIDEO   (in English) + DISCUSSION 
Animal rights: All that matters. Hodder. 2013
2.     VALÉRY GIROUX Univ Montréal, Centre de recherche éthique
Droits légaux fondamentaux pour tous les êtres sensibles -- VIDEO (en français) + DISCUSSION
Les droits fondamentaux des animaux: une approche anti-spéciste (2012)

3.    MARTINE LACHANCE UQÀM, Science juridique
Le statut juridique de l'animal au Québec: la reconnaissance de sa sensibilité est-elle enfin possible? -- VIDEO (en français) + DISCUSSION

La reconnaissance juridique de la nature sensible de l’animal : du gradualisme français à l’inertie québécoise.
Rev Barreau du Québec 72, 2013

Sponsored by - Parrainné par: UQÀM Groupe de recherche international en droit animal GRIDA GRIDA  UQÀM Institut d'Été Web Science and the Mind   websicmind   KARA Kébek Animal Rights Association    animal rights



- - - - -      THURSDAY, JULY 17    - - - - -

 

9-10am

Collective Intelligence: What is it?  How can we measure it?  And increase it? -- VIDEO
THOMAS MALONE, MIT Sloan School of Management

Overview & Readings -- Blog Discussion



10-11am

Domains and Dimensions of Group Cognition -- VIDEO
GEORG THEINER, Villanova University, Department of Philosophy

Overview & Readings -- Blog Discussion

 

Coffee Break 11am to 11:30am

 

11:30-12:30

The Promises and Pitfalls of Latent Attribute Inference -- VIDEO
DEREK RUTHS, McGill University, Computer Science
Overview & Readings
  -- Blog Discussion

Lunch time 12:30pm to 2pm

 

2-3pm

Socio-Technical Epistemology -- VIDEO

JUDITH SIMON, Institut fuer Technikfolgenabschuetzung und Systemanalyse (ITAS)
Overview & Readings
-- Blog Discussion

3-4pm

*Applying Data Mining to Real-Life Crime Investigation -- (TO COME)

BENJAMIN FUNG, McGill University
Overview & Readings
-- Blog Discussion


 

Coffee Break 4pm to 4:30pm

 

4:30pm

Summary and discussion of day's sessions

 

 

- - - - -      FRIDAY, JULY 18    - - - - -

 

Closing day

 


9-10am

Natural Language Processing on the Web -- VIDEO
GUY LAPALME, University of Montreal, IRO, RALI

Overview & Readings  -- Blog Discussion


10-11am

The Social Data Revolution: Are We Ready? -- VIDEO

CLAUDE THÉORET, Nexalogy Environics Canada

Overview & Readings -- Blog Discussion

Coffee Break 11am to 11:30am

 

11:30-12:30

Open Memetrics: Monitoring, Measuring and Mapping Memes -- VIDEO

STEVAN HARNAD, UQAM & U Southampton 
Overview and Readings -- Blog Discussion



 End of Summer Institute



 

KATY BORNER, Indiana University, Department of Information and Library Science

 

Humanexus: Envisioning Communication and Collaboration


OVERVIEW: This presentation opens with a screening of Humanexus, an award-winning semi-documentary that visualizes human communication from the Stone Age to today and beyond. The film aims to make tangible the enormous changes in the quantity and quality of our collective knowledge and the impact of different media and distribution systems on knowledge exchange. It follows a presentation and discussion of recent collaborative work on scholarly communication and collaboration. Last but not least, everyone will be invited to explore the Information Visualization MOOC (for free or for IU credits) and to visit the Places & Spaces: Mapping Science exhibit on display at the summer school.


READINGS:

    Stipelman, Brooke A., Hall, Kara L., Zoss, Angela, Okamoto, Janet, Stokols, Dan, and Börner, Katy (submitted) Mapping the Impact of Transdisciplinary Research: A Visual Comparison of Investigator Initiated and Team Based Tobacco Use Research Publications. The Journal of Translational Medicine and Epidemiology.
    Bollen, Johan, David Crandall, Damion Junk, Ying Ding, and Katy Börner. 2014. From funding agencies to scientific agency: Collective allocation of science funding as an alternative to peer review. EMBO Reports 15 (1): 1-121.
    Mazloumian, Amin, Dirk Helbing, Sergi Lozano, Robert Light, and Katy Börner. 2013. Global Multi-Level Analysis of the 'Scientific Food Web'. Scientific Reports 3, 1167.
    Börner, Katy, Noshir S. Contractor, Holly J. Falk-Krzesinski, Stephen M. Fiore, Kara L. Hall, Joann Keyton, Bonnie Spring, Daniel Stokols, William Trochim, and Brian Uzzi. 2010. A Multi-Level Systems Perspective for the Science of Team Science. Science Translational Medicine 2 (49): 49(cm)24.

Relevant books:
    Börner, Katy, and David E. Polley. 2014. Visual Insights: A Practical Guide to Making Sense of Data. Cambridge, MA: The MIT Press.
    Scharnhorst, Andrea, Katy Börner, and Peter van den Besselaar, eds. 2012. Models of Science Dynamics: Encounters Between Complexity Theory and Information Science. Springer Verlag.
    Börner, Katy, Mike Conlon, Jon Corson-Rikert, and Ying Ding, eds. 2012. VIVO: A Semantic Approach to Scholarly Networking and Discovery. Morgan & Claypool Publishers LLC.
    Börner, Katy. 2010. Atlas of Science: Visualizing What We Know. The MIT Press.
        Humanexus
        Information Visualization MOOC
        Places & Spaces: Mapping Science exhibit

LES CARR, University of Southampton, Web Science

Web Impact on Society --  VIDEO


OVERVIEW: The Web is not just an engineered technical artefact because the Web architecture (HTTP, HTML and URIs) is only the kernel of an enormously complex social-technical machine. Phenomena like online banking, Web TV, internet shopping, e-government and social networking are the names that we give to human activities and human agendas that have co-opted the capabilities of this web architecture. While we may look to the Web to offer a source of "big data" for "social analytics", one of the goals of Web Science is to try to find a perspective that helps us to understand the bigger "socio-technical" picture of the Web, and hence to better interpret the data that we harvest from the Web. By looking at specific examples of how the Web has grown and developed (such as open access, open government data), we can start to see some of the principles and mechanisms of the socio-technical Web.

READINGS:
    Tinati, R.,  Carr, L., Halford, S., Pope, C. (2013) The HTP Model: Understanding the Development of Social Machines, WWW2013 Workshop: The Theory and Practice of Social Machines,
    Tinati, R., Carr, L., Halford, S., Pope C. (2014) (Re)Integrating the Web: Beyond ‘Socio-Technical’, WWW2014

SIMON DeDEO, Indiana University, Santa Fe Institute

 

Collective Memory in Wikipedia


OVERVIEW: In an analysis of range of social systems, from online collaboration in Wikipedia to revolutionary activity in the Arab Spring, we find a common structure to social reasoning that crucially involves the formation of long-term memories and dispositions. No individual member serves as the system memory or reasoner; these dispositions are, instead, collective states of the group as a whole. The underlying computational structure appears to make use of at least one (formally) unbounded resource. We provide a game theoretic account of group-level strategies based on a simple belief-formation mechanism, and show the challenges that arise in connecting these group level phenomena to the beliefs and desires of the underlying individuals.


READINGS:

    DeDeo, S. (2013). Collective Phenomena and Non-Finite State Computation in a Human Social System. PloS one, 8(10), e75818.
   
Hooper, P. L., DeDeo, S., Caldwell Hooper, A. E., Gurven, M., & Kaplan, H. S. (2013). Dynamical Structure of a Traditional Amazonian Social Network. Entropy, 15(11), 4933-4955.
   
DeDeo, S. (2014) Groups Minds and the Case of Wikipedia
    Klingenstein, Sara, Tim Hitchcock, Simon DeDeo (2014) The civilizing process in London's Old Bailey. Proceedings of the National Academy of Sciences


 


ALAN EVANS Montreal Neurological Institute & McGill University, Biomedical Engineering 
Mapping the Brain Connectome --  VIDEO

OVERVIEW: The study of macroscopic neural connectivity using neuroimaging has exploded in recent years, with applications in many areas of clinical and basic neuroscience.  These approaches yield metrics of information flow across a network that are not accessible with focal metrics such as functional activation, metabolism or anatomical morphometry. However, there remain fundamental issues, both technical and conceptual, in reducing connectivity information from different imaging techniques into a holistic model of neural connectivity.  We will discuss different forms of connectivity, as defined by structural and functional correlation (MRI, fMRI, PET) and DTI tractography, with illustrations in normal and disordered brain.

READINGS:

    He, Y., & Evans, A. (2010). Graph theoretical modeling of brain connectivity. Current opinion in neurology, 23(4), 341-350.
    Bullmore, E. T., & Bassett, D. S. (2011). Brain graphs: graphical models of the human brain connectome. Annual review of clinical psychology, 7, 113-140.
    Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: a structural description of the human brain. PLoS computational biology, 1(4), e42.



JEAN-DANIEL FEKETE, Institut National de Recherche en Informatique et Automatique (INRIA) Unite de Recherche Saclay - ile-de-France

 

Visualizing Dynamic Interactions --  VIDEO


OVERVIEW: Graphs are powerful mathematical structures for modeling and representing many natural phenomena. In trying to explore and make sense of graphs collected in the wild — such as social interactions stored by social network sites or correlations between brain signals obtained using fMRI — visualization is often used. However, traditional visualization techniques are limited to sparse graphs: dense graphs are unreadable. Much progress has been made recently using matrix-based and hybrid visualizations to explore large and dense networks. Although understanding the visualization of the adjacency matrix of a graph is not as immediate as the traditional node-link representation, it does not suffer from most of its drawbacks and only takes a few minutes to grasp, a very reasonable time considering its expressive power. I’ll show how this relatively novel representation can be used to visualize many types of graphs, even dynamic graphs, with no limitation on density and good scalability. I'll show some results on social networks and brain signals.


READINGS:

    Wybrow, M., Elmqvist, N., Fekete, J. D., von Landesberger, T., van Wijk, J. J., & Zimmer, B. (2014). Interaction in the Visualization of Multivariate Networks. In Multivariate Network Visualization (pp. 97-125). Springer International Publishing.
    Bach, B., Pietriga, E., & Fekete, J. D. (2014, April).
Visualizing Dynamic Networks with Matrix Cubes. In SICCHI Conference on Human Factors in Computing Systems (CHI).



BENJAMIN FUNG, McGill University


       Applying Data Mining to Real-Life Crime Investigation


          OVERVIEW: Data mining has demonstrated a lot of success in many domains, from direct marketing to bioinformatics. Yet, limited research has been conducted to leverage the power of data mining in real-life crime investigation. In this presentation, I will discuss two data mining methods for crime investigation with a live software demonstration. The first method aims at identifying the true author of anonymous e-mail. The second method is a subject-based search engine that can help investigators to retrieve criminal information from a large collection of textual documents.

READINGS:

  Non-technical readings and audio clip:
     New York Times: Decoding Your E-Mail Personality
     CJAD: The Aaron Rand Show (Radio) on Cybercrime Investigation

    Technical readings:
     Iqbal, F; H. Binsalleeh, B. C. M. Fung, and M. Debbabi
(2013) A unified data mining solution for authorship analysis in anonymous textual communications. Information Sciences (INS): Special Issue on Data Mining for Information Security, 231: 98-112
     Dagher
G. G. and B. C. M. Fung (2013) Subject-based semantic document clustering for digital forensic investigations. Data & Knowledge Engineering (DKE), 86: 224-241


FABIEN GANDON, INRIA Research Center of Sophia-Antipolis

 

Social and Semantic Web: Adding the Missing Links

 

OVERVIEW: Since the mid-90s the Web re-opened in read-write mode and, almost as a side effect, paved the way to numerous new social media applications. Today, the Web is no longer perceived as a document system but as a virtual place where persons and software interact in mixed communities. These large scale interactions create many problems --  in particular, reconciling the formal semantics of computer science (e.g. logics, ontologies, typing systems, etc.) on which the Web architecture is built, with the soft semantics of people (e.g. posts, tags, status, etc.) on which the Web content is built. Wimmics, among other research labs, studies methods, models and algorithms to bridge formal semantics and social semantics on the Web. We focus on the characterization of typed graph formalisms to model and capture these different pieces of knowledge and hybrid operators to process them jointly. This talk will describe the basics of semantic web formalisms and introduce different initiatives using these frameworks to represent reason and support social media and social applications on the web.


READINGS:

    Nicolas Marie, Myriam Ribiere, Fabien Gandon, Florentin Rodio, Discovery Hub: on-the-fly linked data exploratory search,  Proc. of I-Semantics 2013, Graz, Austria
    Michel Buffa, Nicolas Delaforge, Guillaume Erétéo, Fabien Gandon, Alain Giboin, Freddy Limpens: ISICIL: Semantics and Social Networks for Business Intelligence. SOFSEM 2013: 67-85
    Nathalie Aussenac-Gilles, Fabien Gandon, From the knowledge acquisition bottleneck to the knowledge acquisition overflow: A brief French history of knowledge acquisition, International Journal of Human-Computer Studies, Volume 71, Issue 2, Pages 157-165, February 2013
    Guillaume Erétéo, Fabien Gandon, and Michel Buffa, SemTagP: Semantic Community Detection in Folksonomies, IEEE/WIC/ACM International Conference on Web Intelligence, August 2011, Lyon.
    Freddy Limpens, Fabien Gandon and Michel Buffa, Helping Online Communities to Semantically Enrich Folksonomies, Web Science Conference, April, 2010, Raleigh, NC, USA.
    Guillaume Erétéo, Michel Buffa, Fabien Gandon, and Olivier Corby. Analysis of a Real Online Social Network using Semantic Web Frameworks. In Proc. International Semantic Web Conference, ISWC'09, Washington, USA, October 2009




LEE GILES, Pennsylvania State University

 

Scholarly Big Data: Information Extraction and Data Mining


OVERVIEW:  Collections of scholarly documents are usually not thought of as big data. However, large collections of scholarly documents often have many millions of publications, authors, citations, equations, figures, etc., and large scale related data and structures such as social networks, slides, data sets, etc. We discuss scholarly big data challenges, insights, methodologies and applications. We illustrate scholarly big data issues with examples of specialized search engines and recommendation systems based on the SeerSuite software. Using information extraction and data mining, we illustrate applications in such diverse areas as computer science, chemistry, archaeology, acknowledgements, citation recommendation, collaboration recommendation, and others.


READINGS:

    Khabsa, M & Giles, C.L. (2014) The Number of Scholarly Documents on the Web. PLOS ONE 10.1371/journal.pone.0093949
    Caragea, C., Wu, J., Ciobanu, A., Williams, K., Fernandez-Ramrez, J., Chen, H. H., ... & Giles, L. (2014).
CiteSeer x: A Scholarly Big Dataset. In Advances in Information Retrieval (pp. 311-322). Springer International Publishing.           
    Flake, G. W., Lawrence, S., Giles, C. L., & Coetzee, F. M. (2002). Self-organization and identification of web communities. Computer, 35(3), 66-70.


PETER GLOOR, MIT Center for Collective Intelligence

 

Collaborative Innovation Networks

 

OVERVIEW: Every disruptive innovation is not the result of a lone inventor, but of a small group of likeminded individuals, working together in close collaboration to get their cool idea off the ground. They are leveraging the concept of swarm creativity, where this small team - the Collaborative Innovation Network (COIN) - empowered by the collaborative technologies of the Internet and social media, turns their creative labor of love into a product that changes the way how we think, work, or spend our day.

This talk describes a series of ongoing projects at the MIT Center for Collective Intelligence with the goal of analyzing the new idea creation process through tracking human interaction patterns on three levels:

On the global level, macro- and microeconomic indicators such as the valuation of companies and consumer indices, or election outcomes, are predicted based on social media analysis on Twitter, Blogs, and Wikipedia. On the organizational level, productivity and creativity of companies and teams is measured through extracting 'honest signals' from communication archives such as company e-mail. On the individual level, individual and team creativity is analyzed through face-to-face interaction with sociometric badges and personal e-mail logs.   

The talk introduces the concept of coolhunting, finding new trends by finding the trendsetters, and coolfarming, helping the trendsetters getting their idea over the tipping point. The talk also presents the concept of 'Virtual Mirroring', increasing individual and team creativity by analyzing and optimizing five inter-personal interaction variables of honest communication: 'strong leadership', 'rotating leaders', 'balanced contribution', 'fast response', and 'honest sentiment.'

READINGS:

    Gloor, P. A., Krauss, J., Nann, S., Fischbach, K., & Schoder, D. (2009, August). Web science 2.0: Identifying trends through semantic social network analysis. In Computational Science and Engineering, 2009. CSE'09. International Conference on (Vol. 4, pp. 215-222). IEEE.
    Kleeb, R., Gloor, P. A., Nemoto, K., & Henninger, M. (2012).
Wikimaps: dynamic maps of knowledge. International Journal of Organisational Design and Engineering, 2(2), 204-224.
    Gloor, P. (2010) Coolfarming - Turn Your Great Idea Into The Next Big Thing AMACOM, NY
    Gloor, P.  (2006) Swarm Creativity, Competitive Advantage Through Collaborative Innovation Networks. Oxford

 



JENNIFER GOLBECK, University of Maryland

 

You Can't Hide: Predicting Personal Traits in Social Media


OVERVIEW: People share a huge amount of personal information online. With over a billion people on social media, this is opening up new abilities for researchers to predict a range of personal attributes that reveal how we live, think, and interact, even as people may try to keep this information private. This presentation will cover the methods and results in this area and argue for the future science and policy these advances demand.


READINGS:

    Golbeck, J. (2013). Analyzing the social web.
    Newnes.
Golbeck, J., Robles, C., Edmondson, M., & Turner, K. (2011, October). Predicting personality from twitter. In Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom) (pp. 149-156). IEEE
    Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802-5805.
    Golbeck, J., Robles, C., & Turner, K. (2011, May). Predicting personality with social media. In CHI'11 Extended Abstracts on Human Factors in Computing Systems (pp. 253-262). ACM..



ROBERT GOLDSTONE, Psychological and Brain Sciences, Indiana University

 

Learning Along with Others

OVERVIEW:  We have developed internet-enabled experimental platforms to explore group patterns that emerge when people can see and imitate the solutions, innovations, and choices of their peers over several rounds.  Experiments and simulations show that there is a systematic relation between the difficulty of a problem search space and the optimal social network for transmitting solutions. With more complex search spaces, people imitate: prevalent options, options that become increasingly prevalent, high-scoring options, solutions similar to one’s own solution, and during the early stages of an extended search process.  Historical records of baby names show that naming choices are influenced by both the frequency of a name, and increasingly by its “momentum” in the recent past.

READINGS:

    Goldstone, R. L., Wisdom, T. N., Roberts, M. E., & Frey, S. (2013). Learning along with others. Psychology of Learning and Motivation, 58, 1-45.
    Wisdom, T. N., Song, X., & Goldstone, R. L. (2013).
Social Learning Strategies in Networked Groups. Cognitive science, 37(8), 1383-1425.
    Theiner, G., Allen, C., & Goldstone, R. L. (2010).
Recognizing group cognition. Cognitive Systems Research, 11(4), 378-395.   
    Frey, S., & Goldstone, R. L. (2013). Cyclic game dynamics driven by iterated reasoning. PLoS One, 8(2)
    Roberts, M. E., & Goldstone, R. L. (2011).  Adaptive Group Coordination and Role DifferentiationPLoS One, 6, 1-8.
    Gureckis, T. M., & Goldstone, R. L. (2009). How you named your child: Understanding the relationship between individual decision-making and collective outcomes. Topics in Cognitive Science, 1, 651-674.


STEPHEN GRIFFIN, University of Pittsburgh, School of Information Science

 

New Models of Scholarly Communication for Digital Scholarship --  VIDEO

 

OVERVIEW: Contemporary research and scholarship increasingly uses large-scale datasets and computationally intensive processing.  Cultural shifts in the scholarly community challenge long-standing of academic institutions and call into question the efficacy and fairness of traditional models of scholarly communication. Scholars are also calling for greater authority in the publication of their works and rights management.  Agreement is growing on how best to manage and share massive amounts of diverse and complex information objects.  Open standards and technologies allow interoperability across institutional repositories.  Content level interoperability based on semantic web and linked open data standards is becoming more common.   Information research objects are increasingly thought of as social as well as data objects - promoting knowledge creation and sharing and possessing qualities that promote new forms of scholarly arrangements and collaboration. This talk will present alternative paths for expanding the scope and reach of digital scholarship and robust models of scholarly communication necessary for full reporting.  The overall goals are to increase research productivity and impact, and to give scholars a new type of intellectual freedom of expression.

READINGS:

    Griffin, S. (2013) Scholarly Communication: New Models for Digital Scholarship Workflows Coalition for Networked Information, Spring 2013 Meeting
    Griffin, S. et al (2014) The Denton Declaration: An Open Data Manifesto
    Borgman, C.L. (2013) Digital Scholarship and Digital Libraries: Past, Present, and Future Theory and Practice of Digital Libraries Conference, September 2013

    Calhoun, K (2014) Exploring Digital Libraries: Foundations, practice, prospects Facet Publishing London, UK

http://www.openscholarship.org/jcms/c_5012/en/home

http://www.ischool.pitt.edu/scholarlycom/

http://www.sis.pitt.edu/~repwkshop (from Internet Archive Wayback Machine)

http://www.digitalhumanities.org/dhq/vol/3/1/000035/000035.html

http://en.wikipedia.org/wiki/E-Science

http://journalofdigitalhumanities.org/

http://chia.pitt.edu/
http://www.perseus.tufts.edu/hopper/




DAME WENDY HALL, University of Southampton

Web Science: It's All In the Mind --  VIDEO
 

OVERVIEW: This year we celebrate the 25th Anniversary of the World Wide Web. Twenty-five years ago there were no web sites, by 1994 there were 800, today it is estimated there are nearly a billion. The reason for this is not solely down to the technology, it is because we - as individuals, organisations and society - create the content that makes the Web grow. This socio-technical aspect of the Web was the founding principal of Web Science. In this talk we will discuss the theory and practice of Web Science – past, present and future – and conjecture the nature of collective intelligence on the Web. Will the Web ever develop a mind of it’s own?


READINGS:

    Berners-Lee, T., Hall, W., Hendler, J., Shadbolt, N., & Weitzner, D. (2006). Creating a Science of the Web. Science, 313(5788), 769-771.
    Berners-Lee, T., Hall, W., Hendler, J. A., O'Hara, K., Shadbolt, N., & Weitzner, D. J. (2006). A framework for web science. Foundations and trends in Web Science, 1(1), 1-130.
    Hendler, J., Shadbolt, N., Hall, W., Berners-Lee, T., & Weitzner, D. (2008). Web science: an interdisciplinary approach to understanding the web. Communications of the ACM, 51(7), 60-69.
    O'Hara, K., Contractor, N. S., Hall, W., Hendler, J. A., & Shadbolt, N. (2013).
Web Science: understanding the emergence of macro-level features on the World Wide Web. Foundations and Trends in Web Science, 4(2-3), 103-267.
    Tiropanis, T., Hall, W., Shadbolt, N., De Roure, D., Contractor, N., & Hendler, J. (2013). The Web Science Observatory. IEEE Intelligent Systems, 28(2), 100-104.


HARRY HALPIN, University of Edinburgh, Institute of Communicating and Collaborating Systems, School of Informatics

 

Does the Web Extend the Mind - and Semantics?


OVERVIEW: Under what conditions does the Web count as a part of your own mind? We discuss the conditions upon which cognitive extension and integration can be upheld, and inspect these in light of the Web. We also argue that this ability to integrate the mind into media such as the Web is inherently social, insofar as it involves interaction with both technological scaffolding and other humans. Also, there are many cases where external media like the Web are not actually integrated cognitively, but simply serve as a way to co-ordinate intelligent problem-solving via distributed cognition. Yet distributed cognition should not be underestimated, as it can serve as a stepping stone to a wider kind of cognitive integration: collective intelligence. Finally, we inspect the impact of the Web — via phenomena like tagging, social media, and search engines — on traditional notions of language and semantics.


READINGS:
    Hui, Y., & Halpin, H. (2013). Collective individuation: the future of the social web. The Unlike Us Reader, 103-116
    Halpin, H., Robu, V., & Shepherd, H. (2007, May). The complex dynamics of collaborative tagging. In Proceedings of the 16th international conference on World Wide Web (pp. 211-220). ACM.
    Halpin, H (2013) Does the web extend the mind?. In: Proceedings of the 5th Annual ACM Web Science Conference (WebSci '13). ACM, New York, NY, USA, 139-147.

JIAWEI HAN, University of Illinois at Urbana-Champaign, Department of Computer Science 

 

Knowledge Mining in Heterogeneous Information Networks

 

OVERVIEW: People and informational objects are interconnected, forming gigantic, interconnected, integrated information networks.  By structuring these data objects into multiple types, such networks become semi-structured heterogeneous information networks.  Most real world applications that handle big data, including interconnected social media and social networks, medical information systems, online e-commerce systems, or database systems, can be structured into typed, semi-structured, heterogeneous information networks.  For example, in a medical care network, objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits, diagnosis, and treatments are intertwined together, providing rich information and forming heterogeneous information networks.  Effective construction, exploration and analysis of large-scale heterogeneous information networks poses an interesting but critical challenge.

In this talk, we present principles, methodologies and algorithms for mining in heterogeneous social and information networks and show that mining typed, heterogeneous networks is a promising research frontier in data mining research.  Departing from many existing network models that view data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and can uncover surprisingly rich knowledge from interconnected data.  This heterogeneous network modeling will lead to the discovery of a set of new principles and methodologies for mining and exploring interconnected data, such as rank-based clustering and classification, meta path-based similarity search, and meta path-based link/relationship prediction.  We will also discuss our recent progress on construction of quality semi-structured heterogeneous information networks from unstructured data and point out some promising research directions.


READINGS:

    Yizhou Sun and Jiawei Han (2012) Mining Heterogeneous Information Networks: Principles and Methodologies, Morgan & Claypool Publishers

    Chi Wang, Marina Danilevsky, Jialu Liu, Nihit Desai, Heng Ji, and Jiawei Han, Constructing Topical Hierarchies in Heterogeneous Information Networks, Proc. 2013 IEEE Int. Conf.on Data Mining (ICDM'13), Dallas, TX, Dec. 2013


STEVAN HARNAD, UQAM & U Southampton

"Memetrics: Monitoring, Measuring and Mapping Memes"


Closing Overview and Discussion of Summer Institute

Overview: Memes are the practices and products that we copy from one another and pass on from generation to generation. Memes began with analog mimicry, but with language they became digital. Natural language is a code that subsumes all other codes (maths, logic, programming languages). With language you can say, and understand, and convey anything that can be said. The first arbitrarily shaped word spoken (or, more likely, gestured) with the intention to convey a true/false proposition was the first digital meme. Words are almost all the names of categories (things we need to know what to do with). Language evolved as a means of sharing our categories through verbal instruction instead of having to learn them through slow, risky trial-and-error induction from direct experience. The Web has now become our “Cognitive Commons” — the global repository of our digital memes, and the means of monitoring, measuring, mapping and maximizing our categories: the quotidial ones as well as the scholarly and scientific ones. How many words were needed to “initialize” this whole process? Why were we so quick to use the web for chatter and commerce, but slower to use it to share research findings? And can cognizing — the felt mental state of thinking — be extended beyond individual cognizing minds to collective and even “global” minds?


READINGS

Blondin-Massé, A., Harnad, S., Picard, O. & St-Louis, B. (2013) Symbol Grounding and the Origin of Language: From Show to Tell. In: Lefebvre C, Comrie B & Cohen H (Eds.) Current Perspective on the Origins of Language,

Harnad, Stevan (2013) The Postgutenberg Open Access Journal (revised). In, Cope, B and Phillips, A (eds.) The Future of the Academic Journal (2nd edition). 2nd edition of book  Chandos.



JIM HENDLER, Rensselaer Polytechnic Institute, Department of Computer Science 

 


The Semantic Web: The Inside Story


OVERVIEW:  In this talk I look at the Semantic Web idea of adding knowledge to the Web in ways compatible with machine processing.  Emerging in the late 90s, and growing since then,the languages , usage and uptake of semantic technologies has been increasing.  I'll discuss the genesis of this idea, some key steps in its history, and current usage. I also proposes challenges: Having far surpassed the original vision, how do we continue to use and grow the semantic web? 


READINGS:

    Hendler, J., & Berners-Lee, T. (2010). From the Semantic Web to social machines: A research challenge for AI on the World Wide Web. Artificial Intelligence, 174(2), 156-161.
    Shadbolt, N., Hall, W., Hendler, J. A., & Dutton, W. H. (2013).
Web science: a new frontier. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1987), 20120512. Hendler, J. (2014). Big data meets computer science. Journal of Computing Sciences in Colleges, 29(6), 5-6.


TONY HEY, Microsoft Research Connections

 

Open Science and the Web --  VIDEO

 

OVERVIEW: Turing award winner, Jim Gray, envisioned a world where all research literature and all research data were online and interoperable. He believed that such a distributed, global digital library could significantly increase the research "information velocity" and improve the scientific productivity of researchers. The last decade has seen significant progress in the move towards open access to scholarly research publications and the removal of barriers to access and re-use. But barrier-free access to the literature alone only scratches the surface of what the revolution of data intensive science promises. Recently, in the US, the White House has called for federal agencies to make all research outputs (publications and data) openly available. But in order to make this effort effective, researchers need better tools to capture and curate their data, and Jim Gray called for 'letting 100 flowers bloom' when it came to research data tools. Universities have the opportunity and obligation to cultivate the next regeneration of professional data scientists who can help define, build, manage, and preserve the necessary data infrastructure. This talk will cover some of the recent progress made in open access and open data, and will discuss some of the opportunities ahead.

READINGS:
    Fox, G., Hey, T., & Trefethen, A. (2013). Where Does All the Data Come From?. Data-Intensive Science, 115.
    Hey, T. (2010).
The next scientific revolution. Harv Bus Rev, 88(11), 56-63. The Fourth Paradigm: Data-Intensive Scientific Discovery Book 2009
http://research.microsoft.com/en-us/collaboration/fourthparadigm/default.aspx
http://eprints.rclis.org/9202/1/heyhey_final_web.pdf


FRANCIS HEYLIGHEN, Vrije Universiteit Brussel, ECCO - Evolution, Complexity and Cognition research group

 

Towards a Global Brain: the Web as a Self-organizing, Distributed Intelligence --  VIDEO

 

OVERVIEW: Distributed intelligence is an ability to solve problems and process information that is not localized inside a single person or computer, but that emerges from the coordinated interactions between a large number of people and their technological extensions. The Internet and in particular the World-Wide Web form a nearly ideal substrate for the emergence of a distributed intelligence that spans the planet, integrating the knowledge, skills and intuitions of billions of people supported by billions of information-processing devices. This intelligence becomes increasingly powerful through a process of self-organization in which people and devices selectively reinforce useful links, while rejecting useless ones. This process can be modeled mathematically and computationally by representing individuals and devices as agents, connected by a weighted directed network along which "challenges" propagate. Challenges represent problems, opportunities or questions that must be processed by the agents to extract benefits and avoid penalties. Link weights are increased whenever agents extract benefit from the challenges propagated along it. My research group is developing such a large-scale simulation environment in order to better understand how the web may boost our collective intelligence. The anticipated outcome of that process is a "global brain", i.e. a nervous system for the planet that would be able to tackle both global and personal problems.


READINGS:
    Heylighen, F. (2014). Return to Eden? Promises and Perils on the Road to a Global Superintelligence. The End of the Beginning: Life, Society and Economy on the Brink of the Singularity, B. Goertzel and T. Goertzel, Eds.
    Heylighen, F. (2013). Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence. In Complexity Perspectives on Language, Communication and Society (pp. 117-149). Springer Berlin Heidelberg.


BRYCE HUEBNER, Georgetown University, Department of Philosophy

 

Macrocognition: Situated versus Distributed

 

OVERVIEW: 'Macrocognition' has two distinct, but closely related meanings. Cacciabue and Hollnagel (1995) introduced it to denote the study of cognition in realistic tasks, where people interact with various forms of environmental and social scaffolding; Klein and colleagues also used it to understand how people manage uncertainty and make sense of real world environments. I introduced a second use (Huebner 2014) as shorthand for system-level cognition implemented by integrated networks of specialized computational mechanisms, whether in individuals or groups. Macrocognition has one sense that's closer to 'situated or extended cognition' and another that's closer to 'distributed or collective cognition' but they are often conflated. There are important differences between the hypothesis of collective cognition (HCC) and the hypothesis of extended cognition (HEC). Recent work on situated and collective memory and philosophical approaches to coordination and planning suggest that HCC is more plausible if we abandon HEC in favor of an 'ontologically thinner' approach to situated cognition. There is a form of collective planning distinct from the planning that relies on web-based technologies and other forms of social scaffolding. Distinguishing two forms of macrocognition, one situated the other distributed, can help us to make sense of a number of theoretically and empirically interesting phenomena.


READINGS:

Huebner, B. (2011). Genuinely collective emotions. European Journal for Philosophy of Science, 1(1), 89-118.

Huebner, B. (2014). Macrocognition: A Theory of Distributed Minds and Collective Intentionality. Oxford University Press.

Klein, G., Ross, K. G., Moon, B. M., Klein, D. E., Hoffman, R. R., & Hollnagel, E. (2003). Macrocognition. Intelligent Systems, IEEE, 18(3), 81-85.

http://brycehuebner.weebly.com/
http://www.sciencedirect.com/science/article/pii/S1389041713000259


CHARLES-ANTOINE JULIEN, Mcgill University, School of Information Studies


   Visual Tools for Interacting with Large Networks


    OVERVIEW: Useful real-work networks tend to be large and complex, which makes them difficult to browse and navigate by humans. Visual interfaces can mitigate this problem but these tools inevitably suffer from scalability issues, which have led to the development of various clutter reduction techniques such as sampling and filtering. We present and discuss ongoing work concerning visual tools for information exploration and retrieval using large semantic ontology networks (e.g., Library of Congress Subject Headings, Medical Subject Headings, personal information folder structures), which aim to help searchers describe and recognize the information they seek, and discover previously unknown and valuable topics.
READINGS:
    Ellis, G., & Dix, A. (2007). A taxonomy of clutter reduction for information visualisation. IEEE Transactions on Visualization and Computer Graphics, 13, 1216-1223.
    Gruber, T. (2008). Ontology. In Liu, Ling; Özsu, M. Tamer. Encyclopedia of Database Systems. Springer-Verlag.
    Katifori, A., Halatsis, C., Lepouras, G., Vassilakis, C., & Giannopoulou, E. (2007). Ontology visualiazation methods - a survey. ACM Computing Surveys, 39(4, article 10), 1-43.
von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J. J., Fekete, J. D., & Fellner, D. W. (2011). Visual Analysis of Large Graphs: State-of-the-Art and Future Research Challenges. Computer Graphics Forum, 30(6), 1719-1749.


KAYVAN KOUSHA, University of University of Wolverhampton

 

Web Impact Metrics for Research Assessment --  VIDEO


OVERVIEW: Web metrics are being increasingly explored in the assessment research impact. Hyperlinks, web citations, and URL citations can today be systematically compared with conventional measures (e.g., Web of Science citation counts). Formal citations are also being extracted from web databases and digital libraries by CiteSeer, Google Scholar, and from the huge digitized database of Google Books. These may prove informative as alternative and supplementary citation impact metrics, especially in the social sciences, arts and humanities, where traditional citation indexes are not available or have insufficient coverage. New web impact metrics come from citations in online syllabi and course reading lists, which reflect the educational impact of research, and from download counts of academic publications, which reflect reading and usage. Social impact metrics or Altmetrics — including social bookmarks, tweets, online reading of scientific publications, and viewings of online academic videos — are also emerging. Web impact metrics need to be used cautiously in research evaluation, however, because they still suffer from a generic lack of quality control compared with traditional citation metrics.

READINGS:

    Kousha, K. & Thelwall, M. (2014). Web Impact Metrics for Research Assessment. In: B. Cronin & C.R. Sugimoto, (Eds), Beyond Bibliometrics: Harnessing Multidimensional Indicators of Scholarly Impact, MIT Press.
    Thelwall, M., Vaughan, L., & Bjorneborn, L. (2005).
Webometrics. ARIST, 39(1), 81-135.
    Kousha, K., & Thelwall, M. (2007).
Google Scholar citations and Google Web/URL citations: A multidiscipline exploratory analysis. Journal of the American Society for Information Science and Technology, 58(7), 1055-1065.

    Thelwall, M. & Kousha, K. (in press, 2014). ResearchGate: Disseminating, communicating and measuring scholarship? Journal of the American Society for Information Science and Technology.
    Kousha, K., Thelwall. M & Abdoli, M.  (2012). The role of online videos in research communication: A content analysis of YouTube videos cited in academic publications, Journal of the American Society of Information Science and Technology, 63(9), 1710–1727.
     Kousha, K. & Thelwall. M. (2008). Assessing the Impact of Research on Teaching:  An Automatic Analysis of Online Syllabuses in Science and Social Sciences, Journal of the American Society of Information Science and Technology, 59(13), 2060–2069.
 


GUY LAPALME, University of Montreal, IRO, RALI


    Natural Language Processing on the Web

    OVERVIEW: Even with the advent of the semantic web, most of the content available on the web is still in natural language, more than half of it in English, but more and more of it in other languages also. We will present some odede
f the links (pun intended) between natural language processing (NLP) and the web: how NLP helps in processing information on the web, but also how web technologies help in the development of NLP technologies.

    READINGS

    Lapalme, G. (2013) XML: Looking at the Forest Instead of the Trees
    Lapalme, G., P. Langlais, and F. Gotti (2012) The Bilingual Concordancer TransSearch, NAACL 2012
    Gotti, F., P. Langlais, and G. Lapalme (2014) Designing a Machine Translation System for Canadian Weather Warnings: a Case Study, Natural Language Engineering 20(3): 399-433


VINCENT LARIVIERE, Universite de Montreal

 

Transformations in Scholarly Communication in the Digital World --  VIDEO


OVERVIEW:  Digital technologies — easy to update, reuse, access and transmit and require little space — have changed how researchers produce and disseminate scientific knowledge. Based on quantitative studies in the sociology of science, this talk will discuss these transformations, higlighting three aspects: the increase of scientific collaboration, the diversification of publication venues, and the use of social media.


READINGS:

    Wallace, M. L., Lariviere, V., & Gingras, Y. (2012). A small world of citations? The influence of collaboration networks on citation practices. PloS one, 7(3), e33339.
    Lariviere, V., Gingras, Y., & Archambault, E. (2006).
Canadian collaboration networks: A comparative analysis of the natural sciences, social sciences and the humanities. Scientometrics, 68(3), 519-533.
    Bollen, J., Van de Sompel, H., Hagberg, A., & Chute, R. (2009).
A principal component analysis of 39 scientific impact measures. PloS one, 4(6), e6022.
    http://www.chss.uqam.ca/Portals/0/docs/Canadian_Networks_Final.pdf
    http://ella.slis.indiana.edu/~sugimoto/preprints/OnTheRelationship.pdf
    http://arxiv.org/abs/1304.6460
    http://arxiv.org/abs/1205.4328
    http://arxiv.org/abs/1308.1838
    http://arxiv.org/abs/0809.5250



YANG-YU LIU, Northeastern University, Center for Complex Network Research, Physics Department

 

Controllability and Observability of Complex Systems

 

OVERVIEW: The ultimate proof of our understanding of complex systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered systems towards a desired state, a framework to control complex systems is lacking. In this talk I will show that many dynamic properties of complex systems can studied be quantitatively, via a combination of tools from control theory, network science and statistical physics. In particular, I will focus on two dual concepts, i.e. controllability and observability, of general complex systems. Controllability concerns our ability to drive the system from any initial state to any final state within finite time, while observability concerns the possibility of deducing the system's internal state from observing its input-output behavior. I will show that by exploring the underlying network structure of complex systems one can determine the driver (or sensor) nodes that with time-dependent inputs (or measurements) will enable us to fully control (or observe) the whole system.


READINGS:

    Liu, Y. Y., Slotine, J. J., & Barabasi, A. L. (2011). Controllability of complex networks. Nature, 473(7346), 167-173.
    Zhao, C., Wang, W. X., Liu, Y. Y., & Slotine, J. J. (2014).
Universal Symmetry in Complex Network Control. arXiv preprint arXiv:1403.0041.



THOMAS MALONE, MIT Sloan School of Management

 

Collective Intelligence: What is it?  How can we measure it?  And how can we increase it?

 

OVERVIEW: This talk will describe how the same statistical techniques used to measure intelligence in individuals can be used to measure the "collective intelligence" of groups.  We find that, just as with individuals, a single statistical factor can predict the performance of a group on a wide range of different tasks.  This factor is only weakly correlated with the group members' individual intelligence.  It is, however, correlated with the group members' social perceptiveness, conversational behavior, and gender.

The talk will also include brief overviews of other work to increase collective intelligence by: (a) combining predictions from humans and computers, (b) mapping the "genome" of collective intelligence, and (c) harnessing the collective intelligence of thousands of people around the world to develop proposals for what to do about global climate change.

READINGS:

    Bernstein, A., Klein, M., & Malone, T. W. (2012). Programming the global brain. Communications of the ACM, 55(5), 41-43.
    Malone, T. W., Laubacher, R., & Dellarocas, C. (2010).
The collective intelligence genome. IEEE Engineering Management Review, 38(3), 38.
    Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010)  Evidence for a collective intelligence factor in the performance of human groups, Science, 330 (6004), 686-688
    Malone, T. W., Laubacher, R., & Dellarocas, C. (2010) The Collective Intelligence Genome, Sloan Management Review, Spring 2010, 51, 3, 21-31 (Reprint No. 51303).
    Bernstein, A., Klein, M., & Malone, T. W.  Programming the global brainCommunications of the ACM, May 2012, 55 (5): 41-43.




RICHARD MENARY, University of Macquarie, Philosophy


    Enculturated Cognition


    OVERVIEW: What is the relationship between culture and cognition? In this talk I show how we might think of the development of recent cognitive abilities - such as reading, writing and mathematics - as being the result of high fidelity social learning in richly structured socio-cultural niches. The influence of representational systems and new technologies on our cognitive abilities for complex mathematical, narrative and scientific thinking should not be underestimated.


                                                                                 READINGS:
    Menary, R. (2013). Cognitive integration, enculturated cognition and the socially extended mind. Cognitive Systems Research, 25, 26-34.
    Menary, R. (Ed.). (2010). The extended mind. MIT Press.


ADILSON MOTTER, Northwestern University, Dynamics of Complex Systems and Networks Group

 

Bursts, Cascades, and Time Allocation

 

OVERVIEW: In this talk, I will present recent results on three distinct but related problems concerning Web Science and the Mind: bursts in the temporal distribution of words, cascading dynamics in diverse network systems, and human allocation of time. In each case I will discuss key properties, the principles governing these properties, and opportunities their modeling offers for monitoring and controlling complex behavior.


READINGS:

    Cornelius, S. P., Kath, W. L., & Motter, A. E. (2013). Realistic control of network dynamics. Nature communications, 4:1942
    Altmann, E. G., Pierrehumbert, J. B., & Motter, A. E. (2009).
Beyond word frequency: Bursts, lulls, and scaling in the temporal distributions of words. PLoS One, 4(11), e7678.
    Motter A. E.. &  Albert R. (2012), Networks in motion  Physics Today 65(4), 43-48
.

 




CAMERON NEYLON, PLOS (Public Library of Science)

 

Network Ready Research: The Role of Open Source and Open Thinking

 

OVERVIEW: The highest principle of network architecture design is interoperability. Metcalfe's Law says a network's value can scale as some exponent of the number of connections. Our job in building networks is to ensure that those connections are as numerous, operational, and easy to create as possible. Informatics is a science of networks: of physical interactions, genetic control, degree of similarity, or ecological interactions, amongst many others. Informatics is also amongst the most networked of research communities and amongst the most open in the sharing of research papers, research data, tools, and even research in process in online conversations and writing. Lifting our gaze from the networks we work on to the networks we occupy is a challenge. Our human networks are messy and contingent and our machine networks clogged with things we can't use, even if we could access them. What principles can we apply to build our research to make the most of the network infrastructure we have around us. Where are the pitfalls and the opportunities? What will it take to configure our work so as to enable "network ready research"?

READINGS:

    Molloy, J. C. (2011). The open knowledge foundation: open data means better science. PLoS biology, 9(12), e1001195.
    Whyte, A., & Pryor, G. (2011).
Open science in practice: Researcher perspectives and participation. International Journal of Digital Curation, 6(1), 199-213.

http://cameronneylon.net/blog/fork-merge-and-crowd-sourcing-data-curation/

https://www.youtube.com/watch?v=Axr80qm6NHw

 


TAKASHI NISHIKAWA, Northwestern University, Physics & Astronomy

 

Visual Analytics Approach for Discovering Network Structure Beyond Communities

 

OVERVIEW: To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks.  Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem.  In this talk, I will describe an approach that overcomes this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes.  Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups.  The results of applying our method to real networks suggest that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.


READINGS:

    Nishikawa, T., & Motter, A. E. (2011). Discovering network structure beyond communities. Scientific reports, 1, 151
    Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges (pp. 154-175). Springer Berlin Heidelberg.

    Federico, P., Aigner, W., Miksch, S., Windhager, F., & Zenk, L. (2011. A visual analytics approach to dynamic social networks. Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies. ACM.
    Li, K., Guo, L., Faraco, C., Zhu, D., Chen, H., Yuan, Y., ... & Liu, T. (2012). Visual analytics of brain networks. NeuroImage, 61(1), 82-97.


FILIPPO RADICCHI, Indiana University, Center for Complex Networks and Systems Research, School

of Informatics and Computing

 

Analogies between interconnected and clustered networks


OVERVIEW: In this talk, I will illustrate how spectral methods can be used to determine common properties shared by interconnected networks and graphs with community structure. In particular, I will show that degree correlations play a fundamental role for the characterization of the structural phases of these systems.


READINGS:

    Radicchi, F (2014) A paradox in community detection  EPL 106, 38001
    Radicchi, F (2014) Driving interconnected networks to supercriticality Phys. Rev. X 4, 021014
    Radicchi, F (2013) Detectability of communities in heterogeneous networks Phys. Rev. E 88, 010801(R)


MARK ROWLANDS, University of Miami, Department of Philosophy

 

Extended Mentality: What It Is and Why It Matters

 

OVERVIEW: Does it matter if (some) mental processes extend into the subjects's environment. The notion of mattering is an elliptical one: something matters only to someone and in some way. A tacit assumption in the recent debate is that the question of whether mental processes extend should be decided by way of its implications for cognitive science. The persons to whom it matters and who should be charged with adjudicating the issue are, accordingly, cognitive scientists and philosophers of cognitive science. I shall argue against this assumption. What is really at stake is a philosophical vision of the nature of mentality that can, to a considerable extent, be elaborated independently of developments in cognitive science.


READINGS:

    Rowlands, M. (2009). Enactivism and the extended mind. Topoi, 28(1), 53-62.
    Rowlands, M. (2009). Extended cognition and the mark of the cognitive. Philosophical Psychology, 22(1), 1-19.

    Rowlands, M. (2010). The new science of the mind. Mit Press.



ROBERT RUPERT, U Colorado & U Edinburgh, Department of Philosophy

 

What is Cognition, and How Could it be Extended?

 

OVERVIEW: Cognition is the overarching natural kind or property that distinctively contributes to the production of the proprietary phenomenon investigated by cognitive science, that is, intelligent behavior. On the ground, cognitive-scientific practice relies most fundamentally on modeling. Taken together, these two observations suggest a way to identify what it is for a process or state to be cognitive: abstract from the variety of forms of successful cognitive-scientific modeling. The central theoretical construct of cognitive science, the one common to all successful forms of cognitive-scientific modeling, is the relatively persisting, integrated system that moves through the world managing the agent's interaction with the environment when the agent behaves intelligently. I characterize the relevant form of integration more precisely, then ask (1) whether humans currently function as components in cognitive systems that include more than individual humans and (2) whether the griidea of an integrated system can help us to decide whether to count as cognitive the processes occurring in creatures other than humans.


READINGS:

    Rupert, R. D. (2011). Cognitive systems and the supersized mind. Philosophical studies, 152(3), 427-436.
    Rupert, R. D. (2009). Cognitive systems and the extended mind. Oxford University Press.
    Rupert, R. (2013) Memory, Natural Kinds, and Cognitive Extension; or, Martians Don’t Remember, and Cognitive Science Is Not about Cognition, Review of Philosophy and Psychology 4, 1 (2013): 25–47


DEREK RUTHS, McGill University

The Promises and Pitfalls of Latent Attribute Inference

OVERVIEW: The composition of a group determines much of its behavior (are people old or young, PhDs or illiterate, artists or scientists?).  As a result, organizations, governments, and companies are deeply interested in being able to quickly learn the makeup of groups.  In order to approach this problem, we've been developing technologies for inferring the demographics of Twitter populations from the textual content and networks that the users in them produce.  Our methods stand out as the most accurate in the literature.  In this talk, I'm going to give an overview of the latent attribute inference problem, discuss the advances that we've made in solving it, and highlight some of the big issues that still need to be tackled.


READINGS:

    Ruths, D. A., Nakhleh, L., Iyengar, M. S., Reddy, S. A., & Ram, P. T. (2006). Hypothesis generation in signaling networks. Journal of Computational Biology, 13(9), 1546-1557.
    Ruths, J & D. Ruths. (2014) Control Profiles of Complex Networks Science 343: 1373-6

   

JUDITH SIMON, Institut fuer Technikfolgenabschuetzung und Systemanalyse (ITAS)

 

Socio-Technical Epistemology


OVERVIEW: The increasing pervasiveness of technologies of computation, information and communication not only affect our culture, economy and politics: they shape our epistemic practices: increasing amounts of personal data are used for profiling, information gets personalized in more or less transparent ways, we use crowd-sourced or collaboratively created content in our daily quests for knowledge. These technologies offer new possibilities and challenges both in research and in our everyday lives. My talk will try to shed some light on the impact of the computational on epistemic practices and the challenges this raises for philosophy. We need to develop a socio-technical epistemology, harvesting insights from different disciplines beyond philosophy, such as science and technology studies, cognitive science and web science, to provide frameworks for evaluating as well as guiding the design and governance of socio-technical epistemic systems and practices. Socio-technical epistemology can have positive repercussions for these neighbouring disciplines.


READINGS:

    Simon, J. (2010). The entanglement of trust and knowledge on the Web. Ethics and Information Technology, 12(4), 343-355.
    Simon, J. (2010).
A Socioepistemological Framework for Scientific Publishing. Social Epistemology, 24(3), 201-218.




JOHN SUTTON, Macquarie University, Department of Cognitive Science

 

Transactive Memory and Distributed Cognitive Ecologies

 

OVERVIEW: Does the internet alter the way we remember? What understanding of memory makes sense in light of our rich interactions with technologies and with other people? This presentation introduces theoretical and empirical work on distributed cognitive ecologies as a framework for addressing web science and the mind. It surveys recent accounts of the effect of new technologies on human memory, with a focus on transactive memory theory. It embeds recent empirical findings on the ways we remember in conjunction with each other and with online systems in a broader picture of socially distributed remembering. In place of metaphysical concerns about extended cognition and popular worries about the erosion of natural memory, it suggests a number of rich research possibilities for integrating the cognitive and social sciences.


READINGS:

    Michaelian, K., & Sutton, J. (2013). Distributed cognition and memory research: History and current directions. Review of Philosophy and Psychology, 4(1), 1-24.
    Sutton, J., Harris, C. B., Keil, P. G., & Barnier, A. J. (2010).
The psychology of memory, extended cognition, and socially distributed remembering. Phenomenology and the cognitive sciences, 9(4), 521-560.
http://www.johnsutton.net/Sutton_CHSC.pdf
http://www.johnsutton.net/PCS_Sutton_Harris_Keil_Barnier.pdf
http://www.wjh.harvard.edu/~wegner/pdfs/science.1207745.full.pdf

GEORG THEINER, Villanova University, Department of Philosophy

 

Domains and Dimensions of Group Cognition


OVERVIEW: Groups of people working together in a collaborative fashion can accomplish things that would completely baffle individual human beings. The nature, speed, scope, and interdependence of group collaboration have been dramatically expanded by web-based technologies which support the large-scale distribution of cognition across space, time, and people. This development has led many people to speak of groups as ‘distributed cognitive systems’ in their own right. But what exactly does that mean? Building on the psychological infrastructure of joint and collective intentionality (Tomasello, 2014), I distinguish between various forms of joint cognition, distributed cognition, and collective cognition, and illustrate the resulting taxonomy with research from various fields and cognitive domains.


READINGS:

(available via http://villanova.academia.edu/GeorgTheiner)

Gordon, B.R. & Theiner, G. (forthcoming). Scaffolded Joint Action as a Micro-Foundation of Organizational Learning. In C.B. Stone & L.M. Bietti (Eds.), Contextualizing Human Memory: An Interdisciplinary Approach to Understanding How Individuals and Groups Remember the Past. London: Psychology Press.

Theiner, G. (2014). Varieties of Group Cognition. In L. Shapiro (Ed.), The Routledge Handbook of Embodied Cognition (pp. 347-357). New York: Routledge.

Theiner, G. (2013). Transactive Memory Systems: A Mechanistic Analysis of Emergent Group Memory. Review of Philosophy and Psychology, 4(1), 65-89.

Theiner, G., Allen, C., & Goldstone, R. (2010). Recognizing Group Cognition. Cognitive Systems Research, 11(4), 378-395.

Theiner, G. & O’Connor, T. (2010). The Emergence of Group Cognition. In A. Corradini & T. O’Connor (Eds.), Emergence in Science and Philosophy (pp. 78-117). New York: Routledge.


CLAUDE THÉORET, Nexalogy Environics Canada

The Social Data Revolution: Are We Ready?

OVERVIEW: Social Data and Big data are being billed as the next big thing – the key to gaining a competitive advantage and increasing profitability for companies both big and small. The increasing importance of data analysis in decision making has boosted demand for employees with analytical skill sets, popularizing career paths that lead to big data jobs.  But is enterprise ready for the shift?  What are the challenges facing companies in the next 5 years when integrating data based decision making ?  We will explore some of these broad issues in this talk.
    Theoret, Claude G., and Guido Vieira (2012) System and Method for Performing Analysis on Information, Such as Social Media, U.S. Patent Application 13/705,940, filed December 5, 2012.
    Videos
    Start-up Fest


PETER TODD, Indiana University, Department of Psychological and Brain Sciences

 

Foraging in the World, Mind and Online

 

OVERVIEW: How do we decide when to search for something better and when to stick with what we've got?  People, like other organisms, must adaptively trade off between exploring and exploiting their environment to obtain the resources they need.  This applies to whatever space they are searching: whether the external spatial world, looking for patches of food; the social environment, looking for mates or friends; the internal mental environment, looking for concepts in memory; or the online environment, looking for information on the Web.  Common underlying mechanisms may be used to address the explore/exploit tradeoff in each of these domains.  People use similar heuristic strategies to decide when to keep looking and when to give up searching for resources in patches in space (e.g., for fish in a pond), in memory (e.g., for words in a category), and online (e.g., for useful Web pages), as predicted by optimal foraging theory.  Moreover, the connections between search in these domains may have deep evolutionary roots, built on the same underlying mechanisms, as indicated by studies showing that search in an external domain can prime subsequent search strategies in an internal domain.  In this talk, I will describe how new studies are uncovering these connections between spatial search and information search (as described in Cognitive Search: Evolution, Algorithms, and the Brain, Todd, Hills, and Robbins, eds.; MIT Press, 2012).


READINGS:

    Hills, T. T., Jones, M. N., & Todd, P. M. (2012). Optimal foraging in semantic memory. Psychological review, 119(2), 431.
    Hills, T.T.,     Todd, P.M., and Goldstone, R.L. (2008).  Search in external and internal spaces: Evidence for generalized cognitive search processesPsychological Science, 19(8), 802-808.
    Wilke, A., Todd, P.M., and Hutchinson, J.M.C. (2009).  Fishing for the right words: Decision rules for human foraging behavior in external and internal search tasksCognitive Science, 33, 497-529.

PETKO VALTCHEV UQÀM

        Mining Patterns from Linked Data

OVERVIEW: The Web of Data (WoD) can be seen as global database made of multiple datasets. These datasets are published separately — by using new or reusing existing schemas on the Web — yet get interlinked through either direct references between data items or indirect ones, i.e., identity links between items representing the same entity. The technology underlying the WoD, called Linked Data (LD) allows for the construction of a global data graph in which data items are vertices related by edges of different nature. Entities, aka resources, as well as their links, aka properties, are globally identified through URLs. Beside this inherent graph structure, parts of the WoD can behave as a traditional, i.e., relational, database. 
    After substantial efforts on the standards for publishing and querying of LD on the Web, and lately the interlinking and cleansing of sets of LD, the next big issue is properly extracting new knowledge from the WoD. Data Mining (DM) discipline is about finding chunks of useful knowledge hidden in the data. DM methods are roughly divided into predictive ones, where past experience is analyzed in order to guess what the outcome of an unfolding situation, and descriptive ones whose aim is to provide insights into the regularities in the data without a specific goal. Mining LD is both useful and challenging for many reasons, not the least among them being the rich and complex graph structure induced by a large variety of link types, the availability of domain knowledge expressed as schemas, and even fully-blown ontologies, the heterogeneity in the modelling goals behind individual datasets, etc.
    In this talk we discuss the implications of LD for a specific branch of descriptive DM, called pattern mining. We present two different mining methods for that are complementary in many respects. The first one targets usage regularities: It analyses the consumption of resources from the WoD by the users of a specific semantic application and summarizes it as behavioural patterns. The second one mines purely descriptive patterns from a dataset of multiple resource types, which are expressed in a WoD-compliant language and therefore supports ontology design.
READINGS:
    M Rouane-Hacene, M Huchard, A Napoli, P Valtchev, Relational concept analysis: mining concept lattices from multi-relational data Annals of Mathematics and Artificial Intelligence 67 (1), 81-108, 2013
    MH Rouane, M Huchard, A Napoli, P Valtchev, A proposal for combining formal concept analysis and description logics for mining relational data Formal Concept Analysis (vol. of LNCS), 51-65, Springer, 2007
    M Adda, P Valtchev, R Missaoui, C Djeraba, A framework for mining meaningful usage patterns within a semantically enhanced web portal Proc. of the Third C* Conf. on Computer Science and Software,138-147, ACM, 2010
    M Adda, P Valtchev, R Missaoui, C Djeraba, Toward recommendation based on ontology-powered web-usage mining IEEE Internet Computing 11 (4), 45-52, 2007



 

 

 

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