Informaatioteknologian tiedekunta

Collective Intelligence

Viimeisin muutos maanantai 23. lokakuuta 2017, 10.40

Research Group

Professor Vagan Terziyan
Senior Researcher Olena Kaikova
Senior Researcher Hannakaisa Isomäki
Postdoc Researcher Oleksiy Khriyenko
Postdoc Researcher Michael Cochez
Doctoral student Anastasiia Girka
Affiliated: Senoir Researcher Eeva Kallio
 

Description

The group main research objective is addressing the Big Data Challenge with the advanced analytics, which includes aspects from Cognitive Computing, Linked Data and Collective Intelligence. Development includes Everything-as-a-Smart-Service Engineering: Designing self-managing software applications for smart cyber-physical systems and Industry 4.0.

Collective intelligence of artificial smart systems provides the capability of their autonomic behavior (Self-Management and Self-Adaptation), which is known since 2001 as an Autonomic Computing initiative of IBM and which is the only way to address today’s information volumes, complexity and dynamics. The global importance of the topic is confirmed by recently (28.09.2016) announced historic Partnership on AI of the leading IT companies (Facebook, Amazon, DeepMind/Google, IBM and Microsoft) as a non-profit organization that will work to advance public understanding of Artificial Intelligence technologies and formulate best practices on the challenges and opportunities within the field. Academics, non-profits, and specialists in policy and ethics will be invited to join (It’s important to remember that on a day-to-day basis, these teams are in constant competition with each other to develop the best products and services powered by machine intelligence). 

Our group aims to bridge the gap between “computational” aspects of Big Data intelligence and wisdom (AI, cognitive computing, augmented intelligence, computational intelligence, data science, etc.) and “human” theories and models of the intelligence and wisdom (philosophical, cultural, ethical, psychological, etc.). The major assumption is that research and development on human aspects will benefit from the recent knowledge and tools developed within the “computational” sciences and vice versa – current architectures of the “smart” (e.g., smart-data, e-Health, e-Education, cyber-physical, Industry 4.0, etc.) systems may wisely embed discovered human aspects directly to the systems. Such a coevolution of these traditionally “firewalled” domains will be mutually beneficial.

Our interests (within research activities and teaching) also include Cybersecurity-related aspects of the Cognitive Computing and Collective Intelligence. Emerging Cognitive Computing services attract huge amounts of users worldwide. Very recently the new vulnerabilities of Cognitive Computing and of its enabler Deep Learning have been discovered - the so called Cognitive Risks for Cybersecurity associated with the Cognitive Hack attacks. The cyber battleground has shifted recently from an attack on hard assets to a much softer target: the human mind as human behavior is the new and last “weakest link” in the cyber security armor. The Bruce Schneier’s popular quota: “Only amateurs attack machines; professionals target people” [B. Schneier (2000). Semantic Attacks: The Third Wave of Network Attacks], is now becoming an emerging reality. The cognitive hack takes place when a users’ behavior is influenced by misinformation. In his new book [J. Bone (2017). Cognitive Hack: The New Battleground in Cybersecurity ... the Human Mind, CRC Press], James Bone admits that “the human-machine interaction is the greatest threat in cyber space yet very few, if any, security professionals are well versed in strategies to close this gap”. On the other hand, Cognitive Computing and Collective Intelligence enhances creation of “artificial” decision-makers, agents, cognitive robots, etc. These entities are becoming users of various information systems and sources and automatically learn based on this usage experiences. Therefore they are also becoming a potential target for Cognitive Hacking. Very recent articles related to security aspects of Deep Learning noticed numerous potential risks of influencing outcomes of Machine Learning (e.g., decision models) by a variety of (training) data poisoning techniques. Therefore our interest would be on how to handle (based on system's self-awareness and self-protection) such risks for both human minds and artificial minds (i.e., risks of Cognitive Hacking of the Collective Intelligence) to make future smart systems secure.

Regarding the Big Data domain there will be two interconnected aspects of the group R&D:

  • “computational” - enabling autonomous, self-managed Big Data based on “Artificial Wisdom”, “agile” deep learning and self-learning, cognition and self-cognition, computing and self-computing;
  • “human” – addressing the ethical concerns of wisdom within the cognitive computing systems to improve the potential (e.g. medical) decisions to be “ethically wise” either in global context or specifically for the possible use in Finland.

Regarding the digitalization (e.g., digital environments, digital university, e-education, etc.) activity there will be two interconnected aspects of the group R&D:

  • “computational” - enabling cognitive self-development of the “smart” and “wise” digital content as an autonomous self-managed system based on Big Data;
  • “human” – achieving wisdom as a learning outcome of the educational process due to coevolution of the smart content vs. learner.

Regarding the Cyber-Security activity there will be two interconnected aspects of the group R&D:

  • “computational” - enabling cognitive self-protection of the critical infrastructures as autonomous self-managed system based on Big Data;
  • “human” – enabling collective intelligence and collective wisdom as quality assurance instrument for the management and protection of the critical infrastructures.

External Funding

R&D is funded by TEKES; EU FP7; Erasmus+ (Knowledge Alliances, Strategic Partnership, Capacity Building); COST - 'European cooperation in Science and Technology'

Selected projects:

SmartResource http://www.cs.jyu.fi/ai/OntoGroup/SmartResource_details.htm

UBIWARE http://www.cs.jyu.fi/ai/OntoGroup/UBIWARE_details.htm

TRUST http://www.dovira.eu/

International Networks:

- Academic network of the ICT COST Action IC1302 KEYSTONE 'Semantic Keyword-based Search on Structured Data Sources': http://www.keystone-cost.eu/keystone/

- 'Sophia & Phronesis' European wisdom research, learning and development network:

http://www.wisdom-fi.com/network.html

- Tempus HEI Network: http://www.dovira.eu/consortium.html

Collaborative Partners:

University of Cergy-Pontoise, Paris, France - research, education, student and staff exchange, joint projects

National University of Radio & Electronics, Kharkiv, Ukraine - research, education, student exchange, joint projects

Open positions

We are looking for highly interested and strongly motivated new doctoral students assuming funding can be arranged. There are several Master & Doctoral Thesis topics available for those interested in our area of R&D. Please, contact Professor Vagan Terziyan (vagan.terziyan@jyu.fi)

Selected Publications

  • Terziyan V., Golovianko M., & Cochez M., TB-Structure: Collective Intelligence for Exploratory Keyword Search. In: A. Calì, D. Gorgan, & M. Ugarte (Eds.), Semantic Keyword-Based Search on Structured Data Sources. KEYSTONE 2016, LNCS 10151, 2017, Springer, pp. 171-178. (doi:10.1007/978-3-319-53640-8_15)
  • Cochez M., Terziyan V., Ermolayev V., Large Scale Knowledge Matching with Balanced Efficiency-Effectiveness Using LSH Forest, Transactions on Computational Collective Intelligence, LNCS Springer, 2017 (to appear).
  • Cochez M., Periaux J., Terziyan V., Tuovinen T., Agile Deep Learning UAVs Operating in Smart Spaces: Collective Intelligence vs. ”Mission-Impossible”, In: Computational Methods and Models for Transport - New Challenges for the Greening of Transport Systems, Springer, 2017 (Chapter 3, pp.: 47-71). (to appear).
  • Terziyan V., Kaikova O., Ontology for Temporal Reasoning based on Extended Allen’s Interval Algebra, International Journal of Metadata, Semantics and Ontologies, Vol. 11, No. 2, 2016, pp. 93-109. (doi: 10.1504/IJMSO.2016.080348)
  • Terziyan V., Kaikova O., The "Magic Square": A Roadmap towards Emotional Business Intelligence, Journal of Decision Systems, Vol.24, No.3, 2015, Taylor & Francis, pp. 255-272. (doi: 10.1080/12460125.2015.969592)
  • Terziyan V., Golovianko M., Shevchenko O., Semantic Portal as a Tool for Structural Reform of the Ukrainian Educational System, In: Information Technology for Development, Vol. 21, No. 3, 2015, Taylor & Francis, pp. 381-402.  (doi:  10.1080/02681102.2014.899955).
  • Ermolayev V., Akerkar R., Terziyan V., Cochez M., Towards Evolving Knowledge Ecosystems for Big Data Understanding, In: R. Akerkar (ed.), Big Data Computing, Chapman and Hall /CRC, 2013, 542 pp.
  • Terziyan V., Shevchenko O., Golovianko M., An Introduction to Knowledge Computing, Eastern-European Journal of Enterprise Technologies, Vol. 1, No. 2 (67), 2014, pp. 27-40.
  • Khriyenko O., Terziyan V., Kaikova O., End User-Facilitated Interoperability in Internet of Things: Visually-Enriched User-Assisted Ontology Alignment, In: International Journal on Advances in Internet Technology, Vol. 6, Ns. 1&2, 2013, ISSN 1942-2652, pp. 90-100.
  • Terziyan V., Kaykova O., From Linked Data and Business Intelligence to Executable Reality, In: International Journal on Advances in Intelligent Systems, Vol. 5, Ns. 1&2, 2012, ISSN 1942-2679, pp. 194-208.
  • Terziyan V., Kaykova O., Zhovtobryukh D., UbiRoad: Semantic Middleware for Cooperative Traffic Systems and Services, In: International Journal on Advances in Intelligent Systems, Vol. 3, Ns. 3&4, 2010, ISSN 1942-2679, pp. 286-302.
  • Terziyan V., SmartResource – Proactive Self-Maintained Resources in Semantic Web: Lessons learned, In: International Journal of Smart Home, Special Issue on Future Generation Smart Space, Vol.2, No. 2, April 2008, pp. 33-57.

 You can access the publications from here: http://www.mit.jyu.fi/ai/vagan/papers.html