posted by organizer: TeckHouTeng || 3426 views || tracked by 15 users: [display]

INNS-BigData 2016 : The INNS Big Data conference 2016

FacebookTwitterLinkedInGoogle

Link: http://conferences.cwa.gr/inns-big-data2016/
 
When Oct 23, 2016 - Oct 25, 2016
Where Thessaloniki, Greece
Submission Deadline Jun 27, 2016
Notification Due Jul 10, 2016
Final Version Due Jul 19, 2016
Categories    neural networks   big data
 

Call For Papers

###########################################

The INNS Big Data conference 2015

October 23-25, 2016, Thessaloniki, Greece

EXTENDED CALL FOR PAPERS

Important Dates have been extended ...... as follows
###########################################################
Paper Submission June 27, 2016
Paper Decision Notification July 10, 2016
Camera Ready Submission of papers July 17, 2016

Early Registration July 19, 2016
###########################################################

###########################################

Homepage: http://conferences.cwa.gr/inns-big-data2016/

###########################################

Big data is not just about storage of and access to data. Analytics play a big role in making sense of that data and exploiting its value. But learning from big data has become a significant challenge and requires development of new types of algorithms. Most machine learning algorithms can’t easily scale up to big data. Plus there are challenges of high-dimensionality, velocity and variety.

The neural network field has historically focused on algorithms that learn in an online, incremental mode without requiring in-memory access to huge amounts of data. This type of learning is not only ideal for streaming data (as in the Industrial Internet or the Internet of Things), but could also be used on stored big data. Thus, neural network technologies can become significant components of big data analytics platforms. Following on successful run of the inaugural INNS-BigData 2015, this second edition of INNS Conference on Big Data continues on this collaborative adventure with big data and other learning technologies.

Thus the aim of this conference is to promote new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms), implementations on different computing platforms (e.g. neuromorphic, GPUs, clouds, clusters) and applications of Big Data Analytics to solve real-world problems (e.g. weather prediction, transportation, energy management).

Awards
###########################################################
* Best papers will be selected and awarded as follows:
- Best regular paper
- Best student paper

* This will be based on a combination of reviewers’ comments, presentations and importance and quality judged by a panel.

* Best paper awards (500 Euros) are donated by the sponsor Springer Verlag, Germany and will be commemorated by a certificate.

* Students are encouraged to apply for a travel grant sponsored by AI Journal
###########################################################

Co-Sponsors
* International Neural Network Society (INNS)
* Springer

Keynote Speakers
* Frabcesco Bonchi, Technological Center of Catalunya, Spain
* Steve Furber, University of Manchester, UK
* Rudolf Kruse, OVG University of Magdeburg, Germany
* Pitor Mirowski, Google Deep Mind, UK

Advisory Board
* Walter Freeman, University of California, Berkeley, USA
* Ali Minai, University of Cincinnati, USA
* Danil Prokhorov, Toyota Tech Center
* Theodore Trafalis, University of Oklahoma, USA
* Kumar Venayagamoorthy, Clemson University, USA
* Bernard Widrow, Stanford University, USA

General Chairs
* Plamen Angelov, Lancaster University, UK
* Yannis Manolopoulos, Aristotle University, Greece

PC Chairs
* Lazaros Iliadias, Democritus University, Greece
* Asim Roy, Arizona State University, Tempe, USA
* Marley Vellasco, PUC-Rio, Rio de Janeiro, Brazil

Special Sessions Chairs
* Alessandro Ghio, University of Genoa, Italy
* Irwin King, Chinese University of Hong Kong, China

Tutorials/Workshops Chair
* Nikola Kasabov, Auckland Universitty of Technology, New Zealand
* Bernardete Ribeiro, University of Coimbra, Portugal

Poster Session Chairs
* Yi Lu Murphy, University of Michigan-Dearborn, USA
* Liang Zhao, University of Sao Paulo, Brazil

Awards Chari
* Araceli Sanchis de Miguel, Carlos III University, Spain

Competitions Chair
Adel Alimi, University of Sfax, Tunisia

Panel Chair
* Leonid Perlovsky, Harvard University, Boston, USA

Sponsors/Exhibit Chairs
* James Dankert, BAE Systems, USA
* Rosemary Paradis, Lockheed Martin, USA

Publication Chairs
* Danilo Mandic, Imperial College, London, UK
* Mariette Awad, American University of Beirut, Lebanon

International Liaison
* De-Shuang Huang, Tongji University, Shanghai, China
* Petia Georgieva, University of Aveiro, Portugal

Publicity Chairs,
* Teng Teck Hou, Singapore Management University, Singapore
* Simone Scardapane, The Sapienza University of Rome, Italy
* Jose Antonio Iglesias Martinez, Carlos III University, Spain

Paper Submission and Publication
###########################################################
* Original works submitted as a regular paper limited to a maximum of 10 pages in IEEE 2-column format will be published in the proceedings.

* It will be peer-reviewed by at least three PC members on the basis of technical quality, relevance, originality, significance and clarity.

* At least one author of an accepted submission to the conference should register with a regular fee to present their work at the conference.

* Accepted papers will be published in the conference proceedings by Springer.

Special Issue:
###############

* Selected INNS Big Data 2016 papers will be considered for publication in a Special Issue of the Big Data Research journal by Elsevier.
###########################################################

Accepted Tutorials
###########################################################
1. Dr. Luca Oneto and Dr. Davide Anguita DIBRIS, University of Genoa, Italy
Title: Model Selection and Error Estimation Without the Agonizing Pain

2. Giacomo Boracchi, Ph.D. Associate Professor Department of Electronics and Informatics, Politecnico di Milano, Italy
Title: Change Detection in Data Streams: Big Data Challenges

3. Prof. Spiros Likothanassis and Dr. Christos Alexakos, Pattern Recognition Lab, Dept. of Computer Engineering & Informatics, University of Patras, Greece
Title: Preprocessing and Analyzing TMT Proteomics ‘Big Data’ Using Computational Intelligence Techniques

4. Elizabeth Behrman and James Steck, Wichita State University, Wichita, USA
Title: "Machine Learning in Quantum Computing: Applications to Big Data"

5. Apostolos Papadopoulos and Gounaris, Department of Computer Science, Aristotle University of Thessaloniki, Greece
Title: "The Spark Engine"

6. Damianos Chatziantoniou Athens University of Economics and Business (AUEB). Department: Department of Management Science and Technology, Greece
Title: "Federation and Interoperability in Big Data Systems"
###########################################################

Accepted Workshops
###########################################################
1. Scalable Machine Learning
2. Intelligent Transportation Systems and Big Data
3. Workshop on Big Data in Bioinformatics
###########################################################

Topics and Areas include, but not limited to:
* Autonomous, online, incremental learning – theory, algorithms and applications in big data
* High dimensional data, feature selection, feature transformation – theory, algorithms and applications
for big data
* Scalable algorithms for big data
* Learning algorithms for high-velocity streaming data
* Big data streams analytics
* Deep neural network learning
* Machine vision and big data
* Brain-machine interfaces and big data
* Cognitive modeling and big data
* Embodied robotics and big data
* Fuzzy systems and big data
* Evolutionary systems and big data
* Evolving systems for big data analytics
* Neuromorphic hardware for scalable machine learning
* Parallel and distributed computing for big data analytics (cloud, map-reduce, etc.)
* Big data and collective intelligence/collaborative learning
* Big data and hybrid systems
* Big data and self-aware systems
* Big Data and infrastructure
* Big data analytics and healthcare/medical applications
* Big data analytics and energy systems/smart grids
* Big data analytics and transportation systems
* Big data analytics in large sensor networks
* Big data and machine learning in computational biology, bioinformatics
* Recommendation systems/collaborative filtering for big data
* Big data visualization
* Online multimedia/ stream/ text analytics
* Link and graph mining
* Big data and cloud computing, large scale stream processing on the cloud

Related Resources

ICANN 2017   International Conference on Artificial Neural Networks 2017
DSAA 2017   The 4th IEEE International Conference on Data Science and Advanced Analytics 2017
BigData-FAB 2016   Special Issue on “Big Data and Machine Learning in Finance, Accounting and Business” in Electronic Commerce Research (Springer)
Big Data- ADDS 2017   Special Issue on Big Data Analytics & Data-Driven Science
BDCloud 2017   The 7th IEEE International Conference on Big Data and Cloud Computing
ISNN 2017   Fourteenth International Symposium on Neural Networks
ICBDA 2017   The 2017 IEEE International Conference on Big Data Analysis (ICBDA 2017) - Ei Compendex
Elsevier JOCS NCP&BD 2017   Elsevier Journal of Computational Science (SCI IF=1.078) Special Issue on The Convergence of New Computing Paradigms and Big Data Analytics Methodologies for Online Social Networks
ECML-PKDD 2017   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
IEEE-ICCCBDA 2017   2nd International Conference on Cloud Computing and Big Data Analysis ICCCBDA -IEEE,Ei Compendex