Saturday 26 November 2016

Call for Papers: IEEE DSAA'17


IEEE DSAA'2017: 2017 International Conference on
Data Science and Advanced Analytics

Tokyo, Japan
October 19-21, 2017


  • A very competitive acceptance rate (about 10%) for regular papers
  • Jointly supported by IEEE, ACM and American Statistics Association
  • Strong inter-disciplinary and cross-domain culture
  • Strong engagement of analytics, statistics and industry/government
  • Double blind, and 10 pages in IEEE 2-column format


Data-driven scientific discovery is regarded as the fourth science paradigm. Data science is a core driver of the next-generation science,  technologies and applications, and is driving new researches, innovation,  profession, economy and education  across disciplines and across domains. There are many associated scientific challenges, ranging from data capture, creation, storage, search, sharing, modeling, analysis, and visualization. Among the complex aspects to be addressed we mention here the integration across heterogeneous, interdependent complex data resources for real-time decision making, streaming data, collaboration, and ultimately value co-creation. Data science encompasses the areas of data analytics, machine learning, statistics, optimization and managing big data, and has become essential to glean understanding from large data sets and convert data into actionable intelligence, be it data available to enterprises, society, Government or on the Web.

DSAA takes a strong interdisciplinary approach, features by its strong engagement with statistics and business, in addition to core areas including analytics, learning, computing and informatics. DSAA fosters its unique Trends and Controversies session, Invited Industry Talks session, Panel discussion, and four keynote speeches from statistics, business, and data science. DSAA main tracks maintain a very competitive acceptance rate (about 10%) for regular papers.

Following the preceeding three editions DSAA'2016 (Montreal),  DSAA'2015 (Paris), and DSAA'2014 (Shanghai), the 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA'2017) aims to provide a premier forum that brings together researchers, industry practitioners, as well as potential users of big data, for discussion and exchange of ideas on the latest theoretical developments in Data Science as well as on the best practices for a wide range of applications.

DSAA is also technically sponsored by ACM through SIGKDD and by the American Statistics Association.

DSAA solicits then both theoretical and practical works on data science and advanced analytics. DSAA'2017 will consist of two main tracks: Research and Applications, and a series of Special sessions.  The Research Track is aimed at collecting original (unpublished nor under consideration at any other venue) and significant contributions related to foundations of Data Science and Analytics. The Applications Track is aimed at collecting original papers describing better and reproduciable practices with substantial contributions to Data Science and Analytics in real life scenarios. DSAA special sessions substantially upgrade traditional workshops to encourage emerging topics in data science while maintain regirous selection criteria. Call for proposals to organize special sessions are highly encouraged.


Paper Submission deadline:           May 25, 2017
Notification of acceptance:            July 25, 2017
Final Camera-ready papers due:   August 15, 2017
Early Registration deadline:           August 31, 2017


All accepted papers, including main tracks and special sessions, will be published by IEEE and will be submitted for inclusion in the IEEE Xplore Digital Library. The conference proceedings will be submitted for EI indexing through INSPEC by IEEE. Top quality papers accepted and presented at the conference will be selected for extension and invited to the special issues of International Journal of Data Science and Analytics (JDSA, Springer).


General areas of interest to DSAA'2017 include but are not limited to:

1. Foundations

  • Mathematical, probabilistic and statistical models and theories
  • Machine learning theories, models and systems
  • Knowledge discovery theories, models and systems
  • Manifold and metric learning
  • Deep learning and deep analytics
  • Scalable analysis and learning
  • Non-IID learning
  • Heterogeneous data/information integration
  • Data pre-processing, sampling and reduction
  • Dimensionality reduction
  • Feature selection, transformation and construction
  • Large scale optimization
  • High performance computing for data analytics
  • Architecture, management and process for data science

2. Data analytics, machine learning and knowledge discovery

  • Learning for streaming data
  • Learning for structured and relational data
  • Latent semantics and insight learning
  • Mining multi-source and mixed-source information
  • Mixed-type and structure data analytics
  • Cross-media data analytics
  • Big data visualization, modeling and analytics
  • Multimedia/stream/text/visual analytics
  • Relation, coupling, link and graph mining
  • Personalization analytics and learning
  • Web/online/social/network mining and learning
  • Structure/group/community/network mining
  • Cloud computing and service data analysis

3. Management, storage, retrieval and search

  • Cloud architectures and cloud computing
  • Data warehouses and large-scale databases
  • Memory, disk and cloud-based storage and analytics
  • Distributed computing and parallel processing
  • High performance computing and processing
  • Information and knowledge retrieval, and semantic search
  • Web/social/databases query and search
  • Personalized search and recommendation
  • Human-machine interaction and interfaces
  • Crowdsourcing and collective intelligence

4. Social issues

  • Data science meets social science
  • Security, trust and risk in big data
  • Data integrity, matching and sharing
  • Privacy and protection standards and policies
  • Privacy preserving big data access/analytics
  • Social impact and social good


Papers in this track should motivate, describe and analyze the reproduciable use of Data science tools and/or techniques in practical applications as well as illustrate their actual impact on business and/or society.

We seek contributions that address topics such as (but not limited to) the following:

  • Best practices and lessons learned from both success and failure
  • Data-intensive organizations, business and economy
  • Quality assessment and interestingness metrics
  • Complexity, efficiency and scalability
  • Big data representation and visualization
  • Business intelligence, data-lakes, big-data technologies
  • Data science education and training practices and lessons
  • Large scale application case studies and domain-specific applications, such as:
    • Online/social/living/environment data analysis
    • Mobile analytics for hand-held devices
    • Anomaly/fraud/exception/change/drift/event/crisis analysis
    • Large-scale recommender and search systems
    • Data analytics applications in cognitive systems, planning and decision support
    • End-user analytics, data visualization, human-in-the-loop, prescriptive analytics
    • Business/government analytics, such as for financial services, manufacturing, retail, utilities, telecom, national security, cyber-security, e-governance, etc.


Submissions to the main conference, including Research Track, Applications Track, and Special Sessions should be made through the IEEE DSAA'2017 Submission Web site.

The paper length allowed is a maximum of ten (10) pages, in the IEEE 2-column format (see the IEEE Proceedings Author Guidelines:

To help ensure correct formatting, please use the style files for U.S. letter size found at the link above as templates for your submission, which include both LaTeX and Word.

All submissions will be blind reviewed by the Program Committee on the basis of technical quality, relevance to conference topics of interest, originality, significance, and clarity. Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity.


Call for tutorials:
Call for special sessions:
Call for sponsorship:


General Chairs:
  • Hiroshi Motoda,           Osaka University, Japan
  • Fosca Giannotti,          Information Science and Technology Institute of the National Research Council at Pisa, Italy
  • Tomoyuki Higuchi,         Institute of Statistical Mathematics, Japan
Program Chairs - Research Track
  • Takashi Washio,           Osaka University, Japan
  • Joao Gama,                University of Porto, Portugal
Program Chairs - Application Track
  • Ying Li,                  DataSpark Pte. Ltd., Singapore
  • Rajesh Parekh,            Facebook, also with KDD2016 and The Hive, USA
Special Session Chairs
  • Huan Liu,                 Arizona State University, USA
  • Albert Bifet,             Telecom ParisTech, France
Trends & Controversies Chairs
  • Philip S. Yu,             University of Illinois at Chicago, USA
  • Pau-Choo (Julia) Chung,   National Cheng Kung University, Taiwan
Award Chair
  • Bamshad Mobasher,         DePaul University, USA
NGDS (Next Generation Data Scientist) Award Chairs
  • Kenji Yamanishi,          University of Tokyo, Japan
  • Xin Wang,                 University of Calgary, Canada
Tutorial Chairs
  • Zhi-Hua Zhou,             Nanjing University, China
  • Vincent Tseng,            National Chiao Tung University, Taiwan
Panel Chairs
  • Geoff Webb,               Monash University, Australia
  • Bart Goethals,            University of Antwerp, Belgium
Invited Industry Talk Chairs
  • Yutaka Matsuo,            University of Tokyo, Japan
  • Hang Li,                  Huawei Technologies, Hong Kong
Publicity Chairs
  • Tu Bao Ho,                Japan Advanced Institute of Science & Technology, Japan
  • Diane J. Cook,            Washington State University
  • Marzena Kryszkiewicz,     Warsaw University of Technology, Poland
Local Organizing Chairs
  • Satoshi Kurihara,         University of Electro-Communications, Japan
  • Hiromitsu Hattori,        Ritsumeikan University, Japan
Publication Chair
  • Toshihiro Kamishima,      National Institute of Advanced Industrial Science and Technology, Japan  
Web Chair
  • Kozo Ohara,               Aoyama Gakuin University, Japan
Sponsorship Chairs
  • Yoji Kiyota,              NEXT Co., Ltd, Japan
  • Kiyoshi Izumi,            University of Tokyo, Japan
  • Tadashi Yanagihara,       KDDI Corp., KDDI R\&D Laboratory, Japan
  • Longbing Cao,             University of Technology Sydney, Australia  
  • Byeong Kang               University of Tasmania, Australia


Wednesday 23 November 2016

IJCNN 2017 Deadline Extended

IJCNN 2017 - International Joint Conference on Neural Networks
May 14-19, 2017, Anchorage, Alaska
You can also follow IJCNN2017 on Facebook and Twitter.

Important Announcement

The paper submission deadline has been extended by two weeks, to 01Dec2016!

Important Dates

  • Paper Submission                         December 01, 2016
  • Paper Decision Notification         January 20, 2017
  • Camera-Ready Submission          February 20, 2017


The 2017 International Joint Conference on Neural Networks (IJCNN 2017) will be held at the William A. Egan Civic and Convention Center in Anchorage, Alaska, USA, May 14–19, 2017. The conference is organized jointly by the International Neural Network Society and the IEEE Computational Intelligence Society, and is the premiere international meeting for researchers and other professionals in neural networks and related areas.  It will feature invited plenary talks by world-renowned speakers in the areas of neural network theory and applications, computational neuroscience, robotics, and distributed intelligence.  In addition to regular technical sessions with oral and poster presentations, the conference program will include special sessions, competitions, tutorials and workshops on topics of current interest

For the latest updates, follow us on Facebook ( and Twitter (@ijcnn2017).

Paper Submission is Open

  • Regular paper can have up to 8 pages in double-column IEEE Conference format
  • All papers are to be prepared using IEEE-compliant Latex or Word templates on paper of U.S. letter size.
  • All submitted papers will be checked for plagiarism through the IEEE CrossCheck system. 
  • Papers with significant overlap with the authors own papers or other papers will be rejected without review.

Topics and Areas of Interest

The range of topics covered include, but is not limited to, the following.

(See for a more detailed list of topics).

  • Deep learning
  • Neural network theory & models 
  • Computational neuroscience 
  • Cognitive models 
  • Brain-machine interfaces 
  • Embodied robotics 
  • Evolutionary neural systems 
  • Neurodynamics 
  • Neuroinformatics 
  • Neuroengineering 
  • Hardware, memristors 
  • Neural network applications 
  • Machine perception (vision, speech, ...) 
  • Social media 
  • Big data
  • Pattern recognition
  • Machine learning
  • Collective intelligence
  • Hybrid systems
  • Self-aware systems
  • Data mining
  • Sensor networks
  • Agent-based systems
  • Computational biology
  • Bioinformatics
  • Artificial life
  • Connectomics
  • Philosophical issues

Organizing Committee

The full organizing committee can be found at:

General Chair
Yoonsuck Choe, Texas A and M University, USA

Program Chair
Christina Jayne, Robert Gordon University, UK

Technical Co-Chairs
Irwin King, The Chinese University of Hong Kong, China
Barbara Hammer, University of Bielefeld, Germany

Sponsoring Organizations

  • INNS - International Neural Network Society
  • IEEE - Computational Intelligence Society
  • BSCS - Budapest Semester in Cognitive Science

IEEE Transactions on Fuzzy Systems, vol. 24, issue 5, 2016

1. On Pythagorean and Complex Fuzzy Set Operations
Author(s): Scott Dick; Ronald R. Yager; Omolbanin Yazdanbakhsh
Pages: 1009- 1021

2. Classification of Type-2 Fuzzy Sets Represented as Sequences of Vertical Slices
Author(s): Lorenzo Livi; Hooman Tahayori; Antonello Rizzi; Alireza Sadeghian; Witold Pedrycz
Pages: 1022- 1034

3. Power Average of Trapezoidal Intuitionistic Fuzzy Numbers Using Strict t-Norms and t-Conorms
Author(s): Shu-Ping Wan; Zhi-Hong Yi
Pages: 1035- 1047

4. Aperiodic Sampled-Data Sliding-Mode Control of Fuzzy Systems With Communication Delays Via the Event-Triggered Method
Author(s): Shiping Wen; Tingwen Huang; Xinghuo Yu; Michael Z. Q. Chen; Zhigang Zeng
Pages: 1048- 1057

5. Fault Detection and Isolation for Affine Fuzzy Systems With Sensor Faults
Author(s): Huimin Wang; Guang-Hong Yang; Dan Ye
Pages: 1058- 1071

6. Knowledge Measure for Atanassov's Intuitionistic Fuzzy Sets
Author(s): Kaihong Guo
Pages: 1072- 1078

7. Takagi–Sugeno–Kang Transfer Learning Fuzzy Logic System for the Adaptive Recognition of Epileptic Electroencephalogram Signals
Author(s): Changjian Yang; Zhaohong Deng; Kup-Sze Choi; Shitong Wang
Pages: 1079- 1094

8. A Survey of Adaptive Fuzzy Controllers: Nonlinearities and Classifications
Author(s): Meng Joo Er; Sayantan Mandal
Pages: 1095- 1107

9. Optimal Design of Constraint-Following Control for Fuzzy Mechanical Systems
Author(s): Ruiying Zhao; Ye-Hwa Chen; Shengjie Jiao
Pages: 1108- 1120

10. Multiscale Opening of Conjoined Fuzzy Objects: Theory and Applications
Author(s): Punam K. Saha; Subhadip Basu; Eric A. Hoffman
Pages: 1121- 1133

11. Decentralized State Feedback Control of Uncertain Affine Fuzzy Large-Scale Systems With Unknown Interconnections
Author(s): Huimin Wang; Guang-Hong Yang
Pages: 1134- 1146

12. Fuzzy Adaptive Control With State Observer for a Class of Nonlinear Discrete-Time Systems With Input Constraint
Author(s): Yan-Jun Liu; Shaocheng Tong; Dong-Juan Li; Ying Gao
Pages: 1147- 1158

13. Fuzzy-Based Goal Representation Adaptive Dynamic Programming
Author(s): Yufei Tang; Haibo He; Zhen Ni; Xiangnan Zhong; Dongbin Zhao; Xin Xu
Pages: 1159- 1175

14. Nonparametric Statistical Active Contour Based on Inclusion Degree of Fuzzy Sets
Author(s): Maoguo Gong; Hao Li; Xiang Zhang; Qiunan Zhao; Bin Wang
Pages: 1176- 1192

15. The Role of Crisp Elements in Fuzzy Ontologies: The Case of Fuzzy OWL 2 EL
Author(s): Fernando Bobillo
Pages: 1193- 1209

16. Transfer Prototype-Based Fuzzy Clustering
Author(s): Zhaohong Deng; Yizhang Jiang; Fu-Lai Chung; Hisao Ishibuchi; Kup-Sze Choi; Shitong Wang
Pages: 1210- 1232

17. Observer-Based Fuzzy Control for Nonlinear Networked Systems Under Unmeasurable Premise Variables
Author(s): Hongyi Li; Chengwei Wu; Shen Yin; Hak-Keung Lam
Pages: 1233- 1245

18. Adaptive Fuzzy Hysteresis Internal Model Tracking Control of Piezoelectric Actuators With Nanoscale Application
Author(s): Pengzhi Li; Peiyue Li; Yongxin Sui
Pages: 1246- 1254

Tuesday 1 November 2016

IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 11, November 2016

1. Decomposition Techniques for Multilayer Perceptron Training
Author: Luigi Grippo; Andrea Manno; Marco Sciandrone
Page(s): 2146 - 2159

2. Learning Robust and Discriminative Subspace With Low-Rank Constraints
Authors: Sheng Li; Yun Fu
Page(s): 2160 - 2173

3. Decentralized Dimensionality Reduction for Distributed Tensor Data Across Sensor Networks
Authors: Junli Liang; Guoyang Yu; Badong Chen; Minghua Zhao
Page(s): 2174 - 2186

4. Learning Transferred Weights From Co-Occurrence Data for Heterogeneous Transfer Learning
Authors: Liu Yang; Liping Jing; Jian Yu; Michael K. Ng
Page(s): 2187 - 2200

5. Multiple Representations-Based Face Sketch–Photo Synthesis
Authors: Chunlei Peng; Xinbo Gao; Nannan Wang; Dacheng Tao; Xuelong Li; Jie Li
Page(s): 2201 - 2215

6. RBoost: Label Noise-Robust Boosting Algorithm Based on a Nonconvex Loss Function and the Numerically Stable Base Learners
Authors: Qiguang Miao; Ying Cao; Ge Xia; Maoguo Gong; Jiachen Liu; Jianfeng Song
Page(s): 2216 - 2228

7. Estimating Sensorimotor Mapping From Stimuli to Behaviors to Infer C. elegans Movements by Neural Transmission Ability Through Connectome Databases
Authors: Cheng-Wei Li; Chung-Chuan Lo; Bor-Sen Chen
Page(s): 2229 - 2241

8. A Comparison of Algorithms for Learning Hidden Variables in Bayesian Factor Graphs in Reduced Normal Form
Authors: Francesco A. N. Palmieri
Page(s): 2242 - 2255

9. Sparse Bayesian Classification of EEG for Brain–Computer Interface
Authors: Yu Zhang; Guoxu Zhou; Jing Jin; Qibin Zhao; Xingyu Wang; Andrzej Cichocki
Page(s): 2256 - 2267

10. Robust Kernel Low-Rank Representation
Authors: Shijie Xiao; Mingkui Tan; Dong Xu; Zhao Yang Dong
Page(s): 2268 - 2281

11. Improving on Deterministic Approximate Bayesian Inferences for Mixture Distributions
Authors: Yohei Nakada
Page(s): 2282 - 2300

12. Optimizing Single-Trial EEG Classification by Stationary Matrix Logistic Regression in Brain–Computer Interface
Authors: Hong Zeng; Aiguo Song
Page(s): 2301 - 2313

13. Online Learning ARMA Controllers With Guaranteed Closed-Loop Stability
Authors: Savaş Şahin; Cüneyt Güzeliş
Page(s): 2314 - 2326

14. Feature Extraction Using Memristor Networks
Authors: Patrick M. Sheridan; Chao Du; Wei D. Lu
Page(s): 2327 - 2336

15. Exponential Stability and Stabilization of Delayed Memristive Neural Networks Based on Quadratic Convex Combination Method
Authors: Zhanshan Wang; Sanbo Ding; Zhanjun Huang; Huaguang Zhang
Page(s): 2337 - 2350

16. Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming
Authors: Yi Cao; Yuhua Li; Sonya Coleman; Ammar Belatreche; Thomas Martin McGinnity
Page(s): 2351 - 2363

17. Multi-AUV Target Search Based on Bioinspired Neurodynamics Model in 3-D Underwater Environments
Authors: Xiang Cao; Daqi Zhu; Simon X. Yang
Page(s): 2364 - 2374

18. A Consistent Model for Lazzaro Winner-Take-All Circuit With Invariant Subthreshold Behavior
Authors: Ruxandra L. Costea; Corneliu A. Marinov
Page(s): 2375 - 2385

19. Asymptotically Stable Adaptive–Optimal Control Algorithm With Saturating Actuators and Relaxed Persistence of Excitation
Authors: Kyriakos G. Vamvoudakis; Marcio Fantini Miranda; João P. Hespanha
Page(s): 2386 - 2398

20. Identification of Nonlinear Spatiotemporal Dynamical Systems With Nonuniform Observations Using Reproducing-Kernel-Based Integral Least Square Regulation
Authors: Hanwen Ning; Guangyan Qing; Xingjian Jing
Page(s): 2399 - 2412

21. Echo State Networks With Orthogonal Pigeon-Inspired Optimization for Image Restoration
Authors: Haibin Duan; Xiaohua Wang
Page(s): 2413 - 2425

22. Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction
Authors: Guoxu Zhou; Andrzej Cichocki; Yu Zhang; Danilo P. Mandic
Page(s): 2426 - 2439

23. Synchronization Control of Neural Networks With State-Dependent Coefficient Matrices
Authors: Junfeng Zhang; Xudong Zhao; Jun Huang
Page(s): 2440 - 2447

24. Efficient χ2 Kernel Linearization via Random Feature Maps
Authors: Xiao-Tong Yuan; Zhenzhen Wang; Jiankang Deng; Qingshan Liu
Page(s): 2448 - 2453