Tuesday, 22 September 2015

Call for Papers to IEEE TFS 2015 Special Issue on Brain Computer Interface (BCI)

The submission deadline has been extended from October 1, 2015 to November 1, 2015


Brain computer interfaces (BCIs) have attracted rapidly increasing research interest in the last decade, thanks to recent advances in neurosciences, wearable/mobile biosensors, and analytics. However, there are many challenges in the transition from their laboratory settings to real-life applications, including the reliability and convenience of the sensing hardware, the availability of high-performance and robust algorithms for signal analysis and interpretation, and fundamental advances in automated reasoning that enable the reasoning and generalization across individuals.

Computational intelligence techniques, particularly fuzzy sets and systems, have demonstrated superior performance in handling uncertainties in many real-world applications. They have also started attracting more attentions in the BCI domain. More specifically, fuzzy sets and systems have been used in electroencephalogram (EEG) feature extraction (e.g., self-organizing fuzzy neural networks, fuzzy region of interest, fuzzy wavelet packet), pattern recognition (e.g., fuzzy ARTMAP, type-1 and type-2 fuzzy logic systems, fuzzy-neural systems, fuzzy c-means clustering, fuzzy integrals, fuzzy SVM, fuzzy similarity, rough sets), optimization (e.g., fuzzy particle swarm optimization), etc..

This special issue aims at showcasing the most exciting and recent advances in fuzzy sets and systems for BCI and related topics. It welcomes survey, position, research, and application papers.


The topics include but are not limited to:
  • Fuzzy control for BCI
  • Fuzzy signal processing for BCI/EEG/ ECoG/ MEG/MRI
  • Fuzzy feature extraction for BCI/EEG/ ECoG/ MEG/MRI
  • Fuzzy pattern recognition (classification, regression) for BCI/EEG/ ECoG/MEG/MRI
  • Fuzzy sets and systems for handling uncertainties and individual differences in BCI
  • Fuzzy approaches applied to data fusion of EEG with other physiological and contextual sensing modalities
  • Hybrid approaches that combine fuzzy sets and systems with machine learning, data mining, or other computational intelligence techniques for BCI


  • November 1, 2015: Submission deadline
  • January 1, 2016: Notification of 1st round review
  • February 1, 2015: Revised submission due
  • April 1, 2016: Final notice of acceptance/reject
  • August 2016: Special issue publication


Manuscripts should be prepared according to the instruction of the “Information for Authors” section of the journal found and submission should be done through the IEEE TFS journal website: http://mc.manuscriptcentral.com/tfs-ieee.  Clearly mark “Special Issue on Brain Computer Interfaces” in your cover letter to the Editor-in-Chief.  All submitted manuscripts will be reviewed using the standard procedure that is followed for regular submissions.


Dr. Dongrui Wu
DataNova, USA

Dr. Brent Lance
Translational Neuroscience Branch
Army Research Laboratory, USA

Dr. Vernon Lawhern
Translational Neuroscience Branch
Army Research Laboratory, USA

Call for Papers IEEE Computational Intelligence Magazine Special Issue on Model Complexity, Regularization and Sparsity


Aims and Scope

The effective management of solution complexity is one of the most important issues in addressing Computational Intelligence problems. Regularization techniques control model complexity by taking advantage of some prior information regarding the problem at hand, represented as penalty expressions that impose these properties on the solution. Over the past few years, one of the most prominent and successful types of regularization has been based on the sparsity prior, which promotes solutions that can be expressed as a linear combination of a few atoms belonging to a dictionary. Sparsity can in some sense be considered a measure of simplicity and, as such, is compatible with many nature-inspired principles of Computational Intelligence. Nowadays, sparsity has become one of the leading approaches for learning adaptive representations for both descriptive and discriminative tasks, and has been shown to be particularly effective when dealing with structured, complex and high-dimensional data.

Regularization, including sparsity and other priors to control the model complexity, is often the key ingredient in the successful solution of difficult problems; it is therefore not surprising that these aspects have also recently gained a lot of attention in big-data processing, due to unprecedented challenges associated with the need to handle massive datastreams that are possibly high-dimensional and organized in complex structures.

This special issue aims at presenting the most relevant regularization techniques and approaches to control model complexity in Computational Intelligence. Submissions of papers presenting regularization methods for Neural Networks, Evolutionary Computation or Fuzzy Systems, are welcome. Submissions of papers presenting advanced regularization techniques in specific, but relevant, application fields such as data/datastream-mining, classification, big-data analytics, image/signal analysis, natural-language processing, are also encouraged.

Topics of Interest

  • Regularization methods for big and high-dimensional data;
  • Regularization methods for supervised and unsupervised learning;
  • Regularization methods for ill-posed problems in Computational Intelligence;
  • Techniques to control model complexity;
  • Sparse representations in Computational Intelligence;
  • Managing model complexity in data analytics;
  • Effective priors for solving Computational Intelligence problems;
  • Multiple prior integration;
  • Regularization in kernel methods and support vector machines.

Important Dates

  • 22nd January, 2016: Submission of Manuscripts
  • 30th March, 2016: Notification of Review Results
  • 30th April, 2016: Submission of Revised Manuscripts
  • 15th June, 2016: Submission of Final Manuscripts
  • November, 2016: Special Issue Publication

Submission Process

The maximum length for the manuscript is typically 20 pages in single column with double-spacing, including figures and references. Authors of papers should specify in the first page of their manuscripts the corresponding author's contact and up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website.

Submission should be made via at https://easychair.org/conferences/?conf=ieeecim1116

Guest Editors

Prof. Cesare Alippi,
Dipartimento di Elettronica, Informazione e Biongegneria, Politecnico di Milano,
via Ponzio 34/5, Milano, 20133, Italy
email: cesare.alippi@polimi.it

Dr. Giacomo Boracchi,
Dipartimento di Elettronica, Informazione e Biongegneria, Politecnico di Milano,
via Ponzio 34/5, Milano, 20133, Italy
email: giacomo.boracchi@polimi.it

Dr. Brendt Wohlberg,
Theoretical Division, Los Alamos National Laboratory,
Los Alamos NM 87545, USA
email: brendt@lanl.gov

Tuesday, 15 September 2015

Call for Papers WCCI 2016 Special Session Computational Intelligence in Ecological Informatics and Environmental Modelling


Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. The amount of data describing global and local environments and the eco-systems that inhabit them is rapidly increasing. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling. This is a highly topical area and is open to a broad array of methods from the field of computational intelligence.


The scope of the special session includes, but is not limited to, the following topics:
  • Species distribution and ecological niche modelling
  • Predicting species abundance
  • Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
  • Analysis of species assemblages
  • Classification of species
  • Environmental impact assessment
  • Modelling of environmental events including floods
  • Issues in the preparation of ecological data for modelling
  • Modelling of pollutants or contamination in air, land or water
  • Modelling water quality
  • Greenhouse gas emissions modelling and the effects of climate change
  • Modelling the future development of populations
  • Detecting landscape features
  • Modelling water drainage systems
  • Assessment of habitat quality
  • Forecasting of algal blooms
  • Habitat suitability modelling
  • Predicting crop hazards, pests or diseases
  • Modelling interactions between multiple species
  • Identifying landscape features
  • Modelling ecosystem biomass
  • Learning of phenological patterns


Important Dates

  • Paper submission deadline: 15th January 2016
  • Author notification: 15th March 2016
  • Deadline for final manuscript: 15th April 2016
  • Early registration deadline: 15th April 2016
  • Conference dates: 25th July-29th July 2016

Paper Submission As this is a cross-disciplinary special session, please submit papers via the IEEE CEC 2016 submission site: http://ieee-cis.org/conferences/cec2016/upload.php Please make sure you select "Computational Intelligence in Ecological Informatics and Environmental Modelling" under "Cross-Disciplinary and CI Applications Special Sessions" as the Main Research Topic of your submission. Accepted papers will be published in the conference proceedings that are most appropriate for the paper (IJCNN, FUZZ-IEEE or CEC). This decision will be made by the Special Session Organisers in consultation with the Special Session Chair and one of the three Conference Chairs.



  • Dr Michael J. Watts, Auckland Institute of Studies, Auckland, New Zealand. mjwatts@ieee.org
  • Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn


Dr Michael J. Watts

Dr Michael J. Watts (Senior Member, IEEE) is the Academic Head of Programme for Information Technology at Auckland Institute of Studies, Auckland, New Zealand. His research interests are in the areas of ecological informatics, neural networks, neuro-fuzzy systems, and evolutionary algorithms. He is particularly interested in the intersections of these areas, where computational intelligence methods can be applied to ecological informatics. He has published more than 70 peer-reviewed papers in a range of international journals and conferences on a variety of topics, particularly computational intelligence and ecological informatics. He is currently on the editorial boards of the Springer journal Soft Computing and the IEEE Transactions on Neural Networks and Learning Systems. He teaches basic database engineering, fundamentals of programming, data mining and information security and serves on a number of committees of the IEEE Computational Intelligence Society including the Neural Networks Technical Committee and the Standards Committee.


Professor Jie Yang

Jie Yang is the Professor and Director of the Institute of Image Processing and Pattern recognition in Shanghai Jiao Tong University. He received a bachelor’s degree in Automatic Control in Shanghai Jiao Tong University, where a master’s degree in Pattern Recognition & Intelligent System was achieved three years later. In 1994, he received Ph.D. at Department of Computer Science, University of Hamburg, Germany. He is the principal investigator of more than 30 national and ministry scientific research projects in image processing, pattern recognition, data mining, and artificial intelligence, including two national 973 research plan projects, three national 863 research plan projects, three National Nature Foundation projects, five international cooperative projects with France, Korea, Japan, New Zealand. He has published four books and more than four hundred articles in national or international academic journals and conferences. Up to now, he has supervised 4 postdoctoral, 32 doctors and 55 masters, and been awarded five research achievement prizes from ministry of Education, China and Shanghai municipality. One Ph.D. dissertation he supervised was evaluated as "National Best Ph.D. Dissertation" in 2009. Two Ph.D. dissertations he supervised were evaluated as "Shanghai Best Ph.D. Dissertation" in 2009 and 2010. He has owned 25 patents.

Sunday, 13 September 2015

Call for participation: IEEE DSAA’2015

IEEE 2015 International Conference on Data Science and Advanced Analytics
(IEEE DSAA'2015)
19-21 October, 2015, Paris, France
Website: http://dsaa2015.lip6.fr/


DSAA2015 will feature
  • 31 main track Long presentations (acceptance rate 9%) and 50 main track Short presentations (acceptance rate 14.7%)
  • 4 keynote speeches to be delivered by prestigious academic and industry leaders, 
  • 7 invited prestigious speakers about the trends and controversies of data science and big data analytics, 
  • 8 special sessions on emerging interesting data science and analytics topics, consisting of 43 special session presentations
  • the Application track, an industry session and exhibitions to highlight the best practices and industry applications, and 
  • a panel jointly held with IEEE Big Data Initiative will include high profile panelists from data science, big data, computational intelligence, data mining, machine learning, statistics etc.

Technical Sessions

DSAA'2015 consists of the following main and special session tracks:
  • Research Track
  • Applications Track
  • Industry session
  • Special Session on Trends & Controversies in Data Science (TCDS, by invitation only)
  • Special Session on Big Behavioral Data Analytics (BBDA)
  • Special Session on Big Data, Distributed Technologies and Intelligent Agents (BDIA)
  • Special Session on Bioinformatics, Health and Medical Analytics (BHMA)
  • Special Session on Data Oriented Constructive Mining and Multi-Agent Simulation (DOCMAS)
  • Special session on Emotion and Sentiment in Intelligent Systems and Big Social Data Analysis (SentISData)
  • Special Session on Environmental and Geo-spatial Data Analytics (EnGeoData)
  • Special Session on Exploratory Computing (EC)
  • Special Session on Statistical and Mathematical Tools for Data Science (SMTDS)
  • Exhibitions


Early-bird registration due: 2 October, 2015
Website: http://dsaa2015.lip6.fr/

Website & Enquiries

DSAA'2015: http://dsaa2015.lip6.fr/
Contacts:  dsaa2015@dsaa.co

Call for Papers WCCI 2016 Special Session: "Computational Intelligence in Bioinformatics (CIB)"


Bioinformatics, computational biology, and bioengineering present a number of complex problems with large search spaces. Recent applications of Computational Intelligence (CI) in this area suggest that they are well-suited to this area of research. This special session will highlight applications of CI to a broad range of topics. Particular interest will be directed towards novel applications of CI approaches to problems in these areas. The scope of this special session includes evolutionary computation, neural computation, fuzzy systems, artificial immune systems, swarm intelligence, ant-colony optimization, simulated annealing, and other CI methods or hybridizations between CI approaches. Applications of these CI methods to bioinformatics, computational biology, and bioengineering problems are the main focus of this hybrid special session. There is a clear interest in both the Computational Intelligence community and Biology communities for this special session. This hybrid special session is sponsored by the IEEE CIS BBTC (Computational Intelligence Society - Bioinformatics and Bioengineering Technical Committee).

Main Topics:

Analysis and visualization of large biological data sets, biological and medical ontologies, biomedical data modelling and mining, biomedical model parameterization, brain computer interface, computational proteomics, systems biology, ecoinformatics and applications to ecological data analysis, emergent properties in complex biological systems, gene expression array analysis, gene finding, genetic networks, high-throughput data analysis, immuno- and chemo-informatics, in-silico optimization of biological systems, medical image analysis, medical imaging and pattern recognition, medicine and health informatics, metabolic pathway analysis, microarray design or oligonucleotide selection, modelling, simulation and optimization of biological systems, molecular docking and drug design, molecular evolution and phylogenetics, molecular sequence alignment and analysis, motif and signal detection, robustness and evolvability of biological networks, single nucleotide polymorphism (SNP) analysis, structure prediction and folding, systems and synthetic biology, and treatment optimization.

Paper submission:

The special session on Computational Intelligence in Bioinformatics is soliciting high quality papers of original research and application papers that have not been published elsewhere and are not under consideration for publication elsewhere. Manuscripts should be prepared according to the standard format and page limit of regular papers specified in the WCCI 2016 website. All papers will be rigorously reviewed by at least two reviewers. Accepted papers will be treated in the same way as regular papers and published in the WCCI 2016 conference proceedings.

Short Biography of the Organizers:

Vassilis Plagianakos
Department of Computer Science and Biomedical Informatics
University of Thessaly, Greece

Roberto Tagliaferri
Department of Computer Science
University of Salerno, Italy

Short CVs

Vassilis Plagianakos received both his B.Sc. and Ph.D. degrees in Mathematics from the University of Patras, Greece. He is currently Assistant Professor and Head of the Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece. His research interests are mainly focused on the areas of neural networks and machine learning, applications in pattern recognition, intelligent decision making, evolutionary and genetic algorithms, parallel and distributed computations, bioinformatics, clustering, intelligent optimization, and real-world problem solving. He has published more than 30 journal and 70 conference papers, and his published work has received more than 700 citations. He serves as a reviewer in numerous journals and conferences. He was a co-organizer of the bioinformatics hybrid special sessions at WCCI 2012 and WCCI 2014. He is an IEEE CIS BBTC member.

Roberto Tagliaferri is full professor in Computer Science at the University of Salerno. He has had courses in Computer Architecture, Artificial and Computational Intelligence, and Bioinformatics for computer scientists and biologists. He has been co-organizer of international workshops, special sessions and schools on Neural Nets, Computational Intelligence and Bioinformatics and co-editor of 18 books and of 5 Special Issues on International Journals. His research activity has been oriented to Computational Intelligence models and applications in the areas of Astrophysics, Biomedicine and Bioinformatics. His activity produced more than 150 scientific papers published on international journals or proceedings of international conferences with referee. He is an IEEE CIS BBTC member and Chair of the Italy Chapter of the IEEE CIS. He is associate editor of the IEEE Transactions on Cybernetics and of the BMC journal Source Code for Biology and Medicine.

Friday, 11 September 2015

IEEE Transactions on Computational Intelligence and AI in Games, Volume 7, Number 3, September 2015

1. The Age of Analytics
Author(s): Bauckhage, C.; Drachen, A.; Thurau, C.
Page(s): 205 - 206

2. An Analytic and Psychometric Evaluation of Dynamic Game Adaption for Increasing Session-Level Retention in Casual Games
Author(s): Harrison, B.; Roberts, D.L.
Page(s): 207 - 219

3. Detecting Predatory Behavior in Game Chats
Author(s): Cheong, Y.-G.; Jensen, A.K.; Gudnadottir, E.R.; Bae, B.-C.; Togelius, J.
Page(s): 220 - 232

4. Player Preference and Style in a Leading Mobile Card Game
Author(s): Cowling, P.I.; Devlin, S.; Powley, E.J.; Whitehouse, D.; Rollason, J.
Page(s): 233 - 242

5. Thinking Style and Team Competition Game Performance and Enjoyment
Author(s): Wang, H.; Yang, H.-T.; Sun, C.-T.
Page(s): 243 - 254

6. Churn Prediction in Online Games Using Players’ Login Records: A Frequency Analysis Approach
Author(s): Castro, E.G.; Tsuzuki, M.S.G.
Page(s): 255 - 265

7. Clustering Game Behavior Data
Author(s): Bauckhage, C.; Drachen, A.; Sifa, R.
Page(s): 266 - 278

8. Postprocessing Gameplay Metrics for Gameplay Performance Segmentation Based on Audiovisual Analysis
Author(s): Marczak, R.; Schott, G.; Hanna, P.
Page(s): 279 - 291

9. Past Our Prime: A Study of Age and Play Style Development in Battlefield 3
Author(s): Tekofsky, S.; Spronck, P.; Goudbeek, M.; Plaat, A.; van den Herik, J.
Page(s): 292 - 303

10. On Learning From Game Annotations
Author(s): Wirth, C.; Furnkranz, J.
Page(s): 304 - 316

IJCNN/WCCI 2016 Call For Special Sessions

25-29, July, 2016
Vancouver, Canada

Special session proposals for are invited to the 2016 IEEE World Congress on Computational Intelligence/International Joint Conference on Neural Networks (WCCI/IJCNN). Special session proposals should include the title, aim and scope (including a list of main topics), and the names of the organizers of the special session, together with a short biography of all organizers. A list of potential contributors will be very helpful.

Special session proposals will be evaluated based on the timeliness, uniqueness of the topic and qualifications of the proposers. The proposers are expected to have a PhD degree and have a good publication track record in the proposed area. After review, a decision on whether the proposal will be provisionally accepted will be sent to the proposers within two weeks after receipt of the proposals. Provisionally accepted special sessions will be listed on the website. However, it is likely that a provisionally accepted proposal will be combined with another one to avoid multiple special sessions covering a similar topic.

Manuscripts submitted to special sessions should be done through the paper submission website of IEEE WCCI 2016. It is the responsibility of the special session organizers to make sure that papers submitted to their special session clearly indicate the name of the special session the paper belongs to. All papers submitted to special sessions will be subject to the same peer-review procedure as the regular papers. Special sessions having fewer than four accepted papers will be cancelled and the accepted papers will be moved to regular sessions.

Special sessions proposals should be submitted to ijcnn16ss@gmail.com

Wednesday, 9 September 2015

Updated: Call for Papers WCCI 2016 Special Session "Computational Intelligence for Physiological and Affective Computing"

Affective Computing (AC) is "computing that relates to, arises from, or deliberately influences emotions," as initially coined by Professor R. Picard (Media Lab, MIT). It has been gaining popularity rapidly in the last decade because it has great potential in the next generation of human-computer interfaces. One goal of affective computing is to design a computer system that responds in a rational and strategic fashion to real-time changes in user affect (e.g., happiness, sadness, etc.), cognition (e.g., frustration, boredom, etc.) and motivation, as represented by speech, facial expressions, gestures, physiological signals, neurocognitive performance, etc. Physiological Computing (PC) relates to computation that incorporates physiological signals in order to produce useful outputs (e.g., in computer-human interaction). It mainly differs from AC in the sense that its foremost focus is not the modeling of affect but rather the utilization of physiological information generally.

Practical applications of AC and PC based systems seek to achieve a positive impact on our everyday lives by monitoring, recognising and acting on our emotional states and physiological signals. Integrating these sensing modalities into intelligent and pervasive computing systems will reveal a far richer picture of how our fleeting emotional responses, changing moods, feelings and sensations, such as pain, touch, tastes and smells, are a reaction to or influence how we implicitly or explicitly interact with the environment and increasingly the connected computing artifacts within.

The integration and use of AC and PC raise many new challenges for signal processing, machine learning and computational intelligence. Fuzzy Logic Systems in particular provide a highly promising avenue for addressing some of the fundamental research challenges in AC/PC where most data sources such as: body signals (e.g., heart rate, brain waves, skin conductance and respiration) facial features, speech and human kinematics are very noisy/uncertain and subject-dependent. Clearly however, other key areas of Computational Intelligence (CI) research, such as evolutionary learning algorithms and neural network based classifiers provide essential tools to address the significant challenge of AC/PC.

The Computational Intelligence for Physiological and Affective Computing special session aims to bring together researchers from the three areas of CI to discuss how CI techniques can be used individually or in combination to help solve challenging AC/PC problems, and conversely, how physiological and affect (emotion) and its modeling can inspire new approaches in CI and its applications. Topics of interest for this special session include but are not limited to:
  • Models of emotion and physiological information
  • Classifiers for physiological information
  • Applications based on/around physiological information
  • Fuzzy set and system based architectures for processing emotions and other affective states
  • Automatic emotion recognition & synthesis from physiological signals, facial expressions, body language, speech, or neurocognitive performance
  • Multimodal sensor fusion for emotion recognition
  • Emotion mining from texts, images, or videos
  • Affective interaction with virtual agents and robots based on fuzzy systems
  • Physiological and emotion driven control
  • Applications of affective computing in interactive learning, affective gaming, personalized robotics, virtual reality, social networking, smart environments, healthcare and behavioral informatics, etc.

As a cross-disciplinary and CI applications oriented special session, accepted and presented papers will be published under one of the three conference proceedings (Fuzzy-IEEE, IJCNN or IEEE CEC) that are most appropriate for the presented research. For paper submissions please visit WCCI IEEE 2016 submissions: http://www.wcci2016.org/submission.php

Important Dates

Paper submission deadline: 15th January 2016
Author notification: 15th March 2016
Deadline for final manuscript: 15th April 2016
Early registration deadline: 15th April 2016
Conference dates: 25th July – 29th July 2016

Program Committee

Egon L. van den Broek, University of Twente, The Netherlands
Hani Hagras, University of Essex, UK
Marie-Jeanne Lesot, LIP6-UPMC, France
Chin-Teng Lin, National Chiao Tung University, Taiwan
Jiann-Shing Shieh, Yuan Ze University, Taiwan
Shrikanth Narayanan, University of Southern California, USA
Ana Paiva, Technical University of Lisbon, Portugal
Rahat Iqbal, Coventry University, UK
Dr. Vernon Lawhern, US Army Research Lab
Mei Si, Rensselaer Polytechnic Institute, USA
Carlo Strapparava, Fondazione Bruno Kessler, Italy
Shangfei Wang, University of Science and Technology of China, China
Dongrui Wu, GE Global Research, USA
Georgios Yannakakis, IT University of Copenhagen, Denmark
Slawomir Zadrozny, Polish academy of science, Poland
Michel Valstar, University of Nottingham, UK
Christopher Peters, KTH Royal Institute of Technology, Sweden
Palaniappan Ramaswamy, University of Kent, UK
Ahmed Kattan, Um Al-Qura University, Saudi Arabia
Brent Lance, US Army Research Lab
Victor Zamudio, Instituto Tecnologico de Leon, Mexico
Vasile Palade, Coventry University, UK
Li-Wei Ko, National Chiao Tung University, Taiwan
Nicolas Sabouret, University Paris-Sud, France
Maria Rifqi, University Panthéon-Assas, France


Dr Faiyaz Doctor
School of Computing, Electronics and Maths
Faculty of Engineering, Environment & Computing
Coventry University
Email: faiyaz.doctor AT coventry.ac.uk
Dr. Faiyaz Doctor is a Senior Lecturer in the Faculty of Engineering and Computing at Coventry University. He has previously worked jointly in industry and academia to develop novel artificial intelligence solutions for addressing real world problems related to smart environments, energy optimization, predictive analytics and decision support. His work has resulted in high profile innovation awards (Best KTP Regional Finalist 2011, Load Stafford Award for Innovation) and an international patent on improved approaches for data analysis and decision-making using hybrid neuro-fuzzy and type-2 fuzzy systems: WO/2009/141631. He has previously been a co-investigator on a Technology Strategy Board funded project on developing driver prediction models using artificial intelligence approaches in collaboration with Jaguar Land Rover Ltd. His research interests are in the area of computational intelligence with an emphasis on fuzzy logic, type-2 fuzzy logic and hybrid systems where his research has been applied to ambient intelligence, pervasive and affective computing, industrial automation and biomedical systems. Dr. Doctor has published over 40 papers in peer reviewed international journals, conferences and workshops. He currently chairs the IEEE Computational Intelligence Society’s Emergent Technologies ‘Affective Computing’ Task Force and has been co-organizer of the special session on Fuzzy Systems for Physiological and Affective Computing (FSPAC) at the 2015 IEEE International Conference on Fuzzy Systems and the special session on Computational Intelligence for Physiological and Affective Computing (CIPAC) and co-chair at the special session on Brain and Physiological Computation for Affective Computing at the 2014 IEEE World Congress on Computational Intelligence. He has been a guest editor for the Journal of Ambient Intelligence and Smart Environments (JAISE), Thematic Issue on Affect Aware Ubiquitous Computing and has served as co-organiser of the International Workshop on Applications of Affective Computing in Intelligent Environments (ACIE 2013, 2014) in conjunction with the International Conference on Intelligent Environments. He is also and member of the IEEE and IEEE Computational Intelligence Society.

Dr Christian Wagner
Horizon Digital Economy Institute & Intelligent Modeling and Analysis Group
School of Computer Science
University of Nottingham, UK
Email: christian.wagner AT nottingham.ac.uk
Christian Wagner is an Associate Professor in Computer Science at the University of Nottingham, UK. He received his PhD in Computer Science from the University of Essex in 2009 after which he was involved both in the management and scientific work of the EU FP7 project ATRACO, joining the University of Nottingham in 2011. His main research interests are centred on uncertainty handling, approximate reasoning (reasoning in the face of uncertainty, lack of knowledge and vagueness), decision support and data-driven decision making using computational intelligence techniques. Recent applications of his research have focused in particular on decision support in environmental and infrastructure planning & management contexts as well as cyber-security. He has published more than 60 peer-reviewed articles in international journals and conferences, two of which recently won best paper awards (Outstanding IEEE Transactions on Fuzzy Systems paper 2013 (for a paper in 2010) and a best paper award for a Fuzz-IEEE 2012 conference paper), and several book chapters. Dr Wagner is currently active PI and Co-I on a number of research projects, with overall funding as PI of £1 million and funding as Co-I of 2 million. He is an Associate Editor of the IEEE Transactions on Fuzzy Systems journal (IF: 6.3) and is actively involved in the academic community through for example the organization of special sessions at premiere IEEE conference such as the World Congress on Computational Intelligence 2014 and the IEEE Conference on System, Man and Cybernetics 2015. He has developed and been involved in the creation of multiple open source software frameworks, making cutting edge research accessible both to peer researchers as well as to different (multidisciplinary - beyond computer science) research and practitioner communities, including R and Java based toolkits for type-2 fuzzy systems in use in more than ten countries.

Dr Dongrui Wu
DataNova, NY, USA
Email: drwu09 AT gmail.com
Dongrui Wu received a PhD in Electrical Engineering from the University of Southern California (USC) in 2009. He was a Research Associate in the USC Institute for Creative Technologies and Signal Analysis & Interpretation Laboratory (SAIL) 2009-2010, and a Lead Research Engineer in the Machine Learning Lab of GE Global Research 2010-2015. Now he is starting his own business. Dr. Wu's research interests include affective computing, brain-computer interaction, computational intelligence, intelligent control, machine learning, optimization, and text analysis. He has over 80 publications, including a book "Perceptual Computing." He received IEEE Computational Intelligence Society (CIS) Outstanding PhD Dissertation Award in 2012, IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, and NAFIPS Early Career Award in 2014. He was a selected participant of the 1st Heidelberg Abel/Fields/Turing Laureate Forum in 2013, NAE 2015 German-American Frontiers of Engineering, and the 13th Annual National Academies Keck Futures Initiative (NAKFI) conference in 2015. Dr. Wu had worked on a broad range of projects from GE Capital, Healthcare, Transportation, Power and Water, and Oil & Gas. Two of his projects won the prestigious CIO 100 Awards in 2012 (TrueSense for GE Water) and 2014 (Fleet Optimizer for GE Capital), respectively. Additionally, he received 10 Above and Beyond Awards for outstanding performance. Dr. Wu is an Associate Editor of IEEE Transactions on Fuzzy Systems, IEEE Transactions on Human-Machine Systems, and PeerJ Computer Science. He was the lead Guest Editor of the IEEE Computational Intelligence Magazine Special Issue on Computational Intelligence and Affective Computing, and former Chair of the IEEE CIS Affective Computing Task Force. He is a Senior Member of IEEE, and an Executive Committee member of the Association for the Advancement of Affective Computing (AAAC).

Dr Marie-Jeanne Lesot
Université Pierre et Marie Curie
Laboratoire d'Informatique de Paris 6, LIP6
Email: Marie-Jeanne.Lesot AT lip6.fr
Marie-Jeanne Lesot obtained her PhD in 2005 from the University Pierre et Marie Curie in Paris. Since 2006 she has been an associate professor in the department of Computer Science Lab of Paris 6 (LIP6) and a member of the Learning and Fuzzy Intelligent systems (LFI) group. Her research interests focus on fuzzy machine learning with an objective of data interpretation and semantics integration and, in particular, to model and manage subjective information; they include similarity measures, fuzzy clustering, linguistic summaries, affective computing and information scoring. She has published 43 international journal and conference papers and co-edited a book on scalable fuzzy algorithms for data management and analysis. She has also participated in various joint projects with companies, on topics such as fraud detection, dynamic monitoring of information propagation, emotional design and emotion mining among others.

Monday, 7 September 2015

WCCI 2016 Panel Session

IEEE WCCI 2016 is pleased to confirm a Panel Session on "How to Publish Your Research Papers in IEEE CIS Transactions?". Members of the panel session include Derong Liu, Chin-Teng Lin, Kay Chen Tan, Graham Kendall, Angelo Cangelosi, Hisao Ishibuchi, Witold Pedrycz and Nikhil R. Pal (Moderator). Click goo.gl/Eg0RJg for more information.

Friday, 4 September 2015

Call for Papers WCCI 2016 Special Session on Type-2 Fuzzy Sets and Systems Applications (T2-A)

Introduction to the Special Session:

Type-2 fuzzy sets and systems are paradigms which seek to realize computationally efficient fuzzy systems with the ability to give excellent performance in the face of highly uncertain conditions.
Specifically, type-2 fuzzy sets provide a framework for the comprehensive capturing and modelling of uncertain data, which, together with approaches such as clustering and similarity measures (to name but two) provides strong capability for reasoning about and with uncertain information sources in a variety of contexts and applications.
Type-2 fuzzy systems combine the potential of type-2 fuzzy sets with the strengths of rule-based inference in order to provide highly capable inference systems over uncertain data which remain white-box systems (i.e. interpretable).

The aim of this special session is to present and focus top quality research in the areas related to the practical aspects and applications of type-2 fuzzy sets and systems. The session will also provide a forum for the academic community and industry to report on recent advances within the type-2 fuzzy sets and systems research. Topics include, but are not limited to:

  • Type-2 Applications
  • Applications including similarity and distance measures for type-2 fuzzy sets
  • Data analysis*
  • Robotics*
  • Decision Making*
  • Clustering and Classification*
  • Modelling*
  • Computing with words*
  • Type-2 Fuzzy Agents
  • Any other application area that employs type-2 fuzzy sets

* using type-2 fuzzy sets and/or fuzzy systems

Contact email: christian.wagner@nottingham.ac.uk


Dr Christian Wagner
Horizon Digital Economy Institute & Intelligent Modeling and Analysis Group
School of Computer Science
University of Nottingham, UK
Email: christian.wagner@nottingham.ac.uk
Prof Hani Hagras
School of Computer Science and Electronic Engineering
University of Essex, UK
Email: hani@essex.ac.uk

Tuesday, 1 September 2015

IEEE Transactions on Neural Networks and Learning Systems, Volume 26, Issue 9, September 2015

1. Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori
Author(s): Weisheng Chen;  Shuzhi Sam Ge; Jian Wu; Maoguo Gong
Page(s): 1842 - 1854

2. Progressive Learning Machine: A New Approach for General Hybrid System Approximation
Author(s): Yimin Yang; Yaonan Wang; Q. M. Jonathan Wu; Xiaofeng Lin; Min Liu
Page(s): 1855 - 1874

3. Artificial Electrical Morris–Lecar Neuron
Author(s): Rachid Behdad; Stephane Binczak; Alexey S. Dmitrichev; Vladimir I. Nekorkin; Jean-Marie Bilbault
Page(s): 1875 - 1884

4. Distributed Containment Control for Multiple Unknown Second-Order Nonlinear Systems With Application to Networked Lagrangian Systems
Author(s): Jie Mei; Wei Ren; Bing Li; Guangfu Ma
Page(s): 1885 - 1899

5. Training Recurrent Neural Networks With the Levenberg–Marquardt Algorithm for Optimal Control of a Grid-Connected Converter
Author(s): Xingang Fu; Shuhui Li; Michael Fairbank; Donald C. Wunsch; Eduardo Alonso
Page(s): 1900 - 1912

6. Learning Stable Multilevel Dictionaries for Sparse Representations
Author(s): Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias
Page(s): 1913 - 1926

7. Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent
Author(s): En-Liang Hu; James T. Kwok
Page(s): 1927 - 1938

8. Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling
Author(s): Stavros Ntalampiras
Page(s): 1939 - 1949

9. A Kernel Classification Framework for Metric Learning
Author(s): Faqiang Wang; Wangmeng Zuo; Lei Zhang; Deyu Meng; David Zhang
Page(s): 1950 - 1962

10. Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network
Author(s): Bo Zhao; Ruoxi Ding; Shoushun Chen; Bernabe Linares-Barranco; Huajin Tang
Page(s): 1963 - 1978

11. MTC: A Fast and Robust Graph-Based Transductive Learning Method
Author(s): Yan-Ming Zhang; Kaizhu Huang; Guang-Gang Geng; Cheng-Lin Liu
Page(s): 1979 - 1991

12. Context Dependent Encoding Using Convolutional Dynamic Networks
Author(s): Rakesh Chalasani; Jose C. Principe
Page(s): 1992 - 2004

13. An Improved TA-SVM Method Without Matrix Inversion and Its Fast Implementation for Nonstationary Datasets
Author(s): Yingzhong Shi; Fu-Lai Chung; Shitong Wang
Page(s): 2005 - 2018

14. Learning With Mixed Hard/Soft Pointwise Constraints
Author(s): Giorgio Gnecco; Marco Gori; Stefano Melacci; Marcello Sanguineti
Page(s): 2019 - 2032

15. Adaptive Synchronization of Memristor-Based Neural Networks with Time-Varying Delays
Author(s): Leimin Wang; Yi Shen; Quan Yin; Guodong Zhang
Page(s): 2033 - 2042

16. Passivity and Passification of Memristor-Based Recurrent Neural Networks With Additive Time-Varying Delays
Author(s): Rajan Rakkiyappan; Arunachalam Chandrasekar; Jinde Cao
Page(s): 2043 - 2057

17. Two Efficient Twin ELM Methods With Prediction Interval
Author(s): Kefeng Ning; Min Liu; Mingyu Dong; Cheng Wu; ZhanSong Wu
Page(s): 2058 - 2071

18. Dynamic Infinite Mixed-Membership Stochastic Blockmodel
Author(s): Xuhui Fan; Longbing Cao; Richard Yi Da Xu
Page(s): 2072 - 2085

19. Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation
Author(s): Mou Chen; Gang Tao; Bin Jiang
Page(s): 2086 - 2097

20. Robust Novelty Detection via Worst Case CVaR Minimization
Author(s): Yongqiao Wang; Chuangyin Dang; Shouyang Wang
Page(s): 2098 - 2110

21. A Distributed Approach Toward Discriminative Distance Metric Learning
Author(s): Jun Li; Xun Lin; Xiaoguang Rui; Yong Rui; Dacheng Tao
Page(s): 2111 - 2122

22. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals
Author(s): Hao Quan; Dipti Srinivasan; Abbas Khosravi
Page(s): 2123 - 2135

23. Group Factor Analysis
Author(s): Arto Klami; Seppo Virtanen; Eemeli Leppäaho; Samuel Kaski
Page(s): 2136 - 2147

24. Fick’s Law Assisted Propagation for Semisupervised Learning
Author(s): Chen Gong; Dacheng Tao; Keren Fu; Jie Yang
Page(s): 2148 - 2162

25. Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network
Author(s): Maciej Kusy; Roman Zajdel
Page(s): 2163 - 2175

26. Variational Bayesian Inference Algorithms for Infinite Relational Model of Network Data
Author(s): Takuya Konishi; Takatomi Kubo; Kazuho Watanabe; Kazushi Ikeda
Page(s): 2176 - 2181

27. Hyperparameter Selection for Gaussian Process One-Class Classification
Author(s): Yingchao Xiao; Huangang Wang; Wenli Xu
Page(s): 2182 - 2187

28. Properties and Performance of Imperfect Dual Neural Network-Based k WTA Networks
Author(s): Ruibin Feng; Chi-Sing Leung; John Sum; Yi Xiao
Page(s): 2188 - 2193

29. A Unified Framework for Data Visualization and Coclustering
Author(s): Lazhar Labiod; Mohamed Nadif
Page(s): 2194 - 2199

30. Discriminative Hierarchical K-Means Tree for Large-Scale Image Classification
Author(s): Shizhi Chen; Xiaodong Yang; Yingli Tian
Page(s): 2200 - 2205

31. Retargeted Least Squares Regression Algorithm
Author(s): Xu-Yao Zhang; Lingfeng Wang; Shiming Xiang; Cheng-Lin Liu
Page(s): 2206 - 2213

32. Online Sequential Extreme Learning Machine With Kernels
Author(s): Simone Scardapane; Danilo Comminiello; Michele Scarpiniti; Aurelio Uncini
Page(s): 2214 - 2220