CFP: IEEE TFS Special Issue on "Fuzzy Techniques in Financial Modelling and Simulation"

I. AIMS AND SCOPE

Computational intelligence has attracted a significant and increasing interest from the financial engineering and economics communities in recent years.  Computational systems capturing sentiments, preferences, behaviour and beliefs, are becoming indispensable in virtually all financial applications, from portfolio selection to proprietary trading, algorithmic trading, and risk management.  The bar has been raised with the revision of regulations, and the required compliance and risk management.  The new rules should be implemented through new processes and supported by developing new computational tools.

The fuzzy systems domain provides an armoury of techniques to address the challenges currently encountered in the financial engineering area.  Fuzzy logic can be used to effectively describe and incorporate financial experts’ and market participants’ intuition and behaviour, reaching beyond the capabilities of probabilistic models traditionally used in financial modelling.  In addition, fuzzy techniques can be used in conjunction with probabilistic models or with other machine learning techniques, such as evolutionary optimisation methods or neural networks, in order to better address the challenges raised in this area.

The objective of this special issue is to bring together the most recent advances in the design and application of fuzzy approaches to real problems in financial engineering.  A focus of interest is simulating scenarios at different level of granularity, as well as developing test environments for new financial and banking regulation, while accommodating behavioural aspects.

II. TOPICS COVERED

This special issue solicits original contributions on theoretical developments for financial modelling and simulations based on the following paradigms:

• fuzzy time series         
• fuzzy data mining
• fuzzy intelligent          
• fuzzy optimisation
• decision-making           
• fuzzy systems
• fuzzy granular             
• fuzzy-rough approaches
• computing                   
• evolving fuzzy systems
• neuro-fuzzy systems     
• support vector machines

Application papers of these paradigms to the following financial engineering areas are welcome:

• agent-based artificial financial markets
• financial-regulation test environments
• financial sentiment analysis, emotion mining
• financial scenarios modelling and simulation
• algorithmic trading       
• instruments pricing
• financial forecasting     
• risk management
• contagion analysis        
• systemic risk modelling
• portfolio optimization   
• trading strategies
• behavioural finance      
• finance big data analytics

III. IMPORTANT DATES

July 1, 2015: Submission deadline
Oct. 1, 2015: Notification of the first-round review
Nov. 1, 2015: Revised submission due
Dec. 15, 2015: Final notice of acceptance/reject

IV. SUBMISSION GUIDELINES

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 Fuzzy Techniques in Financial Modelling and Simulation” 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.

V. GUEST EDITORS

Ronald Yager
Machine Intelligence Institute
Iona College, USA
ryager@iona.edu

Antoaneta Serguieva
Financial Computing and Analytics Group
University College London, UK
a.serguieva@ucl.ac.uk

Vasile Palade
Faculty of Engineering and Computing
Coventry University, UK
vasile.palade@coventry.ac.uk

Hisao Ishibuchi
Computer Science and Intelligent Systems
Osaka Prefecture University, Japan
hisaoi@cs.osakafu-u.ac.jp

Neural Networks, Volume 62, Pages 1-118, February 2015

Communication and Brain  
Author(s): Yutaka Sakaguchi, Takeshi Aihara, Peter Ford Dominey, Ichiro Tsuda
Pages: 1-2

Mathematical Theory and Model

Mathematical modeling for evolution of heterogeneous modules in the brain  
Author(s): Yutaka Yamaguti, Ichiro Tsuda
Pages: 3-10

Self-organization of a recurrent network under ongoing synaptic plasticity  
Author(s): Takaaki Aoki
Pages: 11-19

Hodge–Kodaira decomposition of evolving neural networks  
Author(s): Keiji Miura, Takaaki Aoki
Pages: 20-24

Memories as bifurcations: Realization by collective dynamics of spiking neurons under stochastic inputs
Author(s): Tomoki Kurikawa, Kunihiko Kaneko
Pages: 25-31

Multistate network model for the pathfinding problem with a self-recovery property
Author(s): Kei-Ichi Ueda, Masaaki Yadome, Yasumasa Nishiura
Pages: 32-38

Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model  
Author(s): Alexander Woodward, Tom Froese, Takashi Ikegami
Pages: 39-46

Physiology, Neuroscience and Model

Phase shifts in alpha-frequency rhythm detected in electroencephalograms influence reaction time  
Author(s): Yasushi Naruse, Ken Takiyama, Masato Okada, Hiroaki Umehara, Yutaka Sakaguchi
Pages: 47-51

Spatial consistency of neural firing regulates long-range local field potential synchronization: A computational study  
Author(s): Naoyuki Sato
Pages: 52-61

Arm-use dependent lateralization of gamma and beta oscillations in primate medial motor areas  
Author(s): Ryosuke Hosaka, Toshi Nakajima, Kazuyuki Aihara, Yoko Yamaguchi, Hajime Mushiake
Pages: 62-66

Spatiotemporal patterns of current source density in the prefrontal cortex of a behaving monkey  
Author(s): Kazuhiro Sakamoto, Norihiko Kawaguchi, Kohei Yagi, Hajime Mushiake
Pages: 67-72

Computational model of visual hallucination in dementia with Lewy bodies  
Author(s): Hiromichi Tsukada, Hiroshi Fujii, Kazuyuki Aihara, Ichiro Tsuda
Pages: 73-82

Behavioral and System model

Immediate return preference emerged from a synaptic learning rule for return maximization  
Author(s): Yoshiya Yamaguchi, Takeshi Aihara, Yutaka Sakai
Pages: 83-90

A wavelet-based method for extracting intermittent discontinuities observed in human motor behavior  
Author(s): Yasuyuki Inoue, Yutaka Sakaguchi
Pages: 91-101

Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation  
Author(s): Sylvain Mahé, Raphaël Braud, Philippe Gaussier, Mathias Quoy, Alexandre Pitti
Pages: 102-111

Communication, concepts and grounding  
Author(s): Frank van der Velde
Pages: 112-117

Call for Papers: IEEE 2015 International Conference on Data Science and Advanced Analytics

Preliminary Call for Papers: IEEE 2015 International Conference on Data Science and Advanced Analytics (DSAA 2015)

19-21 October, 2015, Paris, France

Website: http://dsaa2015.lip6.fr/

Important Dates

Paper Submission deadline: 18 May, 2015
Notification of acceptance: 6 July, 2015
Final Camera-ready papers due: 28 August, 2015

Publications

All accepted papers will be published by IEEE and included 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 publication in the special issues of some international journals, including IEEE Intelligent Systems and WWWJ.

Introduction

Data driven scientific discovery is an important emerging paradigm for computing in areas including social, service, Internet of Things, sensor networks, telecommunications, biology, health-care and cloud. Under this paradigm, Data Science is the core that drives new researches in many areas, from environmental to social. 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, Government or on the Web.

Following the first successful edition held in 2014 in Shanghai, the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2015) 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.

DSAA'2015 will consist of two main Tracks: Research and Application; the Research Track is aimed at collecting contributions related to theoretical foundations of Data Science and Data Analytics. The Application Track is aimed at collecting contributions related to applications of Data Science and Data Analytics in real life scenarios. DSAA solicits then both theoretical and practical works on data science and advanced analytics.

Topics of Interest

General areas of interest to DSAA'2015 include but are not limited to:
1. Foundations

• New mathematical, probabilistic and statistical models and theories
• New machine learning theories, models and systems
• New knowledge discovery theories, models and systems
• Manifold and metric learning, deep learning
• Scalable analysis and learning
• Non-iidness learning
• Heterogeneous data/information integration
• Data pre-processing, sampling and reduction
• High dimensional data, feature selection and feature transformation
• 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
• Intent 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. Storage, retrieval and search

• Data warehouses, cloud architectures
• Large-scale databases
• Information and knowledge retrieval
• Information and knowledge retrieval
• Web/social/databases query and search
• Personalized search and recommendation
• Human-machine interaction and interfaces
• Crowdsourcing and collective intelligence

4. Privacy and security

• 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

5. Applications, practices, tools and evaluation

• Best practices and lessons
• Data-intensive organizations, business and economy
• Domain-specific applications
• Business/government analytics
• Online/social/living/environment data analysis
• Mobile analytics for hand-held devices
• Quality assessment and interestingness metrics
• Complexity, efficiency and scalability
• Anomaly/fraud/exception/change/event/crisis analysis
• Large-scale recommender and search systems
• Big data representation and visualization
• Large scale application case studies

Organizing Committee

Honorary Chair
Usama Fayyad, Barclays Bank, UK

General Chairs
Longbing Cao, University of Technology Sydney, Australia
Eric Gaussier, University Joseph Fourier, France

Conference Chairs
Olivier Capp, Telecom Paristech, CNRS, France
Wei Wang, University of California at Los Angeles, USA

Research Track Chairs
Patrick Gallinari, University Pierre & Marie Curie, France
James Kwok, Hong Kong University of Science and Technology, China

Application Track Chairs
Gabriella Pasi, Universita degli Studi di Milano Biccoca, Italy
Osmar Zaiane, Univ. of Alberta, Canada

Call for Papers: 12th International Symposium on Neural Networks (ISNN2015)

Call for papers: 12th International Symposium on Neural Networks (ISNN2015)， October 15-18, 2015, Jeju, Korea

Sponsors and co-sponsor: The Chinese University of Hong Kong, Pusan National University

Technical co-sponsors: Asia Pacific Neural Network Assembly (pending), IEEE Computational Intelligence Society (pending), International Neural Network Society, and Korean Institute of Intelligent Systems

Website: http://isnn.mae.cuhk.edu.hk

Important Dates

Special session proposals deadline April 15, 2015
Paper submission deadline May 15, 2015
Notification of acceptance June 15, 2015
Camera-ready copy and author registration July 15, 2015

Following the successes of previous events, Twelfth International Symposium on Neural Networks (ISNN 2015) will be held in Jeju, Korea. Jeju's temperate climate, natural scenery, and beaches make it a popular tourist destination for South Koreans as well as visitors from other parts of East Asia. There are numerous popular tourist spots on the island, such as Cheonjeyeon Waterfalls, Mount Halla, Hyeobje Cave, and Hyeongje Island. In particular, Jeju Volcanic Island and Lava Tubes was listed by UNESCO as a World Natural Heritage. ISNN 2015 aims to provide a high-level international forum for scientists, engineers, and educators to present the state of the art of neural network research and applications in related fields. The symposium will feature plenary speeches given by world renowned scholars, regular sessions with broad coverage, and special sessions focusing on popular topics.

Call for Papers and Special Sessions

Prospective authors are invited to contribute high-quality papers to ISNN 2015. In addition, proposals for special sessions within the technical scopes of the symposium are solicited. Special sessions, to be organized by internationally recognized experts, aim to bring together researchers in special focused topics. Papers submitted for special sessions are to be peer-reviewed with the same criteria used for the contributed papers. Researchers interested in organizing special sessions are invited to submit formal proposals to ISNN 2015. A special session proposal should include the session title, a brief description of the scope and motivation, names, contact information and brief biographical information of the organizers.

Topic Areas

Topics areas include, but not limited to, computational neuroscience, connectionist theory and cognitive science, mathematical modeling of neural systems, neurodynamic analysis, neurodynamic optimization and adaptive dynamic programming, embedded neural systems, probabilistic and information-theoretic methods, principal and independent component analysis, hybrid intelligent systems, supervised, unsupervised, and reinforcement learning, deep learning, brain imaging and neural information processing, neuroinformatics and bioinformatics, support vector machines and kernel methods, autonomous mental development, data mining, pattern recognition, time series analysis, image and signal processing, robotic and control applications, telecommunications, transportation systems, intrusion detection and fault diagnosis, hardware implementation, real-world applications.

Paper Submission

Authors are invited to submit full-length papers (10 pages maximum) by the submission deadline through the online submission system. Potential organizers are also invited to enlist five or more papers with cohesive topics to form special sessions. The submission of a paper implies that the paper is original and has not been submitted under review or is not copyright-protected elsewhere and will be presented by an author if accepted. All submitted papers will be refereed by experts in the field based on the criteria of originality, significance, quality, and clarity. The authors of accepted papers will have an opportunity to revise their papers and take consideration of the referees' comments and suggestions. Papers presented at ISNN 2015 will be published in the EI-indexed proceedings in the Springer LNCS series and selected good papers will be included in special issues of several SCI journals.

Organizers:

General Chair
Jun Wang, The Chinese University of Hong Kong, Hong Kong

Steering Chair
Derong Liu, Chinese Academy of Sciences, Beijing, China and University of Illinois - Chicago, USA

Organizing Committee Chairs
Chengan Guo, Dalian University of Technology, Dalian, China
Sungshin Kim, Pusan National University, Busan, Korea
Zhigang Zeng, Huazhong University of Science and Technology, Wuhan, China

Program Chairs
Xiaolin Hu, Tsinghua University, Beijing, China
Yousheng Xia, Fuzhou University, Fuzhou, China
Yunong Zhang, Sun Yet-sen University, Guangzhou, China
Dongbin Zhao, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Special Sessions Chairs
Sanqing Hu, Hangzhou Dianzi University, Hangzhou, China
Kwang Baek Kim, Silla University, Busan, Korea
Tieshan Li, Dalian Maritime University, Dalian, China

Publicity Chairs
Yuanqing Li, South China University of Technology, Guangzhou, China
Yi Shen, Huazhong University of Science and Technology, Wuhan, China
Zhang Yi, Sichuan University, Chengdu, China

Publications Chairs
Jianchao Fan, National Marine Environmental Monitoring Center, Dalian, China
Jin Hu, Chongqing Jiaotong University, Chongqing, China
Zheng Yan, Huawei Shannon Laboratory, Beijing, China

Registration Chairs
Shenshen Gu, Shanghai University, Shanghai, China
Qingshan Liu, Huazhong University of Science and Technology, Wuhan, China

Secretariat
Xinyi Le, The Chinese University of Hong Kong, Hong Kong

Webmaster
Shaofu Yang, The Chinese University of Hong Kong, Hong Kong

CFP: IEEE TNNLS special issue on "New Developments in Neural Network Structures for Signal Processing, Autonomous Decision, and Adaptive Control"

There has been continuously increasing interest in applying neural networks to identification and adaptive control of practical systems that are characterized by nonlinearity, uncertainty, communication constraints, and complexity. The past few years have witnessed a variety of new developments in neural-network-based approaches for behavior learning, information processing, autonomous decision, and system control. Biologically inspired neural network structures can significantly enhance the capabilities of information processing, control and computational performance. New discoveries in neurocognitive psychology, sociology, and elsewhere reveal new neurological learning structures with more powerful capabilities in complex problem solving and fast decision in dynamic environments.  The goal of the special issue is to consolidate recent new developments in neural network structures for signal processing, autonomous decision, and adaptive control with application to complex systems. It welcomes contributions from a wide range of research aspects relevant to the topic, including neural computing, adaptive control, cooperative control, autonomous decision systems, mathematical and computational models, neuropsychology decision and control, algorithms, simulation, applications and/or case studies.

SCOPE OF THE SPECIAL ISSUE

We invite original contributions related to new neural network structures and methods, adaptive neural network control, from theories, algorithms, modelling to experimental studies and applications. Topics include but are not limited to:

• Bio-inspired neural network structures for signal processing
• Cognitive computing and intelligent control
• Fast satisficing decision & control based on risk, gist and environmental cues
• Cooperative control using neural network structures
• Brain-like control design and applications
• New neural network topologies from neurocognitive psychology studies
• Neurocomputing structures for fast decision and control in dynamic environments
• Neural-adaptive learning in distributed multi-agent systems
• Spike timing-based learning algorithms with hierarchical/complex architectures
• Autonomous decision and control using neural structures
• Memory-based reasoning, prediction and control

Important Dates

31 Mar 2015 – Deadline for manuscript submission
31 May 2015 – Notification of authors
31 June 2015 – Deadline for submission of revised manuscripts
31 July 2015 – Final decision of acceptance
Nov. 2015 – Tentative Publication Date

Guest Editors

• Y.D. Song, Chongqing University, China, ydsong@cqu.edu.cn 
• F.L. Lewis, University of Texas at Arlington, USA
• Marios Polycarpou, University of Cyprus
• Danil Prokhorov, Toyota Research Institute North America, Ann Arbor, MI.
• Dongbin Zhao, Institute of Automation, Chinese Academy of Sciences, Beijing

Submission Instructions

1. Read the information for Authors at http://cis.ieee.org/tnnls
2. Submit your manuscript by 31 March 2015 at the TNNLS webpage (http://mc.manuscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manuscript and in the cover letter that the manuscript has been submitted to the special issue on New Developments in Neural Network Structures for Signal Processing, Autonomous Decision, and Adaptive Control . Send also an email to guest editor Y.D. Song with subject “TNNLS special issue submission” to notify about your submission.

Call For Papers: IEEE CIM special issue on "Computational Intelligence for Changing Environments"

IEEE Computational Intelligence Magazine Special Issue on Computational Intelligence for Changing Environments
Amir Hussain, Dacheng Tao, Jonathan Wu and Dongbin Zhao

Aims and Scope:

Over the past decade or so, computational intelligence techniques have been highly successful for solving big data challenges in changing environments. In particular, there has been growing interest in so called biologically inspired learning (BIL), which refers to a wide range of learning techniques, motivated by biology, that try to mimic specific biological functions or behaviors. Examples include the hierarchy of the brain neocortex and neural circuits, which have resulted in biologically-inspired features for encoding, deep neural networks for classification, and spiking neural networks for general modelling.

To ensure that these models are generalizable to unseen data, it is common to assume that the training and test data are independently sampled from an identical distribution, known as the sample i.i.d. assumption. In dynamic and non-stationary environments, the distribution of data changes over time, resulting in the phenomenon of ‘concept drift’ (also known as population drift or concept shift), which is a generalization of covariance shift in statistics. Over the last five years, transfer learning and multitask learning have been used to tackle this problem. Fundamental analyses using probably approximately correct (PAC) and Rademacher complexity frameworks have explained why appropriate incorporation of context and concept drift can improve generalizability in changing environments.

It is possible to use human-level processing power to tackle concept drift in changing enviroments. Concept drift is a real-world problem, usually associated with online and concept learning, where the relationships between input data and target variables dynamically change over time. Traditional learning schemes do not adequately address this issue, either because they are offline or because they avoid dynamic learning. However, BIL seems to possess properties that would be helpful for solving concept drift problems in changing environments. Intuitively, the human capacity to deal with concept drift is innate to cognitive processes, and the learning problems susceptible to concept drift seem to share some of the dynamic demands placed on plastic neural areas in the brain. Using improved biological models in neural networks can provide insight into cognitive computational phenomena.

However, a main outstanding issue in using computational intelligence for changing enviroments and domain adaptation is how to build complex networks, or how networks should be connected to the features, samples, and distribution drifts. Manual design and building of these networks are beyond current human capabilities. Recently, computational intelligence methods has been used to address concept drift in changing enviroments, with promising results. A Hebbian learning model has been used to handle random, as well as correlated, concept drift. Neural networks have been used for concept drift detection, and the influence of latent variables on concept drift in a neural network has been studied. In another study, a timing-dependent synapse model has been applied to concept drift. These works mainly apply biologically-plausible computational models to concept drift problems. Although these results are still in their infancy, they open up new possibilities to achieve brain-like intelligence for solving concept drift problems in changing environments.

Taking the current state of research in computational intelligence for changing environments into account, the objective of this special issue is to collate this research to help unify the concepts and terminology of computational intelligence in changing environments, and to survey state-of-the-art computational intelligence methodologies and the key techniques investigated to date. Therefore, this special issue invites submissions on the most recent developments in computational intelligence for changing enviroments algorithms and architectures, theoretical foundations, and representations, and their application to real-world problems. We also welcome timely surveys and review papers.

Topics of Interest include (but are not limited to):

• Computational intelligence methodologies and implementation for changing environments
• Transfer learning
• Multitask learning
• Domain adaption
• Incremental Learning architectures
• Incremental Unsupervised and semi-supervised learning architectures
• Incremental Incremental Representation learning and disentangling
• Incremental Knowledge augmentation
• Incremental Adaptive Neuro-fuzzy systems
• Incremental and single-pass data mining
• Incremental Neural Clustering
• Incremental Neural regression
• Incremental Adaptive decision systems
• Incremental Feature selection and reduction
• Incremental Constructive Learning
• Novelty detection in Incremental learning

Submission Process

The maximum length for the manuscript is typically 25 pages in single column format with double-spacing, including figures and references. Authors should specify in the first page of their manuscripts the corresponding author’s contact and up to 5 keywords. Submission should be made via:

https://www.easychair.org/conferences/?conf=ieeecimcdbil2015.

Important Dates

1st Feb, 2015: Submission of Manuscripts
15th April, 2015: Notification of Review Results
15th May, 2015: Submission of Revised Manuscripts
15th June, 2015: Submission of Final Manuscripts
November 2015: Publication

Guest Editor

Professor Amir Hussain
University of Stirling
Stirling FK9 4LA SCOTLAND, UK
Email: ahu@cs.stir.ac.uk

Professor Dacheng Tao
University of Technology, Sydney
235 Jones Street, Ultimo, NSW 2007, Australia
Email: dacheng.tao@uts.edu.au

Professor Jonathan Wu
University of Windsor
401 Sunset Avenue, Windsor, ON, Canada
Email: jwu@uwindsor.ca

Professor Dongbin Zhao
Institute of Automation, Chinese Academy of Sciences,
No. 95, Zhongguancun East Road, Beijing 100190, China
E-mail: dongbin.zhao@gmail.com

IEEE Transactions on Computational Intelligence and Artificial Intelligence in Games: Volume 6, Number 4, December 2014

1. Guest Editorial: General Games
Author(s): Browne, C. ; Togelius, J. ; Sturtevant, N.
Page(s): 317 - 319

2. The Game Description Language Is Turing Complete
Author(s): Saffidine, A.
Page(s): 320 - 324
A
3. An Extensible Description Language for Video Games
Author(s): Schaul, T.
Page(s): 325 - 331

4. The Axiom General Purpose Game Playing System
Author(s): Schmidt, G.
Page(s): 332 - 342

5. Efficiency of GDL Reasoners
Author(s): Schiffel, S. ; Bjornsson, Y.
Page(s): 343 - 354

6. A Neuroevolution Approach to General Atari Game Playing
Author(s): Hausknecht, M. ; Lehman, J. ; Miikkulainen, R. ; Stone, P.
Page(s): 355 - 366

7. Self-Adaptation of Playing Strategies in General Game Playing
Author(s): Swiechowski, M. ; Mandziuk, J.
Page(s): 367 - 381

8. EvoMCTS: A Scalable Approach for General Game Learning
Author(s): Benbassat, A. ; Sipper, M.
Page(s): 382 - 394

9. Decaying Simulation Strategies
Author(s): Tak, M.J.W. ; Winands, M.H.M. ; Bjornsson, Y.
Page(s): 395 - 406

10. 2015 IEEE conference on computational intelligence and games
Page(s): 407

IEEE Transactions on Autonomous Mental Development: Volume 6, Number 4, December 2014

1. Editorial: Renewal for the IEEE Transactions on Autonomous Mental Development
Author(s): Z. Zhang
Pages: 241-242

2. The Fourth IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob)2014: Conference Summary and Report
Author(s): G. Metta, L. Natale, and M. Lee
Pages: 243
a
3. Learning from Demonstration in Robots using the Shared Circuits Model
Author(s): K. M. U. Suleman and M. M. Awais
Pages: 244-258

4. A Hierarchical System for a Distributed Representation of the Peripersonal Space of a Humanoid Robot
Author(s): M. Antonelli, A. Gibaldi, F. Beuth, A. J. Duran, A. Canessa, M. Chessa, F. Solari, A. P. del Pobil, F. Hamker, E. Chinellato, and S. P. Sabatini
Pages: 259-273

5. A Wearable Camera Detects Gaze Peculiarities during Social Interactions in Young Children with PervasiveDevelopmental Disorders
Author(s): S. Magrelli, B. Noris, P. Jermann, F. Ansermet, F. Hentsch, J. Nadel, and A. G. Billard
Pages: 274-285

6. Optimal Rewards for Cooperative Agents
Author(s): B. Liu, S. Singh, R. L. Lewis, and S. Qin
Pages: 286-297

IEEE Transactions on Neural Networks and Learning Systems: Volume 26, Issue 1, January 2015

1. Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part I)
Author(s): Xia Liu; Shaobo Lin; Jian Fang; Zongben Xu
Page(s): 7 - 20

2. Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II)
Author(s): Shaobo Lin; Xia Liu; Jian Fang; Zongben Xu
Page(s): 21 - 34

3. Feature Selection Using a Neural Framework With Controlled Redundancy
Author(s): Rudrasis Chakraborty; Nikhil R. Pal
Page(s): 35 - 50

4. Pareto-Path Multitask Multiple Kernel Learning
Author(s): Cong Li; Michael Georgiopoulos; Georgios C. Anagnostopoulos
Page(s): 51 - 61

5. Learning Understandable Neural Networks With Nonnegative Weight Constraints
Author(s): Jan Chorowski; Jacek M. Zurada
Page(s): 62 - 69

6. A Latent Manifold Markovian Dynamics Gaussian Process
Author(s): Sotirios P. Chatzis; Dimitrios Kosmopoulos
Page(s): 70 - 83

7. Existence and Uniform Stability Analysis of Fractional-Order Complex-Valued Neural Networks With Time Delays
Author(s): R. Rakkiyappan; Jinde Cao; G. Velmurugan
Page(s): 84 - 97

8. Identification of the Dynamic Operating Envelope of HCCI Engines Using Class Imbalance Learning
Author(s): Vijay Manikandan Janakiraman; XuanLong Nguyen; Jeff Sterniak; Dennis Assanis
Page(s): 98 - 112

9. Synchronization of Nonlinear Coupled Networks via Aperiodically Intermittent Pinning Control
Author(s): Xiwei Liu; Tianping Chen
Page(s): 113 - 126

10. Digital Implementation of a Biological Astrocyte Model and Its Application
Author(s): Hamid Soleimani; Mohammad Bavandpour; Arash Ahmadi; Derek Abbott
Page(s): 127 - 139

11. Actor–Critic-Based Optimal Tracking for Partially Unknown Nonlinear Discrete-Time Systems
Author(s): Bahare Kiumarsi; Frank L. Lewis
Page(s): 140 - 151

12. Large-Scale Nyström Kernel Matrix Approximation Using Randomized SVD
Author(s): Mu Li; Wei Bi; James T. Kwok; Bao-Liang Lu
Page(s): 152 - 164

13. Reinforcement Learning Design-Based Adaptive Tracking Control With Less Learning Parameters for Nonlinear Discrete-Time MIMO Systems
Author(s): Yan-Jun Liu; Li Tang; Shaocheng Tong; C. L. Philip Chen; Dong-Juan Li
Page(s): 165 - 176

14. Nonsmooth ICA Contrast Minimization Using a Riemannian Nelder–Mead Method
Author(s): Suviseshamuthu Easter Selvan
Page(s): 177 - 183

Reminder: conference paper deadline FUZZ-IEEE 2015

A reminder that the deadline for submitting papers to the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2015 is February 8, 2015. This conference will be held in Istanbul, Turkey, August 2-5, 2015.

Reminder: conference paper deadline for IEEE CIG 2015

A reminder that the paper submission deadline for the 2015 IEEE Conference on Computational Intelligence in Games (IEEE CIG) is April 2, 2015. This conference will be held in Tainan, Taiwan, 31 August to 2 September, 2015.

Reminder: paper deadline for INNS Big Data 2015

A reminder that the deadline for submitting papers to the INNS Conference on Big Data is March 22, 2015. This conference will be held in San Francisco, USA, 8-10 August, 2015.

Database of Computational Intelligence Courses

One of the ways I contribute to the IEEE Computational Intelligence Society is by serving on the University Curricula Subcommittee of the Education Committee. Among the activities of the curricula subcommittee is maintaining a world-wide directory of courses in computational intelligence.

This directory is now available as a searchable online database, at http://ucs.ais.ac.nz/

This is a prototype of a system where educators can submit details of their courses, search existing courses, and engage in discussions with other educators about the listed courses. Anyone may access and search the database, although users must register to submit new courses or leave comments.

This system was developed as a student project by the following students:

• Pankaj Dadral
• Toshitsugu Hashimoto
• Shuxiang Wang
• Dan Mo

The project was supervised by myself and is kindly hosted by Auckland Institute of Studies, where I'm the head of the Information Technology Programme.