IEEE TETCI Special Issue on New Advances in Deep-Transfer Learning (Jun 30)

I. AIM AND SCOPE

While Deep learning (DL) has achieved great success in big data applications, transfer learning (TL) is an important paradigm for small/insufficient data applications, which utilizes the data/knowledge in one task to facilitate the learning in another relevant task. How to integrate DL and TL to combine their advantages is an interesting and important research topic. Deep-Transfer Learning (DTL) is proposed to address this issue. Deep learning extracts knowledge from big data, which can then be used by TL for a new task/domain with small/insufficient data.

Computational intelligence techniques, mainly including neural networks, fuzzy logic, and evolutionary computation, can be valuable in DTL. For example:

• Neural networks (NN) are the cornerstones of DL. 
• Hierarchical/cascaded fuzzy logic systems (FLS) and fuzzy NNs may be viewed as fuzzy rule based DL models. FLSs can also capture interpretable knowledge, which may be easily transferrable to a new domain/task. Therefore, fuzzy logic is expected to play an important role in integrating DL and TL. 
• Evolutionary computation (EC) has been widely used in optimizing shallow NNs and FLSs. TL can also be viewed as an evolutionary learning strategy because it adapts the model to the changing environment. It is interesting to see novel applications of EC in DTL. 
• Other emerging forms of CI, such as (but not limited to) probabilistic computation, swarm intelligence, and artificial immune systems, can also contribute to DTL from different aspects. The aims of this special issue are: (1) present the state-of-theart research on novel CI based DTL methods and their applications, and (2) provide a forum for researchers to disseminate their views on future perspectives of the field.

II. TOPICS 

Topics of interest for this special issue include, but are not limited to: 

Theory and Methods: 

• DTL theory and algorithms 
• Fuzzy logic and fuzzy set based DTL 
• Neural networks based DTL 
• Evolutionary computation for DTL 
• Novel/emerging forms of CI (in addition to NN/FLS/EC) in DTL 
• Uncertainty theory based DTL 
• DTL for feature learning, classification, regression, and clustering 
• DTL for multi-task modeling, multi-view modeling and co-learning 

Applications: 

• CI based DTL for video analysis, text processing and natural language processing 
• CI based DTL for brain-machine interfaces and medical signal analysis

III. SUBMISSIONS 

Manuscripts should be prepared according to the “Information for Authors” section of the journal (http://cis.ieee.org/ieee-transactions-on-emerging-topics-incomputational-intelligence.html) and submissions should be done through the journal submission website: https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of “New Advances in Deep-Transfer Learning” and clearly marking “New Advances in Deep Transfer Learning Special Issue Paper” as comments to the Editor-in-Chief. Submitted papers will be reviewed by at least three different expert reviewers. Submission of a manuscript implies that it is the authors’ original unpublished work and is not being submitted for possible publication elsewhere.

IV. IMPORTANT DATES 

Paper submission deadline: June 31, 2018 

Notice of the first round review results: September 15, 2018 

Revision due: November 15, 2018 

Final notice of acceptance/reject: December 15, 2018 

V. GUEST EDITORS 

Zhaohong Deng, Jiangnan University, China; dengzhaohong@jiangnan.edu.cn 

Jie Lu, University of Technology Sydney, Australia; jie.lu@uts.edu.au 

Dongrui Wu, Huazhong University of Science and Technology, China; drwu@hust.edu.cn 

Kup-Sze Choi, Hong Kong Polytechnic University, Hong Kong, China; kschoi@ieee.org Shiliang Sun, East China Normal University, China; slsun@cs.ecnu.edu.cn 

Yusuke Nojima, Osaka Prefecture University, Japan; nojima@cs.osakafu-u.ac.jp

CFP: IEEE TETCI Special Issue on Computational Intelligence for Smart Energy Applications to Smart Cities (May 15)

I. AIM AND SCOPE

By 2050, more than half the world’s population is expected to live in urban regions. This rapid expansion of population in the cities of the future will lead to increasing demands on various infrastructures; the urban economics will play a major role in national economics. Cities must be competitive by providing smart functions to support high quality of life. There is thus an urgent need to develop smart cities that possess a number of smart components. Among them, smart energy is arguably the first infrastructure to be established because almost all systems require energy to operate.

Smart energy refers to energy monitoring, prediction, use or management in a smart way. In smart cities, smart energy applications include smart grids, smart mobility, and smart communications. While realizing smart energy is promising to smart cities, it involves a number of challenges.

 By using smart grid technologies, distributed power supply is replacing conventional centralized schemes, leading to regional aggregation of energy that must consider the interests of many grid participants. With the increasing penetration of electric vehicles (EVs), EV charging stations must consider many parameters and objectives to optimize the charging schedule. To make transportation or communications infrastructures go green, renewable energy sources (RESs) are often integrated into the whole system as part of power supply; a robust prediction for both power load and energy production becomes necessary for later energy management in response to intermittent power supply from RESs.

Because of the uncertainty of environments, complexity of the problem of interest, or multiplicity of objectives that must be achieved, conventional optimization methods using deterministic search algorithms cannot well address these challenges. By contrast, stochastic optimization can be useful for handling uncertainty; adaptive learning based on, for example, human behaviors, available resources, network capacity, or collected data can be a solution to complex problems; evolutionary computation can be applied to solve problems with many objectives. Computational Intelligence (CI) thus serves as a useful tool for addressing aforementioned difficulties.

II. TOPICS

This Special Issue aims to provide in-depth CI technologies that enable smart energy applications to smart cities. Topics of interest include, but are not limited to:

• Evolutionary computation for smart grids in consideration of many objectives, including energy management system, demand-side management, demand response, advanced metering infrastructure, and behind-the-meter applications.  
• Stochastic optimization for smart mobility in consideration of system uncertainty, with a primary focus on power scheduling for Internet of EVs or green public transportation.  
• Intelligent algorithms for smart communications pertaining to Internet of Things, machine-to-machine communications, vehicle-to-grid communications, and vehicle-to-infrastructure communications under the framework of green communications.  
• Machine/deep learning for renewable energy forecasting or power load forecasting.  
• Survey papers on CI for smart energy applications. 

III. IMPORTANT DATES  

Submission deadline: May 15, 2018.  

Notification due date: October 1, 2018.  

Final version due date: November 1, 2018. 

IV. SUBMISSION GUIDELINES 

Manuscripts should be prepared according to the “Information for Authors” section of the journal (http://cis.ieee.org/ieee-transactions-on-emerging-topics-in-computational-intelligence.html) and submissions should be done through the journal submission website: https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of “Computational Intelligence for Smart Energy Applications to Smart Cities” and clearly marking “Special Issue on Computational Intelligence for Smart Energy Applications to Smart Cities” as comments to the Editor-in-Chief. 

V. GUEST EDITORS  

Wei-Yu Chiu, National Tsing Hua University, Taiwan wychiu@ee.nthu.edu.tw  

Hongjian Sun, Durham University, UK  

Chao Wang, Tongji University, China  

Athanasios V. Vasilakos, Lulea University of Technology, Sweden

CFP: IEEE TEVC Special Issue on Theoretical Foundations of Evolutionary Computation

I. AIM AND SCOPE

Evolutionary computation (EC) methods such as
evolutionary algorithms, ant colony optimization and artificial
immune systems have been successfully applied to a wide
range of problems. These include classical combinatorial
optimization problems and a variety of continuous, discrete
and mixed integer real-world optimization problems that are
often hard to optimize by traditional methods (e.g., because
they are non-linear, highly constrained, multi-objective, etc.).
In contrast to the successful applications, there is still a need
to understand the behaviour of these algorithms. The
achievement and development of a solid theory of bio-inspired
computation techniques is important as it provides sound
knowledge on their working principles. In particular, it
explains the success or the failure of these methods in
practical applications. Theoretical analyses lead to the
understanding of which problems are optimized (or
approximated) efficiently by a given algorithm and which ones
are not. The benefits of theoretical understanding for
practitioners are threefold. 1) Aiding algorithm design, 2)
guiding the choice of the best algorithm for the problem at
hand and 3) determining optimal parameter settings.

The aim of this special issue is to advance the theoretical
understanding of evolutionary computation methods. We
solicit novel, high quality scientific contributions on
theoretical or foundational aspects of evolutionary
computation. A successful exchange between theory and
practice in evolutionary computation is very desirable and
papers bridging theory and practice are of particular interest.
In addition to strict mathematical investigations, experimental
studies strengthening the theoretical foundations of
evolutionary computation methods are very welcome.


II. THEMES

This special issue will present novel results from different subareas
of the theory of bio-inspired algorithms. The scope of
this special issue includes (but is not limited to) the following
topics:

• Exact and approximation runtime analysis
• Black box complexity
• Self-adaptation
• Population dynamics
• Fitness landscape and problem difficulty analysis
• No free lunch theorems
• Theoretical Foundations of combining traditional optimization techniques with EC methods
• Statistical approaches for understanding the behaviour of bio-inspired heuristics
• Computational studies of a foundational nature

All classes of bio-inspired optimization algorithms will be
considered including (but not limited to) evolutionary
algorithms, ant colony optimization, artificial immune
systems, particle swarm optimization, differential evolution,
and estimation of distribution algorithms. All problem
domains will be considered including discrete and continuous
optimization, single-objective and multi-objective
optimization, constraint handling, dynamic and stochastic
optimization, co-evolution and evolutionary learning.

III. SUBMISSION

Manuscripts should be prepared according to the “Information
for Authors” section of the journal found at
http://ieee-cis.org/pubs/tec/authors/
and submissions should be made through the
journal submission website: http://mc.manuscriptcentral.com/tevc-ieee/,
by selecting the Manuscript Type of “TFoEC
Special Issue Papers” and clearly adding “TFoEC Special
Issue Paper” to the comments to the Editor-in-Chief.

Submitted papers will be reviewed by at least three different
expert reviewers. Submission of a manuscript implies that it is
the authors’ original unpublished work and is not being
submitted for possible publication elsewhere.

Each submission will contain at least one paragraph
explaining why the paper is (potentially) relevant to practice.

IV. IMPORTANT DATES

Submission open: February 1, 2018
Submission deadline: October 1, 2018
Tentative publication date: 2019

Papers will be assigned to reviewers as soon as they are
submitted. Papers will be published online as soon as they are
accepted.

For further information, please contact one of the following
Guest Editors.

V. GUEST EDITORS

Pietro S. Oliveto
Department of Computer Science
University of Sheffield
United Kingdom
p.oliveto@sheffield.ac.uk

Anne Auger
INRIA
Ecole Polytechnique Paris
France
anne.auger@jnria.fr

Francisco Chicano
Department of Languages and Computing Sciences
University of Malaga
Spain
chicano@lcc.uma.es

Carlos M. Fonseca
Department of Informatics Engineering
University of Coimbra
Portugal
cmfonsec@dei.uc.pt

CFP: IEEE CIM Special Issue on Deep Reinforcement Learning and Games (Oct 1)

Aims and Scope

Recently, there has been tremendous progress in artificial intelligence (AI) and computational intelligence (CI) and games. In 2015, Google DeepMind published a paper “Human-level control through deep reinforcement learning” in Nature, showing the power of AI&CI in learning to play Atari video games directly from the screen capture. Furthermore, in Nature 2016, it published a cover paper “Mastering the game of Go with deep neural networks and tree search” and proposed the computer Go program, AlphaGo. In March 2016, AlphaGo beat the world’s top Go player Lee Sedol by 4:1. In early 2017, the Master, a variant of AlphaGo, won 60 matches against top Go players. In late 2017, AlphaGo Zero learned only from self-play and was able to beat the original AlphaGo without any losses (Nature 2017). This becomes a new milestone in the AI&CI history, the core of which is the algorithm of deep reinforcement learning (DRL). Moreover, the achievements on DRL and games are manifest. In 2017, the AIs beat the expert in Texas Hold’em poker (Science 2017). OpenAI developed an AI to outperform the champion in the 1V1 Dota 2 game. Facebook released a huge database of StarCraft I. Blizzard and DeepMind turned StarCraft II into an AI research lab with a more open interface. In these games, DRL also plays an important role.

Needless to say, the great achievements of DRL are first obtained in the domain of games, and it is timely to report major advances in a special issue of IEEE Computational Intelligence Magazine. IEEE Trans. on Neural network and Learning Systems and IEEE Trans. on Computational Intelligence and AI in Games have organized similar ones in 2017.

DRL is able to output control signals directly based on input images, and integrates the capacity for perception of deep learning (DL) and the decision making of reinforcement learning (RL). This mechanism has many similarities to human modes of thinking. However, there is much work left to do. The theoretical analysis of DRL, e. g., the convergence, stability, and optimality, is still in early days. Learning efficiency needs to be improved by proposing new algorithms or combining with other methods. DRL algorithms still need to be demonstrated in more diverse practical settings. Therefore, the aim of this special issue is to publish the most advanced research and state-of-the-art contributions in the field of DRL and its application in games. We expect this special issue to provide a platform for international researchers to exchange ideas and to present their latest research in relevant topics. Specific topics of interest include but are not limited to:

·       Survey on DRL and games;

·       New AI&CI algorithms in games;

·       Learning forward models from experience;

·       New algorithms of DL, RL and DRL;

·       Theoretical foundation of DL, RL and DRL;

·       DRL combined with search algorithms or other learning methods;

·       Challenges of AI&CI games as limitations in strategy learning, etc.;

·       DRL or AI&CI Games based applications in realistic and complicated systems.

Important Dates

Submission Deadline: October 1st, 2018

Notification of Review Results: December 10th, 2018

Submission of Revised Manuscripts: January 31st, 2019

Submission of Final Manuscript: March 15th, 2019

Special Issue Publication: August 2019 Issue

Guest Editors

D. Zhao, Institute of Automation, Chinese Academy of Sciences, China, Dongbin.zhao@ia.ac.cn

Dr. Zhao is a professor at Institute of Automation, Chinese Academy of Sciences and also a professor with the University of Chinese Academy of Sciences, China. His current research interests are in the area of deep reinforcement learning, computational intelligence, adaptive dynamic programming, games, and robotics. Dr. Zhao is the Associate Editor of IEEE Transactions on Neural Networks and Learning Systems and IEEE Computation Intelligence Magazine, etc. He is the Chair of Beijing Chapter, and the past Chair of Adaptive Dynamic Programming and Reinforcement Learning Technical Committee of IEEE Computational Intelligence Society (CIS). He works as several guest editors of renowned international journals, including the leading guest editor of the IEEE Trans.on Neural Network and Learning Systems special issue on Deep Reinforcement Learning and Adaptive Dyanmic Programming.

S. Lucas, Queen Mary University of London, UK, simon.lucas@qmul.ac.uk

Dr. Lucas was a full professor of computer science, in the School of Computer Science and Electronic Engineering at the University of Essex until July 31, 2017, and now is the Professor and Head of School of Electronic Engineering and Computer Science at Queen Mary University of London. He was the Founding Editor-in-Chief of the IEEE Transactions on Computational Intelligence and AI in Games, and also co-founded the IEEE Conference on Computational Intelligence and Games, first held at the University of Essex in 2005.  He is the Vice President for Education of the IEEE Computational Intelligence Society. His research has gravitated toward Game AI: games provide an ideal arena for AI research, and also make an excellent application area.

J. Togelius, New York University, USA, julian.togelius@nyu.edu.

Julian Togelius is an Associate Professor in the Department of Computer Science and Engineering, New York University, USA. He works on all aspects of computational intelligence and games and on selected topics in evolutionary computation and evolutionary reinforcement learning. His current main research directions involve search-based procedural content generation in games, general video game playing, player modeling, and fair and relevant benchmarking of AI through game-based competitions. He is the Editor-in-Chief of IEEE Transactions on Computational Intelligence and AI in Games, and a past chair of the IEEE CIS Technical Committee on Games.

Submission Instructions

1.     The IEEE CIM requires all prospective authors to submit their manuscripts in electronic format, as a PDF file. The maximum length for Papers is typically 20 double-spaced typed pages with 12-point font, including figures and references. Submitted manuscript must be typewritten in English in single column format. Authors of Papers should specify on the first page of their submitted manuscript up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website. Submission will be made via https://easychair.org/conferences/?conf=ieeecimcitbb2018.

2.     Send also an email to guest editor D. Zhao (dongbin.zhao@ia.ac.cn) with subject “IEEE CIM special issue submission” to notify about your submission.

3.      Early submissions are welcome. We will start the review process as soon as we receive your contribution.

CFP: IEEE CIM Special Issue on Computational Intelligence for Affective Computing and Sentiment Analysis (Mar 31)

http://sentic.net/ciacsa/

CIACSA @ IEEE CIM

Submissions are invited for a special issue of IEEE Computational Intelligence Magazine (IEEE CIM) on Computational Intelligence for Affective Computing and Sentiment Analysis.

RATIONALE
Emotions are intrinsically part of our mental activity and play a key role in communication and decision-making processes. Emotion is a chain of events made up of feedback loops. Feelings and behavior can affect cognition, just as cognition can influence feeling. Emotion, cognition, and action interact in feedback loops and emotion can be viewed in a structural model tied to adaptation. Besides being important for the advancement of AI, detecting and interpreting emotional information is key in multiple areas of computer science, e.g., human- agent, -computer, and -robot interaction, but also e-learning, e-health, domotics, automotive, security, user profiling and personalization.

In recent years, emotion and sentiment analysis has become increasingly popular also for processing social media data on social networks, online communities, blogs, Wikis, microblogging platforms, and other online collaborative media. The distillation of knowledge from such a big amount of unstructured information, however, is an extremely difficult task, as the contents of today's Web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction.

Most of existing approaches to affective computing and sentiment analysis are still based on the syntactic representation of text, a method that relies mainly on word co-occurrence frequencies. Such algorithms are limited by the fact that they can only process information they can 'see'. As human text processors, we do not have such limitations as every word we see activates a cascade of semantically related concepts, relevant episodes, emotions, and sensory experiences, all of which enable the completion of complex NLP tasks — such as word-sense disambiguation, textual entailment, and semantic role labeling — in a quick and effortless way. Computational intelligence can aid to mimic the way humans process and analyze text and, hence, overcome the limitations of standard approaches to affective computing and sentiment analysis.

TOPICS
Articles are thus invited in areas such as machine learning, active learning, transfer learning, deep neural networks, neural and cognitive models, fuzzy logic, evolutionary computation, natural language processing, commonsense reasoning, and big data computing. Topics include, but are not limited to:
• Context-dependent sentiment analysis
• Deep learning for personality detection
• Deep learning for sarcasm detection
• Tensor fusion networks for sentiment analysis
• Multi-level attention networks for sentiment analysis
• Affective commonsense reasoning
• Statistical learning theory for big social data analysis
• Concept-level sentiment analysis
• Social network modeling and analysis
• Multilingual emotion and sentiment analysis
• Multimodal emotion recognition and sentiment analysis
• Aspect extraction for opinion mining
• Sentic computing
• Conceptual primitives for sentiment analysis
• Affective human-agent, -computer, and -robot interaction
• User profiling and personalization
• Time-evolving sentiment tracking

TIMEFRAME
Submission Deadline: March 31st, 2018
Notification of Review Results: June 15th, 2018
Submission of Revised Manuscripts: July 15th, 2018
Submission of Final Manuscript: September 15th, 2018
Special Issue Publication: Mid-January 2019 (February 2019 Issue)

SUBMISSION AND PROCEEDINGS
The Special Issue will consist of 3 or 4 papers on novel computational intelligence techniques for mining and analyzing emotions and opinions in text, but also in other modalities. Some papers may survey various aspects of the topic. The balance between these will be adjusted to maximize the issue's impact. All articles are expected to successfully negotiate the standard review procedures for IEEE CIM and shall be submitted via EasyChair.

ORGANIZERS
• Erik Cambria, Nanyang Technological University (Singapore)
• Soujanya Poria, Nanyang Technological University (Singapore)
• Amir Hussain, University of Stirling (UK)
• Bing Liu, University of Illinois at Chicago (USA)

5 Minutes with Prof. Bernadette Bouchon-Meunier

IEEE CIS Student Activities Subcommittee invites you to get to know the pioneers and experts in the Computational Intelligence. This month "5 minutes with..." focuses on pioneer Prof. Bernadette Bouchon-Meunier. What is your title, full name, and place of work? Dr. Bernadette Bouchon-Meunier, Director of Research Emeritus at the CNRS (National Center for Scientific Research) - Sorbonne University, Paris, France What grade of member in CIS are you? Life Fellow How long have you been a member of CIS? I have been an IEEE member for 36 years and a CIS member since its inception. One reason why you are a member of CIS: I have been working on fuzzy and intelligent systems for more than 40 years and CIS is the best place to exchange information and meet specialists on these topics. What was your service pathway in the Computational Intelligence Society? I have been a member of the CIS Fuzzy Systems Technical Committee since 2003, chairing it in 2011-2012, I was elected a member of the CIS Adcom several times between 2004 and July 2014. I chaired the Women in Computational Intelligence Committee from 2004 to 2007 and I am still a member of this committee, happy to support women in our field. I chaired the IEEE France Section CIS chapter from 2007 to 2015. I was elected CIS Vice-President for Conferences in July 2014 and re-elected since then. Can you share with us one success story that will motivate young members and provide useful guidelines for their careers? My first research field was Information theory. In 1975, I worked on a cooperative project with a group of sociologists who were looking for a formal model for the questionnaires they used. I discovered Lotfi Zadeh’s seminal paper on fuzzy sets by chance, in the library of mathematics of the university. I was surprised to see that it matched my needs. It was the starting point of years of research based on fuzzy systems and also the first of a long list of multidisciplinary collaborations. Of course, documentary research is much easier now that we have all papers on line but it is also difficult because of the huge number of available documents. My recommendation would be not to have blinders on, to be curious, to collaborate with others. Mutidisciplinarity is enriching. Research out of your main stream may be fruitful. However, keep your own path and don’t get lost in the numerical world.

IEEE Transactions on Neural Networks and Learning Systems; Volume 29, Issue 2, February 2018.

IEEE Transactions on Neural Networks and Learning Systems; Volume 29, Issue 2, February 2018.
Posted by: Haibo He (ieeetnnls@gmail.com)
Date submitted: February 1, 2018.

The following articles appeared in the latest issue of IEEE Transactions on Neural Networks and Learning Systems: Volume 29, Issue 2, February 2018.

This issue published papers on deep learning, multi-view learning, adaptive control, transfer learning, clustering, recurrent neural network, among others. We welcome your submissions to IEEE TNNLS.

These articles can be retrieved on IEEE Xplore:
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5962385
or directly by clicking the individual paper URL below.

IEEE Transactions on Neural Networks and Learning Systems;
Volume 29, Issue 2, February 2018.

1. Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks
Author(s): Son N. Tran; Artur S. d’Avila Garcez
Page(s): 246 - 258
http://ieeexplore.ieee.org/document/7738566/

2. An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition
Author(s): Li Niu; Wen Li; Dong Xu; Jianfei Cai
Page(s): 259 - 272
http://ieeexplore.ieee.org/document/7733141/

3. Hysteretic Noisy Chaotic Neural Networks for Resource Allocation in OFDMA System
Author(s): Ming Sun; Kwang Y. Lee; Yaoqun Xu; Wenlei Bai
Page(s): 273 - 285
http://ieeexplore.ieee.org/document/7733106/

4. Neuroadaptive Fault-Tolerant Control of Nonlinear Systems Under Output Constraints and Actuation Faults
Author(s): Kai Zhao; Yongduan Song; Zhixi Shen
Page(s): 286 - 298
http://ieeexplore.ieee.org/document/7738580/

5. State Estimation for Delayed Genetic Regulatory Networks With Reaction–Diffusion Terms
Author(s): Xian Zhang; Yuanyuan Han; Ligang Wu; Yantao Wang
Page(s): 299 - 309
http://ieeexplore.ieee.org/document/7742956/

6. Incomplete Multisource Transfer Learning
Author(s): Zhengming Ding; Ming Shao; Yun Fu
Page(s): 310 - 323
http://ieeexplore.ieee.org/document/7742923/

7. A pdf-Free Change Detection Test Based on Density Difference Estimation
Author(s): Li Bu; Cesare Alippi; Dongbin Zhao
Page(s): 324 - 334
http://ieeexplore.ieee.org/document/7745962/

8. Multisynchronization of Coupled Heterogeneous Genetic Oscillator Networks via Partial Impulsive Control
Author(s): Ding-Xin He; Guang Ling; Zhi-Hong Guan; Bin Hu; Rui-Quan Liao
Page(s): 335 - 342
http://ieeexplore.ieee.org/document/7748450/

9. Concept Factorization With Adaptive Neighbors for Document Clustering
Author(s): Xiaobing Pei; Chuanbo Chen; Weihua Gong
Page(s): 343 - 352
http://ieeexplore.ieee.org/document/7748469/

10. Multivariate Cryptography Based on Clipped Hopfield Neural Network
Author(s): Jia Wang; Lee-Ming Cheng; Tong Su
Page(s): 353 - 363
http://ieeexplore.ieee.org/document/7755851/

11. Passivity and Output Synchronization of Complex Dynamical Networks With Fixed and Adaptive Coupling Strength
Author(s): Jin-Liang Wang; Huai-Ning Wu; Tingwen Huang; Shun-Yan Ren; Jigang Wu
Page(s): 364 - 376
http://ieeexplore.ieee.org/document/7756377/

12. Constrained Null Space Component Analysis for Semiblind Source Separation Problem
Author(s): Wen-Liang Hwang; Keng-Shih Lu; Jinn Ho
Page(s): 377 - 391
http://ieeexplore.ieee.org/document/7762224/

13. Learning to Predict Eye Fixations via Multiresolution Convolutional Neural Networks
Author(s): Nian Liu; Junwei Han; Tianming Liu; Xuelong Li
Page(s): 392 - 404
http://ieeexplore.ieee.org/document/7762165/

14. Network Unfolding Map by Vertex-Edge Dynamics Modeling
Author(s): Filipe Alves Neto Verri; Paulo Roberto Urio; Liang Zhao
Page(s): 405 - 418
http://ieeexplore.ieee.org/document/7762202/

15. Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors
Author(s): Luis A. Vázquez; Francisco Jurado; Carlos E. Castaneda; Victor Santibánez
Page(s): 419 - 426
http://ieeexplore.ieee.org/document/7762884/

16. Investigating Echo-State Networks Dynamics by Means of Recurrence Analysis
Author(s): Filippo Maria Bianchi; Lorenzo Livi; Cesare Alippi
Page(s): 427 - 439
http://ieeexplore.ieee.org/document/7765110/

17. CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification
Author(s): Yinan Yu; Konstantinos I. Diamantaras; Tomas McKelvey; Sun-Yuan Kung
Page(s): 440 - 456
http://ieeexplore.ieee.org/document/7776910/

18. Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems
Author(s): Ridong Zhang; Jili Tao; Renquan Lu; Qibing Jin
Page(s): 457 - 469
http://ieeexplore.ieee.org/document/7778125/

19. Forward Stagewise Additive Model for Collaborative Multiview Boosting
Author(s): Avisek Lahiri; Biswajit Paria; Prabir Kumar Biswas
Page(s): 470 - 485
http://ieeexplore.ieee.org/document/7778196/

20. Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization
Author(s): Yina Han; Kunde Yang; Yixin Yang; Yuanliang Ma
Page(s): 486 - 499
http://ieeexplore.ieee.org/document/7792117/

21. High-Dimensional Function Approximation With Neural Networks for Large Volumes of Data
Author(s): Peter Andras
Page(s): 500 - 508
http://ieeexplore.ieee.org/document/7833076/