CFP: 2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2018) (Feb 4)

http://civemsa2018.ieee-ims.org/

You are invited to submit papers for the 2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2018) to be held in Ottawa,Ontario, Canada from June 12-14, 2018. The conference is dedicated to all aspects of computational intelligence, virtual environments and human-computer interaction technologies for measurement systems and related applications.

Papers are solicited on, but not restricted to the following topics: 

Intelligent Measurement Systems • Human-computer Interaction • Augmented & Virtual Reality • Accuracy & Precision of Neural & Fuzzy Components • Accuracy & Precision of Virtual Environments • Perception, Neurodynamics, Neurophysiology, Psychophysics • Multimodal Sensing • Multimodal (Visual, Haptic, Audio, etc.) Virtual Environments • Sensors & Displays • Calibration and System Calibration • Multi-Sensor Data Fusion & Intelligent Sensor Fusion • Intelligent Monitoring & Control Systems • Neural & Fuzzy Technologies For Identification, Prediction, & Control of Complex Dynamic Systems • Evolutionary monitoring & control • Evolutionary Techniques For Optimization & Logistics • Neural & Fuzzy Signal/Image Processing For Industrial, Environmental & Domotic Applications • Neural & Fuzzy Signal/Image Processing For Entertainment & Educational Applications • Image Understanding & Recognition • Machine & Deep Learning for Intelligent Systems • Object Modeling • Object & System Model Validation • Virtual Reality languages • Computational Intelligence Technologies For Robotics & Vision •Computational Intelligence Technologies For Medical & Bioengineering Applications • Computational Intelligence For Entertainment & Educational Applications • Distributed Collaborative Virtual Environments • Model-Based Telecommunications & Telecontrol • Hybrid Systems • Fuzzy & Neural Components For Embedded Systems • Hardware Implementation of Neural & Fuzzy Systems For Measurements • Neural, Fuzzy & Genetic/Evolutionary Algorithms For System Optimization & Calibration • Neural & Fuzzy Techniques For System Diagnosis • Reliability of Fuzzy & Neural Components • Fault Tolerance & Testing In Fuzzy & Neural Components • Neural & Fuzzy Techniques For Quality Measurement • Standards • Human Machine Interaction

To view the full list of conference topics, please visit: http://civemsa2018.ieee-ims.org/.

CFP: 2018 IEEE Symposium Series on Computational Intelligence

18-21 November, 2018 Bengaluru, India

The 2018 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2018) is a flagship annual international conference sponsored by the IEEE Computational Intelligence Society promoting all aspects of computational intelligence. The IEEE SSCI 2018 co-locates several symposia under one roof, each dedicated to a specific topic in the computational intelligence domain, thereby encouraging cross-fertilization of ideas and providing a unique platform for top researchers, professionals, and students from all around the world to discuss and present their findings.

The IEEE SSCI meeting features a large number of keynote addresses, tutorials, panel discussions and special sessions all of which are open to all participants. Each meeting will consider awarding best student paper and best overall paper awards. The conference proceedings of the IEEE SSCI have always been included in the IEEE Xplore and indexed by all other important databases. The IEEE SSCI 2018 will be held in Bangalore, India, a garden city, also known as a Silicon Valley of India.

List of Concurrent IEEE Symposia at IEEE SSCI 2018

• Adaptive Dynamic Programming and

• Reinforcement Learning

• CI Applications in Smart Grid

• CI in Big Data

• CI in Control and Automation

• CI in Healthcare and E-health

• CI for Communication Systems

• CI in Cyber Security

• CI in Data Mining

• CI in Dynamic and Uncertain Environments

• CI in E-governance

• CI for Ensemble Learning

• CI for Engineering Solutions

• CI for Financial Engineering and Economics

• CI in Human-like Intelligence Industry

• CI for Multimedia Signal and Vision Processing

• CI in Robotics Rehabilitation and Assistive Technologies

• CI for Security and Defense Applications

• CI in Scheduling and Network Design

• CI in Vehicles and Transportation Systems

• Deep Learning

• Evolving and Autonomous Learning Systems

• CI in Feature Analysis, Selection, and Learning in Image and Pattern Recognition

• Foundations of Computational Intelligence

• Intelligent Agents

• Evolvable systems from biology to hardware

• CI for Embedded and Cyberphysical Systems

• Model-Based Evolutionary Algorithms

• Multi-criteria Decision-Making

• Robotic Intelligence in Informationally Structured Space

• Differential Evolution

• Swarm Intelligence Symposium

• Neuromorphic Cognitive Computing

• CI in Remote Sensing

• CI in Internet of Everything

• Immune Computation

• CI in Wireless Systems

Each symposium has its own organizing committee. Visit http://www.ieee-ssci2018.org/ to learn more about each symposium and its own call for papers! One registration provides you access to all symposia and tutorials and all symposia events.

The IEEE SSCI will feature a series of tutorials featuring a diverse range of topics of relevance to SSCI (the list of symposia gives a good idea of relevant topics, but proposals outside these will also be considered if found relevant).

Important Dates:

Special Session Proposals : April 15, 2018

Tutorial Proposals :             May 15, 2018

Paper Submissions :            June 15, 2018

Early Registration :             September 15, 2018

Organizing Committee:

Honorary Chair:      Nikhil R Pal, Indian Statistical Institute, India

General Co-Chairs: Sundaram Suresh, Nanyang Technological University, Singapore

        Koshy George, People Education Society, Bengaluru, India

Program Chairs:      B.K. Panigrahi, India Institute of Technology – Delhi, India

        Julia Chung, National Cheng Kung University, Taiwan

Finance Chair:        P.N. Suganthan, Nanyang Technological University, Singapore

Organizing Chair:   Jayavelu Senthilnath, Nanyang Technological University, Singapore

Web Chair:             Mukesh Prasad, University of Technology Sydney, Australia

Email us at : ssci2018@gmail.com

For more information, please visit: http://www.ieee-ssci2018.org/

See You in Bengaluru, India

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

Call for workshops and special session proposals -- SmartWorld2018

The 4th IEEE Smart World Congress (SmartWorld 2018)

October 8-12, 2018

Guangzhou, China

http://www.smart-world.org/2018/





SmartWorld 2018 calls for workshops and special sessions which will complement the research topics of the Smart World.



A workshop refers to an academic event in conjunction with SmartWorld 2018. A special session is an embedded session in SmartWorld 2018, focusing on a specific research topic relevant to the main conference. Each workshop will be expected to accept at least 6 papers. Each special session will be expected to accept 4-6 full-length papers.



Workshop/special session organizers are responsible for forming program committees, circulating Call for papers, organizing submissions and reviews as well as planning the final programs. SmartWorld 2018 workshop/special session co-chairs will assist the workshop/special session organizers in organizing workshops/special sessions and ensure their quality and success. The registration fees for workshops/special sessions will be determined by the organizing committees of SmartWorld 2018, which will provide workshop/special session facilities. Workshops/special sessions should strictly follow the important dates. The paper submission deadlines could be after that of the main conference to allow workshops/special sessions to pick up some good papers submitted to the main conference. However, sufficient time (5-7 weeks), should be allocated for peer reviews. Each paper should be reviewed by at least three experts in the corresponding areas.



In order to encourage the workshop organizers, the main conference will offer the following benefits to the workshop organizers: (1) If a workshop has 10-20 full registrations, then one full registration will be waived. (2) If a workshop receives more than 20 full registrations, then its organizers can choose to get a travel grant (up to USD1500) to attend the conference, or to invite one keynote speaker for the workshop with a free registration and travel grant (up to USD1500) for the keynote speaker. The registrations must be from a workshop’s own received submissions and accepted papers only, excluding transferred papers from other workshops/conferences.



Please email your workshop/special session proposals in PDF format by Feb. 8, 2018 to: the SmartWorld 2018 workshop/special session co-chairs, Manuel Roveri (roveri@elet.polimi.it), Seiichi Ozawa (ozawasei@kobe-u.ac.jp) and Qin Liu (gracelq628@hnu.edu.cn). Please use SmartWorld 2018 workshop and special session proposal as the email subject. Paper Submissions for accepted workshop/special session should follow the same Paper Submission Guidelines for the main conference. The length of a workshop paper submission may be about 6 pages.



Workshop & Special Session - Important Dates

- Proposal due: February 8, 2018

- Proposal notification: April 8, 2018

- Submission & notification dues: To be decided by individual workshop/Special Session

- Camera ready version due: August 8, 2018



Contact



Please email inquiries concerning Smartworld 2018 to Conference Organizers: SmartWorld2018Guangzhou@googlegroups.com.



Prof. Guojun Wang, General Chair of SmartWorld 2018



*******************************************************************

Dr. Guojun Wang, Pearl River Scholarship Distinguished Professor

Director of Institute of Computer Networks,

Vice Dean of School of Computer Science and Educational Software,

Guangzhou University, Guangzhou, Guangdong Province,

P. R. China, 510006

Tel/Fax: +86-20-39366920, Mobile: +86-13360581866

Email: csgjwang@gzhu.edu.cn; csgjwang@gmail.com

http://trust.gzhu.edu.cn/faculty/~csgjwang/publications.html

*******************************************************************

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 TETCI Special Issue on Computational Intelligence in Data-Driven Optimization (Jan 31)

I. AIM AND SCOPE

Most evolutionary algorithms and other meta-heuristic search methods typically assume that there are explicit objective functions available for fitness evaluations. In the real world, however, such explicit objective functions may not exist in many cases. For example, in many process industry optimization problems, no explicit models exist for describing the relationship between the final quality of the product and the decision variables, such as control loop outputs and grinding particle size in hematite grinding processes. Therefore, some computationally very intensive numerical simulation, such as computational fluid dynamic simulations or finite element analysis or even physical experiments, are instead conducted as the way to evaluate the fitness value. Thus, historical experimental data becomes significantly important and can be used for optimization. There are also cases where only factual data can be collected.

For solving such optimization problems, evolutionary optimization can be conducted only using a data-driven approach. Data-driven evolutionary optimization can largely be divided into two paradigms, one termed off-line data-driven optimization, where no additional data can be sampled during optimization, and the other is called on-line data-driven optimization, where only a limited number of new data points can be actively sampled during optimization. For both paradigms of data-driven optimization, seamless integration of machine learning techniques, such as model selection, ensemble learning, active learning, semi-supervised learning and transfer learning with evolutionary optimization are essential, due to the fact that data acquisition is very expensive, either computationally or costly.

This special issue aims to present the most recent advances in data-driven optimization, in particular in the integration of evolutionary algorithms and other meta-heuristic search methods with machine learning techniques, neural networks and fuzzy logic systems for surrogate modelling, data mining, preference articulation, and decision-making.

II. TOPICS

The topics of this special issue include but are not limited to the following topics:

• Surrogate-assisted optimization of computationally expensive problems
• Adaptive sampling using active learning and statistical learning techniques
• Surrogate model management in single and multiobjective optimization
• Semi-supervised and transfer learning in data driven optimization
• Machine learning for distributed data driven optimization
• Knowledge mining and transfer for data-driven optimization 
Data-driven large scale and/or many-objective optimization problems
• Preference modeling and articulation in multi- and manyobjective optimization
• Real world applications including multidisciplinary optimization

III. IMPORTANT DATES

• Paper submission deadline: January 31, 2018
• Notice of the first round review: April 15, 2018
• Revision due: June 15, 2018
• Final notice of acceptance/reject: July 30, 2018

IV. SUBMISSION

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 “Computational Intelligence in DataDriven Optimization” and clearly marking “Computational Intelligence in Data-Driven Optimization 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.

V. GUEST EDITORS

Dr. Chaoli Sun, Department of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, Shanxi 030024 China. Email: chaoli.sun.cn@gmail.com

Dr. Handing Wang, Department of Computer Science, University of Surrey, Guildford, GU2 7XH, UK. Email: handing.wang@surrey.ac.uk

Prof. Wenli Du, School of Information Science & Engineering, East China University of Science and Technology, Shanghai, 200237, China. Email: wldu@ecust.edu.cn

Prof. Yaochu Jin, Department of Computer Science, University of Surrey, Guildford, GU2 7XH, UK. Email: yaochu.jin@surrey.ac.uk

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)