October 30, 2017

Cross-Disciplinary Sessions

 

CDSS-01 Computational Intelligence for the Automated Design of Machine Learning and Search
CDSS-02 Computational Intelligence methods for Natural Language Processing
CDSS-03 The Role of Computational Intelligence Technologies in Controlling Borders
CDSS-04 Distributed Computational Intelligence (CI) Methods for Data Analytics Over the Internet of Everything
CDSS-05 Computational Intelligence for Bioinformatics and Computational Biology
CDSS-06 Interactive/Multiple Clustering using Evolutionary Computation, Fuzzy, Machine Learning and/or Neural Networks
CDSS-07 Computational Intelligence for Music, Art, and Creativity
CDSS-08 Computational Intelligence for Games
CDSS-09 Hybrid Special Session on Intelligent Physiological and Affect Aware Systems (IPAAS)
CDSS-10 Computational Intelligence for Energy Storage Systems Modeling and Management
CDSS-11 Computational Intelligence for Cognitive-Cyber-Physical Autonomous Systems (CI-C2PAS)
CDSS-12 Computational intelligence in power system
CDSS-13 Computational Intelligence to Data Enginnering and its Applications to Real-World Problems
CDSS-14 Computational Intelligence in Intelligent Transportation Systems
CDSS-15 Special Session on Computational Intelligence for Cognitive Robotics
CDSS-16 Computational Intelligence for Performance optimisation of PID-like controllers
CDSS-17 Computational Intelligence for Smart Grids Security and Cyber-Physical Power Systems Security
CDSS-18 Special Session on Computational Intelligence Methods towards Big Data Analytics
CDSS-19 Computational Intelligence in Aerospace Science and Engineering
CDSS-20 Nature-inspired design, evolution, and optimization of intelligent systems
CDSS-21 Computational Intelligence for Semantic Knowledge Management
CDSS-22 IEEE CIS Computational Finance and Economics Technical Committe
CDSS-23 Computational Intelligence for Sensing and Predicting Human Mental and Medical State (CI-SEPHUMMES)
CCDS-24 Knowledge Discovery in the Internet of Things (KDIOT)
CDSS-25 Computational Intelligence in Autonomous Vehicles, Driverless Car and Advanced Vehicle Systems
CDSS-26 Open Datasets for Computational Intelligence

 

 

CDSS-01 Computational Intelligence for the Automated Design of Machine Learning and Search

Organized by Nelishia Pillay (pillayn32@ukzn.ac.za), Rong Qu (rong.Qu@nottingham.ac.uk)

Websitehttp://titancs.ukzn.ac.za/WCCI2018SpecialSession.aspx

Machine learning and search algorithms play an imperative role in solving real world problems in industry and business sectors. Systems employing these techniques have contributed to many facets of industry including data mining, transportation, health systems, computer vision, computer security, robotics, software engineering and scheduling amongst others. These systems employ one or more techniques such as neural networks, fuzzy logic, evolutionary algorithms, multi-agent approaches and rule-based systems. Implementation of these techniques require a number of design decisions to be made, e.g. what architecture to use, what parameter values to use, and derivation of problem specific operators. It may also be necessary to employ a hybrid system combining techniques to solve a problem which introduces additional decisions such as which techniques to use and how to combine these techniques. This makes the development of computational systems time consuming, requiring many person-hours. Consequently, there have been a number of initiatives to automate these processes using computational intelligence.

There has been a fair amount of research into parameter tuning and control. The field of auto-ML aims to automate the design of machine learning algorithms so as to produce off-the-shelf machine learning techniques. Attempts to automate neural network architecture design has led to the field of neuroevolution. Research in this area has also been directed at inducing fuzzy
functions, rule-based systems and multi-agent architectures. Hyper-heuristics, which were initially aimed at providing generalized solutions to combinatorial optimization problems, are shown to be effective in the automated design of search techniques. Evolutionary algorithms such as genetic programming and genetic algorithms have made a valuable contribution to this
field.

Scope and Topics

The aim of this special session is to examine and promote recent developments in the field and future directions including the challenges and how these can be overcome.
The topics include but are not limited to:

  • Architecture design, e.g. design of neural networks and multi-agent architectures
  • Automated hybridization of intelligent techniques
  • Auto-ML
  • Automatic programming
  • Derivation of constructive heuristics
  • Derivation of evaluation functions
  • Derivation of operators
  • Explainable machine learning
  • Hyper-heuristics
  • Neuroevolution
  • Parameter control and tuning
  • Search-based software engineering
  • Self*-search

 

CDSS-02 Computational Intelligence methods for Natural Language Processing

Organized by Keeley Crockett (K.Crockett@mmu.ac.uk), Joao Paulo Carvalho (joao.carvalho@inesc-id.pt)

Although language, or linguistic expressions, undoubtedly contains fuzziness in nature, very little research has been conducted in related fields in recent years, as it was shown in “A Critical Survey on the use of Fuzzy Sets in Speech and Natural Language Processing”, Proc. of the IEEE WCCI 2012, Brisbane, Australia. This is partly because of the prevalence of probabilistic machine learning technologies in the natural language processing field. However, there has been a growing recognition that fuzziness found in every aspect of human language has to be adequately captured and that recent developments in the fields of computational intelligence such as computing with words can make a contribution. This session will follow on from the successful, special sessions entitled “Fuzzy Natural Language Processing” which were successfully held at IEEE FUZZ 2017 in Naples, IEEE FUZZ 2015 in Istanbul and IEEE FUZZ 2013 in India and the hybrid special sessions held at both the 2014 IEEE World Congress on Computational Intelligence in Beijing and the 2016 IEEE World Congress on Computational Intelligence in Vancouver.

The aim of this Special Session is therefore to explore new techniques and applications in the field of computational intelligence approaches to natural language processing.

Scope and Topics

The session will provide a forum to disseminate and discuss recent and significant research efforts in fuzzy, neural and evolutionary methods for natural language processing in addition to hybrid and emerging computational intelligence paradigms. It also seeks to present novel applications of computational intelligence technologies within the field of natural language processing It invites researchers from different related fields and gathers the most recent studies including but not limited to:

  • Fuzzy set models of human language
  • Computational intelligence applications to human language processing
  • Machine learning approaches to human language
  • Computational intelligence approaches to text mining
  • Computational Intelligence simulations of language use
  • Fuzzy ontologies for human language
  • Computational intelligence applications to the semantic web
  • Computing with words within natural language processing
  • Real world computational intelligence inspired natural language processing applications
  • Computational intelligence founded methodologies, tools and techniques for mining and interpretation of social media textual data

 

CDSS-03 The Role of Computational Intelligence Technologies in Controlling Borders

Organized by Keeley Crockett (K.Crockett@mmu.ac.uk), Rodoula Makri, George Boultadakis

Websitehttp://www.iborderctrl.eu/IEEE-WCCI2018-SS-CI-in-Controlling-Borders

Continuous border traffic growth, combined with the increased threat of illegal immigration, is putting border agencies under considerable pressure internationally. Border control is likely to face increasing demands for performance efficiency whilst maintaining high levels of security and conformity to legal frameworks, implying the need for intelligent systems that are user friendly and reliable in operational conditions, overcoming the limitations and potential gaps of current operational procedures. Key challenges are in the design of such systems which harness computational intelligence algorithms whilst allowing human empowerment, through the use of technologies which are familiar to all stakeholders.

To address these challenges multi-disciplinary research needs to be carried out in order to develop comprehensive systems to be designed and implemented which can provide automated computationally intelligence platforms. Recent research examples focused on the land border control include disciplines in the areas of: analysis of the traveller’s non-verbal behaviour, analytics of document authenticity, discovery of key patterns through data-mining and machine learning for border control analytics, hidden human detection to confront illegal immigration, advanced algorithms, big data, artificial intelligence and neural networks along with face, fingerprints and palm vein biometric models. These are just some key examples of how scientific disciplines can be combined together to enable automatic risk assessment enabling reliable decision making at border control points which respect an individual’s privacy whilst maintaining data security.
Based on these emerging research trends, the aims of this Special Session are to:

  • to provide a forum for new computational intelligence methodologies / techniques and systems which contribute towards improving border crossing efficiency and security within border control solutions;
  • to provide the opportunity to present recent advances towards holistic systems that combine computational intelligence within an expanded multi-disciplinary context
  • to highlight and assess novel insights and intelligence that can effectively contribute in identifying threats, vulnerabilities and risks in border control improving decision making and efficiency
  • to investigate the social and ethical implications of using computational intelligence technology on the ‘passenger’ themselves.

Scope and Topics

The session will provide a forum for both academia and industry to disseminate and discuss recent and significant research efforts in fuzzy, neural and evolutionary methods and their integration into border security solutions, in addition to hybrid and emerging computational intelligence paradigms. It also seeks to present novel applications of computational intelligence technologies which contribute to human decision making within the border control lifecycle. Technical papers addressing research challenges in these areas are welcomed.
It invites researchers from different related fields and gathers the most recent studies including but not limited to:

  • Biometric identification using computational intelligence
  • Automated interviews of passengers using intelligent systems
  • Detection of fraudulent passenger documents
  • The role of computational intelligence in advanced passenger registration
  • Interaction of sensors, detection tools and advanced signal processing with intelligent systems
  • Confronting illegal immigration through intelligent systems and detection of hidden humans
  • Automated decision making systems at borders
  • Passenger Identity control and status prediction
  • Intelligent Border Control Analytics
  • Ethical and social impact on the use of computational intelligence based systems with stakeholders

 

CDSS-04 Distributed Computational Intelligence (CI) Methods for Data Analytics Over the Internet of Everything

Organized by Farookh Khadeer Hussain, Mukesh Prasad, Albert Zomaya

Computational Intelligence (CI) is a collection of techniques and methods that have been proven to be capable of solving dynamic, complex, real-world business problems and aiding decision making. The various CI methods have been around for a number of decades and have been used by enterprise sectors across the globe for reliable and accurate decision making. Computational Intelligence (CI) methods include (but are not limited to) neural networks, fuzzy systems, and evolutionary computation.

A critical issue facing the CI world is how to handle, analyse, make sense of and derive critical business insights fromthe large amount of enterprise data that is continuously beinggenerated, particularly since this data originates from devices/nodes that are distributed globally and are not confinedto a particular location. For example, the Internet of Things (IoT) has revolutionized the manner in which enterprise data is created and managed, especially in respect of the volume, velocity,and heterogeneity of enterprise data creation and management. It is conservatively estimated that by 2050 there will be 50 billion IoT nodes, roughly seven times the world’s human population, which leads to the pragmatic notion of an ‘Internet of Everything’ (IoE) in which almost every device will be connected to the Internet and churning out data at an unprecedented rate. Enterprise Cloud platforms have come to the rescue in providing a reliable mechanism for the storage and processing of this data. ‘Cloud-enabled Data Analytics’ or ‘Cloud-based Data Analytics’ is a new and upcoming area which requires the development and use of novel, efficient, optimized and distributed CIalgorithms on top of both the incoming data streams (from IoT nodes) and the static data stored on Cloud platforms.From a Computational Intelligence perspective, the transition from traditional ‘Computational Intelligence’ to ‘Distributed ComputationalIntelligence’ is not automatic and needs to be researched. CI methods need to be developed which will enable real-time decision making on data from distributed sources. The accuracy and speed of these distributed CI algorithms are critical for real-time and reliable decision making. Currently, these are open research issues and challenges which impede the deployment of Distributed Computational Intelligence on top of the ‘Internet of Everything’.

For the ‘Internet of Everything’ to become a reality, there is additionally an urgent need to address the underlying issues of intelligent data storage and data processing at two key storage and processing levels – the Edge Level and the Cloud Level. The Edge level is an intermediate transitory level for data storage; however, it can also be used for efficient data analytics (at the Edge level). There is also a need to engineer new distributed and intelligent CI solutions that will work in tandemwith the Edge level and the Cloudlevel, and will synchronize with each other on either a real-time basis or a quasi-real-time basis depending on the business application at hand.

Scope and Topics

The goal of this special session is to target topics related to distributed, computational intelligence algorithms, methods and techniques for reliable business intelligence on top of the Internet of Everything.We invite interested authors to submit their original and unpublished work to this special session.

The main topics of this special session are as follows:

  • Data Analytics middleware for Edge computing
  • Cloud-based intelligent analytics
  • Edge-node-driven data analytics
  • Intelligent data synchronization and updating between Edge nodes or Cloud nodes
  • Data metering for Edge nodes and Cloud nodes
  • Intelligent pricing mechanisms for Edge nodes and Cloud nodes
  • Data-driven privacy and security solutions in Edge computing and Cloud computing
  • Case studies for data analytics using Edge nodes or Cloud nodes

 

CDSS-05 Computational Intelligence for Bioinformatics and Computational Biology

Organized by Antonello Rizzi (antonello.rizzi@uniroma1.it), Alessandro Giuliani (alessandro.giuliani@iss.it)

Websitehttps://sites.google.com/a/uniroma1.it/wcci2018-ci4bcb/

Bioinformatics and Computational Biology deal with a wide range of problems and applications which, in recent years, have been successfully solved by means of Computational Intelligence and Machine Learning techniques. Moreover, due to technological progress, huge amounts of data concerning biological organisms are gathered and collected (e.g. genes transcript, protein structures and the like), thereby demanding the use of parallel and distributed computing for facing Big Datasets and/or high-throughput application requirements. Further, in such fields, data usually encodes complex information, which is natively represented by structured records, such as sequences, graphs and images, most of which lie in so-called “non-metric spaces”, i.e. input spaces for which a meaningful (dis)similarity measure might not be metric, making the problem more challenging since ad-hoc (dis)similarity measures or embedding functions need to be defined.

Scope and Topics

This Special Session aims at collecting the latest research in Computational Intelligence applications for Bioinformatics and Computational Biology, with emphasis on parallel/distributed computing and non-metric spaces analysis, by means of different (or hybridization of) Computational Intelligence techniques, from evolutionary meta-heuristics to neural computation, from pattern recognition to fuzzy systems.

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

  • Protein function prediction
  • Protein folding prediction
  • Generative models for protein contact networks
  • String kernel methods for sequence classification
  • Mining metabolic pathways
  • Gene finding and prediction
  • Exact/inexact motifs and pattern matching
  • Network and Systems Biology
  • Granular computing approaches for non-metric spaces analysis
  • Large-scale data mining and pattern recognition
  • Distributed and parallel computing systems for machine learning and data mining
  • Clinical Diagnostic Systems
  • Medical image analysis

 

CDSS-06 Interactive/Multiple Clustering using Evolutionary Computation, Fuzzy, Machine Learning and/or Neural Networks

Organized by Marcilio de Souto (desouto@univ-orleans.fe), Andre de Carvalho (andre@icmc.usp.br), Christel Vrain (vrain@univ-orleans.fr), Guillaume Cleuziou (guillaume.cleuziou@univ-orleans.fr)

Websitehttps://sites.google.com/site/interactivemultipleclustering/home

Clustering is a well-studied domain. Currently, more and more data are collected from multiple sources or represented by multiple views (e.g., text, video, images, biological data, among others). Also, for the same data there might exist several different structures (clusterings) which are meaningful for the user. In this context, clustering techniques are often required to be able to provide several possibilities for analyzing the data. As a consequence, in recent years, the interdisciplinary research topic on multiple clusterings has drawn significant attention of the data mining community.

Another topic of recent interest in clustering is interactive clustering. For instance, usually clustering is studied in the unsupervised learning framework. However, as pointed out in some studies, in several real-world problems, such as personalized recommendations, it is not possible to reach the “optimal” clustering (the solution that meets the requirements of the user) without interacting with the end user. In order to approach this problem, recently frameworks for interactive clustering with human in the loop have been proposed. These algorithms can interact with the human in steps and receive feedback to improve.

Scope and Topics

The aim of this special session is to bring together researchers from Machine Learning and Data Mining which are actively working in the fields of multiple clusterings and interactive clustering. The idea is to cover a wide spectrum of topics, ranging from multi-view clustering, the interaction with human supervisors to constraint-based clustering, and stimulate cross-fertilization. In this context, as it is a cross-disciplinary session, we welcome papers in which techniques such machine learning, neural networks, fuzzy systems and evolutionary computing are used in the context of Interactive/Multiple Clustering.

In particular, we welcome contributions that address aspects including, but not limited to:

  • Cluster ensemble and   Multi-view clustering:
  • How to combine/merge different clustering, how to control/force the disagreement/diversity between distinct clusterings, how to choose a solution among too many possible ones, how to represent/visualize a set of solutions.
  • Multi-objective clustering.
  • Multiple clustering solutions from very high dimensional and complex databases.
  • Constraint-based clustering and applications as, for instance, alternative clustering.
  • New approaches to interactive clustering.
  • Methodologies for the evaluation of interactive clustering, and comparative studies.

 

CDSS-07 Computational Intelligence for Music, Art, and Creativity

Organized by Chuan-Kang Ting, Francisco Fernández de Vega      

Website: http://cilab.cs.ccu.edu.tw/ci-tf/CIMAC2018.html

Computational intelligence (CI) techniques, including evolutionary computation, neural networks, and fuzzy systems, have shown to be effective for search, optimization, and machine learning problems. Recently, evolutionary computation and deep neural networks gained several promising results and become important tools in computational creativity, such as in music, visual art, literature, architecture, and industrial design.

Scope and Topics

The aim of this special session is to reflect the most recent advances of CI for Music, Art, and Creativity, with the goal to enhance autonomous creative systems as well as human creativity. This session will allow researchers to share experiences and present their new ways for taking advantage of CI techniques in computational creativity. Topics of interest include, but are not limited to, CI in the following aspects:

  • Generation of music, visual art, literature, architecture, and industrial design
  • Algorithmic design in creative intelligence
  • Application of CI to music analysis, classification/clustering, composition, variation and improvisation
  • Optimization in creativity
  • Development of hardware and software for creative systems
  • Evaluation methodologies
  • Assistance of human creativity
  • Computational aesthetics
  • Emotion response
  • Human-machine creativity

 

CDSS-08 Computational Intelligence for Games

Organized by Daniel Ashlock (dashlock@uoguelph.ca), Jialin Liu and Santiago Ontañón

Games are an ideal domain to study computational intelligence (CI) methods because they provide affordable, competitive, dynamic, reproducible environments suitable for testing new search algorithms, pattern-based evaluation methods, or learning concepts.  Games scale from simple problems for developing algorithms to incredibly hard problems for testing algorithms to the limit.  They are also interesting to observe, fun to play, and very attractive to students. Additionally, there is great potential for CI methods to improve the design and development of both computer games as well as tabletop games, board games, and puzzles.  This special session aims at gathering leaders and neophytes in games research as well as practitioners in this field who research applications of computational intelligence methods to computer games.

Scope and Topics

In general, papers are welcome that consider all kinds of applications of methods (evolutionary computation, supervised learning, unsupervised learning, fuzzy systems, game-tree search, rolling horizon algorithms, MCTS, etc.) to games (card games, board games, mathematical games, action games, strategy games, role-playing games, arcade games, serious games, etc.).

Examples include but are not limited to

  • Adaptation in games
  • Automatic game testing
  • Coevolution in games
  • Comparative studies (e.g. CI versus human-designed players)
  • Dynamic difficulty in games
  • Games as test-beds for algorithms
  • Imitating human players
  • Learning to play games
  • Multi-agent and multi-strategy learning
  • Player/opponent modelling
  • Procedural content generation
  • CI for Serious Games (e.g., games for health care, education or training)
  • Results of game-based CI and open competitions

 

CDSS-09 Hybrid Special Session on Intelligent Physiological and Affect Aware Systems (IPAAS)

Organized by Faiyaz Doctor (faiyaz.doctor@gmail.com), Dongrui Wu (drwu09@gmail.com), Marie-Jeanne Lesot (Marie-Jeanne.Lesot@lip6.fr), Ariel Ruiz-Garcia (ariel.ruizgarcia@coventry.ac.uk)

Website: http://www.wcci-ipaas.com/

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

Practical applications of AC and PC based systems seek to achieve a positive impact on our everyday lives by monitoring, recognising and acting on our emotional states and physiological signals. Integrating these sensing modalities into scalable human data analytics and pervasive computing systems will reveal a far richer picture of how our fleeting emotional responses, changing moods, feelings and sensations, such as pain, touch, tastes and smells, are a reaction to or influence how we implicitly or explicitly interact with the environment and increasingly the connected computing artifacts within. The increasing use of unobtrusive wearable sensors, self-monitoring devices and developments in micro- and nanofabrication of implantable devices can be used to track a variety of physiological indicators (e.g. blood pressure, blood gas, pulse, insulin level, EKG, EEG), the interpretation of affective states and behavior contextualization in combination of wider pervasive sensing.

The integration and use of AC and PC raise many new challenges for signal processing, machine learning and Computational Intelligence (CI). Fuzzy Logic Systems in particular provide a highly promising avenue for addressing some of the fundamental research challenges in AC/PC where most data sources such as: body signals (e.g., heart rate, brain waves, skin conductance and respiration) facial features, speech and human kinematics are very noisy/uncertain and subject-dependent. Similarly, the recent success of deep neural network models on classification problems make them a viable path for the development of human-computer interfaces. Clearly however, other key areas of CI research, such as evolutionary learning algorithms and emerging biologically inspired models provide essential tools for behavior discovery and optimization of complex real-word systems and processes using affective and physiological data.

Scope and Topics

The Computational Intelligence and Physiological and Affective Computing special session aims to bring together researchers from the three areas of CI to discuss how CI techniques can be used individually or in combination to help solve challenging AC/PC problems, and conversely, how physiological and affect (emotion) and its modeling can inspire new approaches in CI and its applications. Topics of interest for this special session include but are not limited to:

  • Models of emotion and physiological information
  • Classifiers for physiological information
  • Applications based on/around physiological information
  • Affect acquisition and processing mobile and unconstraint environments
  • CI based architectures for processing emotions and other affective states
  • Automatic and real-time emotion recognition & synthesis from physiological signals, facial expressions, body language, speech, or neurocognitive performance
  • Emotion mining from texts, images, or videos
  • Affective interaction with virtual agents and robots
  • Applications of affective computing in interactive learning, affective gaming, personalized robotics, virtual reality, social networking, pervasive environments, healthcare and behavioral informatics, etc.

 

CDSS-10 Computational Intelligence for Energy Storage Systems Modeling and Management

Organized by Fabio M. Frattale Mascioli (fabio.mascioli@pomos.it), Antonello Rizzi (antonello.rizzi@uniroma1.it), Maurizio Paschero (maurizio.paschero@uniroma1.it)

Websitehttps://sites.google.com/a/uniroma1.it/wcc2018-ci4essmm/

Energy Storage Systems (ESS)s have become widely pervasive in several sectors, both in the civil and in the industrial engineering fields. Among the several applications, the most critical ones regard the storing of energy in the future Smart Grids and microgrids, and the power sourcing for Electric and Hybrid Vehicles. In this context, the management of the ESS represents a crucial task in order to guarantee efficient, effective and robust energy storing. In order to achieve a safe and reliable usage of ESSs, it is important to synthesize suitable models capable to predict the cell behavior in order to avoid damages, to estimate the State of Charge (SoC) and the State of Health (SoH), and to perform the cells equalization. Moreover, the design of efficient and effective algorithms for optimal energy flows routing in Smart Girds and microgrids is a challenging task, especially in presence of ESSs. Computational intelligence techniques represent a powerful approach to face the abovementioned tasks, allowing to deal with the strong nonlinear and dynamic behavior of electrochemical cells, as well as to design Energy Management Systems (EMS) able to cope with nonlinear and time variant systems, such as microgrids and Smart Grids, especially in presence of stochastic renewable energy sources.

Scope and Topics 


Topics of interest include (but are not limited to) applications of Computational Intelligence techniques (Neural networks and Machine Learning, Evolutionary Optimization and Fuzzy Systems) to the following problems:

  • ESS modeling
  • ESS parameters identification
  • ESS state of charge estimation
  • ESS state of health estimation
  • ESS cell balancing
  • Neural Networks for non-linear system identification
  • EMS design for Smart Grids and micro grids in presence of ESSs
  • EMS in hybrid and electric vehicles
  • EMS in Smart Buildings
  • Computational Intelligence techniques for complex systems modelling

 

CDSS-11 Computational Intelligence for Cognitive-Cyber-Physical Autonomous Systems (CI-C2PAS)

Organized by Hussein Abbass , Jianhua Ma , Manuel Roveri , Christian Wagner

Aim & Scope

Computational Intelligence Techniques have been very successful at the interaction of cognitive-cyber- physical autonomous systems. CI techniques have been used for technologies sitting at the human-machine interface to analyse the interaction between the cognitive and cyber domain. Equally, CI has been successful for cyber security and physical robotics such as autonomous vehicles and unmanned aerial systems. The aim of this special session is to bring together success stories in theory and applications of CI techniques in the C2P domain and to showcase recent advances in these fast emerging research areas.

CI for Human-Machine Autonomous Systems

  • Adaptive Automation
  • Adaptive Interfaces and Interface Design
  • Brain Computing
  • Cognitive-Cyber Symbiosis
  • Human-Autonomy Teaming
  • Human-Brain Interface
  • Human-Robot Interaction
  • Workload and Mental load Modelling

CI for Cyber-Physical Autonomous Systems

  • Autonomic Systems
  • Cognitive Computing
  • Cognitive fault detection and diagnosis systems
  • Cyber Robots
  • Ground Vehicles
  • Ethics of C2PAS
  • Intelligent sensor networks
  • Surface Water Vehicles
  • Swarm Systems
  • Unmanned Aerial Vehicles
  • Underwater Vehicles
  • Cognitive fault-diagnosis systems
  • Smart objects and Internet of Things

Applications of C2PAS

  • Applications in Agriculture
  • Applications in Bioinformatics
  • Applications in Critical-Infrastructure Monitoring
  • Applications in Defence
  • Application in Education
  • Applications in Entertainment
  • Applications in Health
  • Applications in Psychology and Cognitive Science
  • Applications in Social Media
  • Applications in Security
  • Applications in Smart home/building

Trusted Autonomy for C2PAS

  • Trust Detection
  • Trust Evaluation
  • Trusted Machine Learning
  • Trust Modelling
  • Trust Monitoring
  • Trusted Optimisation
  • Trusted Simulation Environments
  • Trusted Systems

 

CDSS-12 Computational intelligence in power system

Organized by N Kumarappan (kumarappann@gmail.com), Ramesh Rayudu (Ramesh.Rayudu@vuw.ac.nz)

The demand for electrical energy is growing exponentially and quality and reliability requirements of modern power systems are becoming more and stringent. This special session will focus on the applications of computation intelligence for planning, operation, control, and optimization of electric power systems, in order to provide better secure, stable and reliable system. The computation techniques include neural computation, evolutionary programming , genetic programming, swarm intelligence optimization, artificial immune systems, ant colony search, pattern recognition, data mining, firefly algorithm, cuckoo search, artificial bee colony, etc.

The objective of this special session is to bring together researchers from the academia and industry in the fields of power system engineering and computational intelligence.

The need for efficient and fast computational techniques poses many research challenges. This special session seeks to promote novel research investigations in Power and Energy and related areas.

Topics of interest:

The special session invites contributions in the areas including, but not limited to, the following:

1. Power system operation
2. Power system control
3. Power system planning
4. Power system analysis
5. Power system stability
6. Power system reliability
7. Power system protection
8. Security assessment
9. Power quality
10. Load frequency control
11. Power sector reforms and restructuring
12. Renewable energy systems
13. Smart grids
14. Distributed generation
15. Reactive power compensating devices
16. Vehicle to grid and grid to vehicle (V2G and G2V)

Paper submission:

Potential authors may submit their manuscripts for presentation consideration through WCCI2018 submission system. All the submissions will go through peer review. Details on manuscript submission can be found from WCCI 2018 Website.

Important dates:

Paper submission deadline:   January 15, 2018
Notification of acceptance:    March 15, 2018
Final paper submission and early registration deadline:   May 1, 2018

CDSS-13 Computational Intelligence to Data Enginnering and its Applications to Real-World Problems

Organized by Dhiya Al-Jumeiy(d.aljumeily@ljmu.ac.uk), Abir Hussain (A.hussain@ljmu.ac.uk), Hissam Tawfik (H.Tawfik@leedsbeckett.ac.uk), Jamila Mustafina (DNMustafina@kpfu.ru)

SCOPE AND MOTIVATION

Computational Intelligence (CI), Artificial Intelligence (AI), Data Science and their applications are research areas jointly aligned to benefit research community and society. AI and Data Science encompass a broad field of Computational Intelligence disciplines including data mining, machine learning, ensemble learning, deep learning, fuzzy systems, and evolutionary computation, self-organizing systems and expert systems.

In recognition of the escalating importance and relevance of examining the processes and results associated with obtaining and managing data, as well as scrubbing, exploring, modelling, interpreting, communicating and visualising data across all research domains, including Health, Education, Environment, Medicine, Security, Science, Technology, Business, the Humanities and the Arts, the aim of this Special Session is to allow researchers to communicate their high quality, original ideas by presenting and publishing new advances in computational intelligence to data science, engineering, internet of everything, internet of urgent things and their applications.

TOPICS

The world is moving through the fourth industrial revolution, which is happening all around us and affecting and changing the way we live, work and communicate with each other and the other devices around us. Widely used a new generation of artificial intelligence in intelligent medicine, smart city, robotics, intelligent manufacturing, intelligent energy, national defence and other fields will increase the core of computation intelligence and AI
industry scale within the next decade.

This session is dedicated to researchers and practitioners interested in strategies, theories, practices and tools, exchanging new theoretical, technical and experimental design. It focuses on CI and AI real-world applications and different use cases of solid findings and insights, best practices and applications to real-life situations, and reviewing new opportunities and frameworks for Data Sciences.

This special session brings together CI, AI researchers and practitioners from different scientific disciplines with the goal of fostering collaboration between different and research groups. We aim to increase the understanding and use of AI techniques in the application to real world problems. We welcome contributions that deal with all aspects of the scientific foundations, theories, techniques and applications of computing, data and analytics, including but not limited to:

  • Internet of Everything and Evolutionary computation
  • AI Techniques Applied to Environmental Sciences
  •  Internet of Urgent things and its applications
  • AI Techniques in Support of Aviation and Aerospace Operations
  • Intelligent Approaches for Internet of Everything and its application
  • Internet of Everything to support Smart Cities
  • Computational Intelligence for Clinical Data Analysis
  • Machine Learning and its application for Decision Making
  • Machine Learning Applications in the Energy Sector
  • Deep learning methods for Diagnostic Decision Support
  • Novel data processing and analytics, tools and systems
  • Advances in Neural networks and its applications
  • Fuzzy systems and uncertainty management
  • Ensemble learning for Big data mining and knowledge discovery
  • Innovative methods for Big data complexity management
  • Big Data Engineering
  • Evolutionary Inspired Algorithms and Expert Systems
  • Biomedical Intelligence, Health Informatics and Intelligent Driven Systems
  • Linear and non-linear Learning Approaches
  • Advances in Medical Image and Signal Processing

CDSS-14 Computational Intelligence in Intelligent Transportation Systems

Organized by Prof. David Elizondo(Elizondo@dmu.ac.uk), Dr Lipika Deka (lipika.deka@dmu.ac.uk)

Description

Intelligent Transportation Systems (ITS) is a key field of research around mobility of people and goods. The term Intelligence in ITS mainly refers to innovation in methodologies and the creation of additional services rather than for actual intelligent algorithms and systems. Much of modern ITS technology was originally developed for use on roads, but ITS now covers the whole range of transportation systems. The past years have seen the development and deployment of ITS technologies around the world, increasing productivity, enhancing health, saving lives, time, costs and energy.

Scope and Topics

Many countries have invested massive public funds in research and technological development as the basis for urban and interurban implementation. They have also created their own ITS organizations to represent the industry, liaise with government, and share experience and best practice. Computational Intelligence plays a key role in the next generation of Intelligent Transport Systems. Although there are many conferences related to ITS around the world, in this special session we will focus on the theoretical and technical aspects of these systems, specifically related to computational intelligence. The aim of this special session in Computational Intelligence towards Intelligent Transport Systems is to gather and focus high quality research papers that advance ITS, provide new insights and nourish new innovation in this growing field by means of advanced Computational Intelligence based techniques

  • Adaptive Urban Transport
  • Fuzzy Logic based decision support systems
  • Fuzzy Logic and Transport Planning
  • Learning and Evolutionary Computing based Optimisation in Traffic Management
  • Learning Traffic Models for Simulation
  • Intelligent Analysis and Modelling of Transport related Air Quality
  • Adaptive Personal Mobility – Health and Wellbeing
  • Learning and Optimisation enabling Modal Shift
  • Adaptive and Optimised Supply Chain Management
  • Data Exploitation in ITS
  • Evolutionary Computing multi-objective optimisation in Intelligent Transport and Intelligent Mobility
  • Deep Learning Approaches in ITS such as for predicting traffic flow, vehicle diagnostics etc.
  • Computational Approaches towards integrating multi-modal transport.

CDSS-15 Special Session on Computational Intelligence for Cognitive Robotics

Websitehttp://www.cogrobotics.unina.it/ci4cr_2018/index.php

Organized by Giovanni Acampora, Università degli studi di Napoli Federico II(giovanni.acampora@unina.it) , Silvia Rossi, Università degli Studi di Napoli Federico II(silvia.rossi@unina.it) , Mariacarla Staffa 1 , Università degli Studi di Napoli Federico II(mariacarla.staffa@unina.it), Autilia Vitiello, Università degli Studi di Salerno,(avitiello@unisa.it)

Nowadays, robotics represents a field with a rapidly growing impact on a broad range of industrial and end users market sectors, including healthcare, agriculture, civil, commercial or consumer sectors, logistics, and transport. Nevertheless, its potential could be fully exploited only when robots have additional abilities such as adaptability, interaction capability, dependability, decisional autonomy and cognitive capability.

Aim & Scope:

This special session provides an international forum for academics, developers, and industry- related researchers belonging to the vast communities of Computational Intelligence, Machine Learning, Robotics, etc., to discuss, share experience and explore traditional and new areas of computational intelligence combined to solve a range of problems. The objective of the Special Session is to integrate the growing international community of researchers working on the application of Computational Intelligence techniques in Robotics to a fruitful discussion on the evolution and the benefits of this technology to the society.

Additionally, this special session aims at examining and promoting recent developments in the robotics field and future directions including the related challenges and how these can be overcome with particular focus on computational intelligence methodologies.

The topics include but are not limited to:

– Computational intelligence for Robotic systems
– AI methodologies for Forensic Robotics
– Evolutionary algorithms for Robotic systems
– Machine Learning for Cognitive Robotics
– Human-robot Interaction
– Social Robotics
– Secure Robotic systems
– Coordination and Communication in Robotic Teams
– Emergent and Adaptive Behaviour in Robotics
– Cognitive Architectures for Robots

 

CDSS-16 Computational Intelligence for Performance optimisation of PID-like controllers

Organized by Gilberto Reynoso Meza , Helem Sabina Sánchez

Websitehttps://www.researchgate.net/project/WCCI-special-session-CDSS-16-Computational-Intelligence-for-Performance-optimisation-of-PID-like-controllers

Special sessions description

Intelligent control is a sub-field of control systems engineering of growing interest among researchers. Nowadays, the most accepted definition for intelligent control comprises using one or several tools from computational intelligence and soft computing for control engineering purposes. Such tools range from neural networks, fuzzy logic systems and evolutionary algorithms to rule-based and knowledge-based systems. Such techniques have shown to be useful in complex instances in control systems engineering.

Although several controller structures have been proposed, the proportional-integral- derivative (PID) one (and those which are similar to or derived from the PID) remains a reliable and practical choice for several industrial processes. One of the main advantages of PID-Like controllers is their ease of implementation, giving a good trade-off between simplicity and cost to implement. Owing to this, seeking for new tuning techniques is an ongoing research topic.

PID-like controllers have shown to be the front line solution for control loops in automation systems over the years. While a substitution of such controllers by advanced soft computing tools seems to be unlikely, they might be improved using different tools and techniques from soft computing approaches. The aim of this special session is to provide the opportunity among practitioners to exchange ideas about how to optimise the performance of PID-like controllers for different systems and processes.

Topics and scope

In this special session we will focus in a very specific controller structure: the proportional-integral- derivative (PID) controllers and similar or derived structures. While it is straightforward to improve their performance via evolutionary optimisation (single and/or multi-objective), works dealing with other aspect of computational intelligence, as machine learning and expert systems, are welcome. Topics covered (but not limited to) include insights, tools and theoretical developments optimising the performance of:

  • PID structures: classical, industrial, 2DoF,
    fractional-order PID, discrete, event based,
    neuro-PID, fuzzy-PID.
  • Feedback loops with PID-like control: single-
    input single-output, multi-variable, cascade,
    multi-loops.
  • Automation processes: real applications,
    hardware in the loop integration, non-linear
    processes.
  • PID control problems: auto-tuning, fault
    detection, performance assessment, reliable
    and resilient control, cyber-security, industry
    4.0.

CDSS-17 Computational Intelligence for Smart Grids Security and Cyber-Physical Power Systems Security

Organized by Zhen Ni, South Dakota State University, SD, USA, Stefano Squartini, Università Politecnica delle Marche, Italy Yufei Tang, Florida Atlantic University, FL, USA

Aim:

New computational intelligence and machine learning frameworks have been developed as useful techniques to address many important issues in smart grids security and cyber-physical power systems security. Modern complex electric power grid operation and safety issues also need the special attention and involvement of people with expertise in neural networks, fuzzy networks and evolutionary computation. Grid modernization represents a comprehensive effort to shape the future of our nation’s grid and solve the challenges of integrating conventional and
renewable sources with energy storage and smart buildings, while ensuring that the grid is resilient and secure to withstand growing cybersecurity and climate challenges. A number of new learning and optimization methods, e.g., deep neural network and deep reinforcement learning, are the new trends to address some critical cyber security and adaptation in the cyber-physical power systems.

This special session will provide a unique platform for researchers from different societies, including computational intelligence, machine learning, power and energy, cyber security, communications, neuroscience and among others, to share their research experience towards a smart and sustainable modern power grid. The special session will also enhance the discussion among different communities to explore more challenge cross-discipline topics along this direction.

Scope and Topics:

We are particularly interested in, but not limited to the following topics:

  • Power grid security and vulnerability analysis based on computational intelligence
  • Power system dynamic stability, control and security based on computational intelligence
  • Cyber physical power system testbed and real-time simulation based on computational intelligence
  • New machine learning methods (e.g., deep learning and deep reinforcement learning) for cyber security in smart grid and microgrid
  • New computational intelligence methods for power grid transmission, distribution and communication security (i.e., encryption, authentication and access control)
  • AC/DC power flow stability analysis based on computational intelligence and machine learning
  • New game theoretic methods for smart grid security and cyber-physical power grid security
  • Power grid generation, transmission and load vulnerability assessment and physical security
  • Big data applications in cyber-physical power systems

CDSS-18  Special Session on Computational Intelligence Methods towards Big Data Analytics

Organizers: 1. Yiu-ming Cheung, PhD Professor, FIEEE, FIET, FBCS, FIETI Hong Kong Baptist University, Hong Kong

Introduction

Big data refers to a collection of data sets which is huge and complex to be directly processed using on- hand database management tools or traditional data processing techniques. With the rapid development of information technology and the decrease of cost on collecting and storing data, big data has been generated from scientific fields, industry, business sector, governmental department, and internet.

Usually, the important issue in data processing is to harness relevant data and use it to make the best decisions. However, for big data, it is difficult to find the most valuable pieces of information from a huge amount of data. Therefore, it is desired to develop a new generation of technologies and architectures to economically extract value from huge volumes of a wide variety of data by enabling high velocity capture, discovery, and analysis.

As a set of nature-inspired computational methodologies and approaches, computational intelligence methods enable intelligent behaviors in complex and dynamic environments. It generally includes, but not limited to, artificial neural networks, fuzzy systems, evolutionary computing, swarm intelligence and rough sets, and also embraces broader fields such as image processing, data mining, and natural language processing. In practice, computational intelligence methods have been applied successfully to solve complex real-world problems to which traditional approaches are time-consuming and ineffective. Therefore, they are regarded as promising techniques for big data analytics.

This special session aims at discussing and presenting the latest development on computational intelligence methods towards big data analytics. Original contributions that provide novel theories, frameworks, and solutions to challenging problems of big data analytics will be solicited for this special session.

Indicative Topics/Areas

  • Application of neural networks, fuzzy logic, rough sets, evolutionary computing, and swarm
  • intelligence in big data analysis
  • Deep learning for big data processing
  • Nature-inspired techniques for big data analytics
  • Parallel and distributed methods for knowledge discovery
  • Adaptive and evolving learning methodologies for big data analysis
  • Uncertainty modeling in learning from big data
  • Multiple learning models
  • Active and semi-supervised learning strategies
  • Data stream mining
  • Interactive learning and imbalance learning on big data
  • Intelligent data preprocessing
  • Random weighted networks and transfer learning on big data
  • Data size and feature space adaptation
  • Intelligent techniques in big data classification/clustering

Submission
Manuscripts for a Special Session should NOT be submitted in duplication to any other regular or
special sessions and should be submitted to WCCI 2018 main conference online submission system on
WCCI 2018 conference website.

All submitted papers of Special Sessions have to undergo the same review process. The technical
reviewers for each Special Session paper will be members of the WCCI 2018 Program Committee and
qualified peer-reviewers to be nominated by the Special Session organizers.

CDSS-19 Computational Intelligence in Aerospace Science and Engineering
Organisers by Prof. Massimiliano Vasile , Department of Mechanical & Aerospace Engineering
University of Strathclyde, Glasgow, UK
massimiliano.vasile@strath.ac.ukDr. Ya-zhong Luo (in Chinese, 罗亚中)
College of Aerospace Science and Engineering
National University of Defense Technology
Chang-sha, China, 410073
E-mail: luoyz@nudt.edu.cn; yzluo@sohu.comScope and Motivations:In an expanding world with limited resources and increasing complexity, optimisation and computational intelligence become a necessity. Optimisation can turn a problem into a solution and computational intelligence can offer new solutions to effectively make complexity manageable.All this is particularly true in space and aerospace where complex systems need to perate optimally often in the harsh and inhospitable environment with high level of reliability. In Space and Aerospace Sciences, many applications require the solution of global single and/or multi-objective optimization problems, including mixed variables, multi-modal and non-differentiable quantities. From global trajectory optimization to multidisciplinary aircraft and spacecraft design, from planning and scheduling for autonomous vehicles to the synthesis of robust controllers for airplanes or satellites, computational intelligence (CI) techniques have become an important – and in many cases inevitable – tool for tackling these kinds of problems, providing useful and non-intuitive solutions. Not only have Aerospace Sciences paved the way for the ubiquitous application of computational intelligence, but moreover, they have also led to the development of new approaches and methods.In the last two decades, evolutionary computing, fuzzy logic, bio-inspired computing, artificial neural networks, swarm intelligence and other computational intelligence techniques have been used to find optimal trajectories, design optimal constellations or formations, evolve hardware, design robust and optimal aerospace systems (e.g. reusable launch vehicles, re-entry vehicles, etc.), evolve scheduled plans for unmanned aerial vehicles, improve aerodynamic design (e.g. airfoil and vehicle shape), optimize structures, improve the control of aerospace vehicles, regulate air traffic, etc. This special session intends to collect many, diverse efforts made in the application of computational intelligence techniques, or related methods, to aerospace problems. The session seeks to bring together researchers from around the globe for a stimulating discussion on recent advances in evolutionary methods for the solution of space and aerospace problems.In particular evolutionary methods specifically devised, adapted or tailored to address problems in space and aerospace applications or evolutionary methods that were demonstrated to be particularly effective at solving aerospace related problems are welcome.Session TopicsAuthors are invited to submit papers on one or more of the following topics:

  • Global trajectory optimization
  • Multidisciplinary design for space missions
  • Formation and constellation design and control
  • Optimal control of spacecraft and rovers
  • Planning and scheduling for autonomous systems in space
  • Multiobjective optimization for space applications
  • Resource allocation and programmatics
  • Evolutionary computation for Concurrent Engineering
  • Distributed global optimization
  • Mission planning and control
  • Robust Mission Design under Uncertainties
  • Intelligent search and optimization methods in aerospace applications
  • Image analysis for Guidance Navigation and Control
  • Autonomous exploration of interplanetary and planetary environments
  • Implications of emerging AI fields such as Artificial Life or Swarm Intelligenceon future space research
  • Intelligent algorithms for fault identification, diagnosis and repair
  • Multi-agent systems approach and bio-inspired solutions for system design andcontrol
  • Advances in machine learning for space applications
  • Intelligent interfaces for human-machine interaction
  • Knowledge Discovery, Data Mining and presentation of large data sets

CDSS-20 Nature-inspired design, evolution, and optimization of intelligent systems

Organizers: Sebastian Basterrech, Ph.D., Czech Technical University, Czech Republic, sebastian.basterrech@agents.fel.cvut.czPavel Kromer, Ph.D., Technical University of Ostrava, Czech Republic, pavel.kromer@vsb.cz (corresponding organizer)Roman Senkerik, Ph.D., Tomas Bata University in Zlin, Czech Republic, senkerik@fai.utb.cz

Websitehttp://dap.vsb.cz/naides2018/

Intelligent systems represent an essential part of contemporary social and industrial applications. They facilitate smart operations, management, and control in their target domains and help to utilize the vast amounts of data continuously collected within smart, cognitive environments. Intelligent systems are often based on a computational implementation of successful nature-inspired problem-solving strategies. Neural, evolutionary, and swarm-based methods are only a few members of the broad family of unconventional approaches whose wide applicability was enabled by the advances in information and communication technologies. However, the design and tuning of such intelligent systems, necessary for their successful use in the complex conditions of real-world applications, is a non-trivial process that is often tackled by nature-inspired methods as well. At the same time, many hybrid (multi-paradigm) approaches such as neuroevolution, deep learning, and genetic fuzzy systems, are employed to obtain accurate and efficient intelligent systems.This special session is concerned with novel nature-inspired approaches to design, evolution, and optimization of all types of intelligent systems. It will especially welcome submissions dealing with both theoretical and practical issues of real-world systems and their data-driven adaptation and optimization.

Scope and Topics (include but not limited):

he proposed special session aims to bring together latest research on nature-inspired design, evolution, and optimization of all sorts of intelligent systems. It will facilitate knowledge exchange, technical discussions, and networking on topics of interest that include, but are not limited to:

 Nature-inspired methods for the design of intelligent systems.
• Evolution, adaptation, transfer learning, and optimization of intelligent systems.
• Mining intelligent behaviour from large data collections.
• Data- and simulation-driven intelligent systems.
• Evolutionary and swarm-based methods for fine-tuning of system parameters.
• Hybrid and multi-paradigm intelligent systems.
• Real-world applications of nature-inspired intelligent systems.
• Learning with a small number of examples and unbalanced data.
• Intelligent systems in adversarial modeling.
• Advances in the theory of evolutionary computational methods.
• Evolutionary architectures of Neural Networks.
• Evolutionary methods deep learning algorithms.
• Combination of evolutionary and non-evolutionary methods.

CDSS-21 Computational Intelligence for Semantic Knowledge Management

Organisers: Giovanni Acampora, Witold Pedrycz and Autilia Vitiello University of Naples Federico II, Italy giovanni.acampora@unina.it

Aim and Scope

From the birth of the World Wide Web then on, there has been an exponential growth in research and industrial activities related to the semantic representation of the knowledge aimed at enabling an efficient extraction, sharing, integration and reuse of the information, especially in distributed environments. The achievement of the semantic representation of the information in a given domain through Semantic.

Knowledge Management (SKM) methods has led benefits in several applications such as health care, e- learning, energy management, and so on. However, knowledge representation domains are intrinsically characterized by the vagueness and uncertainty of information. Therefore, computational Intelligence methodologies can enrich and improve SKM methods and open new positive scenarios for designing innovative SKM architectures.

According to these research activities, the aim of this Special Session is to provide a forum for both academia and industry to disseminate and discuss recent and significant research efforts in evolutionary, fuzzy and neural methods for designing and implementing new SKM architectures. The scope of this Special Session covers, but is not limited to:

– Computational Intelligence for semantic representation of knowledge;
– Computational Intelligence for knowledge discovery and information retrieval;
– Computational Intelligence for ontology integration and meta-matching;
– Computational Intelligence for query processing and web search engines;
– Computational Intelligence for semantic storage and web-scale reasoning;
– Computational Intelligence for semantic tagging of texts or multimedia materials;
– Computational Intelligence techniques for semantic Web system supporting e-learning, semantic-based reputation systems, and semantic based applications in general.
– Research on or exploitation of Fuzzy ontologies.

CDSS-22 IEEE CIS Computational Finance and Economics Technical Committee

Organisers: Alex Lipton, Nicolas Courtois, Jon Matonis, Nikola Kasabov, Antoaneta Serguieva.

Website:http://www.ieee-cifer.org

Aim and Scope

The blockchain emerged as a novel distributed consensus scheme that allows transactions, and any other data, to be securely stored and verified in a decentralized way. Considered by some as revolutionary as the Internet, the blockchain has the potential to underpin concepts, frameworks, regulations, and economics. The nascent field of blockchain research is highly interdisciplinary, and has the potential for fascinating research projects and results, sitting at the intersection of computer science, cryptography, economics, engineering, finance, law, mathematics, and politics. Many technical challenges arise with the rapid development of distributed ledger technologies. There is a great interest in applying blockchain to different application scenarios and in solving complex problems. This technology also offers superb opportunities to support the transformation of business models.

This special session aims to provide a forum for researchers in this area to carefully analyze current systems or propose new ones, in order to create a scientific background for a solid development of new blockchain technology systems.

 

Track 1. Theoretical and Empirical Studies and Engineering Applications

Suggested topics include but are not limited to:
  • Adoption and transition dynamics
  • Blockchain-enabled services
  • Blockchain protocols and extensions
  • Case studies (of adoption, attacks, forks, scams, …)
  • Economic and monetary aspects
  • Forensics and monitoring
  • Fraud detection and financial crime prevention
  • Game theoretic analysis of blockchain protocols
  • Legal, ethical and societal aspects
  • P2P network analysis
  • Permissioned and permissionless blockchains
  • Privacy and anonymity-enhancing techniques
  • Proof-of-work, -stake, -burn, and virtual mining
  • Real-world measurements and metrics
  • Regulation and law enforcement
  • Scalability solutions for blockchain systems
  • Security and cryptography engineering questions relevant to blockchains
  • Smart contracts
  • Transaction graph analysis

Track 2. Initial Token Offering / Initial Coin Offering Solutions

 

We invite submissions of short research or/and engineering papers pertaining to the development of solutions and/or business and software ecosystems in relation to or funded by an Initial Coin Offering (ICO), or an Initial Token Offering (ITO) operation. We would expect that these ITO / ICO operations are either ongoing or recently completed or publicly announced (on a company web site, through press releases, recent white papers, etc.). The papers should begin with an introduction, followed by a study of the state of the art and competing research/solutions. It should then contain a novel or original part, a conclusion which makes it clear what are the contributions and achievements, and a list of references. Authors are expected to be not-anonymous, and for each author, a very short 3-line biography should be inserted before the bibliography section. The biographies should emphasize academic, engineering, research and software development qualifications, and professional experience. The papers should be self-contained, with a clear and well defined scope. These ITO / ICO papers are expected to attempt to demonstrate clearly that the presented solutions correspond to the ITO / ICO operations and help to achieve desirable outcomes for the business or software ecosystem being built or developed.

We welcome all sorts of:

  • Feasibility studies
  • Detailed design / engineering / crypto / protocol / architecture white papers
  • Performance usability and security evaluations
  • Software / hardware /cryptographic / other solutions to distributed storage
  • Distributed execution environments
  • Distributed trust and distributed business systems
  • Efficient / optimized implementations
  • In-depth comparative studies
  • Discussions of pros and cons of how blockchain can be used to solve business problems
  • Studies on intellectual property pertaining to practical applications of distributed ledger technology
  • In-depth analyses of existing or future token ecosystems with token economics, transaction activity, etc.

 

CDSS-23 Computational Intelligence for Sensing and Predicting Human Mental and Medical State (CI-SEPHUMMES)

ORGANIZERS:
Primary organizer and corresponding contact:

Jim Torresen, Professor, Department of Informatics
University of Oslo, Norway,
E-mail: jimtoer@iDi.uio.no

Website: https://folk.uio.no/jimtoer/

Co-organizers:
Enrique Garcia Ceja, Postdoctoral Fellow, Department of Informatics, University of Oslo, Norway,
E-mail: enriqug@iDi.uio.no

Dante Barone, Professor, Informatics Institute, Federal University of Rio Grande do Sul, RS, Brazil
E-mail: barone@inf.ufrgs.br

Special session Web page: https://sites.google.com/site/wcci2018sephummes

Summary

Computational intelligence techniques have shown to be effective in classifying human mental and medical state. This is useful for a number of applications including within mental health treatment technology and systems for monitoring and notifying about irregular medical state of elderly people living alone at home. At the same time, it is interesting to consider applying methods across the different application areas which we would like to contribute to through this special session covering two different application domains. Many different kinds of sensing technologies are relevant including sensors available in smartphones, sensor watches and ambient sensors. The latter can be applied either as Dixed mount room sensors or equipped on a moveable robot companion. Other user features like speech and smartphones usage can be relevant to apply for improving prediction accuracy. There is also new compact radar technology including ultra-wideband that are able to sense medical condition remotely. A challenge of the application domain is that data is mostly of private nature and schemes for protecting privacy are important and partly dependent on the type of sensors used.

The number of elderly people living at home is increasing, and this trend is expected to continue since the proportion of elderly people in the world is increasing. Further, the ental health challenges in our society are increasing. Thus, there is a need for technology that can support these user groups. This can potentially make the health care services more effective with reduced recovery time within mental health and making elderly in independent living get better support from caregivers. This special session will be organized for sharing knowledge about technological opportunities and challenges within the addressed domains as well be open to work addressing ethical considerations.

Scope and Topics

The aim of the special session is to provide a forum to disseminate and discuss recent and signiDicant research within applying computation intelligence to classify, model and predict future human mental and medical state. We invite interested authors to submit their original and unpublished work to this special session.

Topics of interest within the given application domain for the special session include (but are not limited to):

• Ambient assisted living
• Ambient sensor systems
• Contributions and Applications of Computational Neuroscience
• Data analytics and visualization
• Emotion detection
• Medical state classiDication and forecasting
• Mental disorders state classiDication and forecasting
• Mobile sensing
• Multi-sensor fusion
• On-body sensor systems
• Privacy in human monitoring
• Remote sensing and monitoring
• Robot companion sensing
• Sensitive data collection and storage
• Sensor networks
• Smart home technologies
• Temporal data analysis
• Time series modeling and forecasting

Potential contributors:

We have recently submitted a survey paper to the Pervasive and Mobile Computing journal: “Mental States Monitoring with Multimodal Sensing and Machine Learning: A Survey” containing 140 references. Another paper named: “Ambient Sensors for Elderly Care and Independent Living: A Survey” is soon ready for journal submission (contains about 150 references). The survey papers are available on request. Authors of the referenced papers would be relevant to invite for submitting work to the special session in addition to acting as reviewers for submitted papers. Mailing lists are also relevant, e.g.: Google Machine Learning News and announcements@lists.artist-embedded.org.

 

CDSS-24 Knowledge Discovery in the Internet of Things (KDIOT)Organizers:

Profa. Priscila Machado Vieira Lima, Prof. Claudio Miceli de Farias, Profa. Flávia Coimbra Delicato
Emails: priscilamvl@gmail.com, claudiofarias@nce.ufrj.br, fdelicato@gmail.com
Affiliations: Federal University of Rio de Janeiro, Brazil
All enquiries about the workshop should be sent to Prof. Claudio Miceli de Farias
(claudiofarias@nce.ufrj.br)

Website: kdiot2018.nce.ufrj.br

 

Scope
The recent technological advances in computer and communication technologies have been fostering an enormous growth in the number of smart objects available for usage. The integration of these smart objects into the Internet originated the concept of Internet of Things (IoT). The IoT vision advocates a world of interconnected objects, capable of being identified, addressed, controlled, and accessed via the Internet. Such objects can communicate with each other, with other virtual resources available on the web, with information systems and human users. IoT
applications involve interactions among several heterogeneous devices, most of them directly interacting with their physical surroundings. The instrumentation of the physical world will revolutionize the way the human being perceives and interacts with the environment. The real- time monitoring of a wide range of parameters, ranging from vital signs to the presence of gases in the atmosphere, can be exploited to build a myriad of applications, whose scope and purpose will be limited only by human imagination.

We can argue that, despite the apparent focus on the interconnected things, IoT paradigm is not about the object themselves, but about data, as its main value is the knowledge produced as a direct result of the data that can be captured from these objects. Such high-level, value-added knowledge can drive new business and operations. Therefore, as core enablers of the high potential to be exploited in IoT are the mechanisms to process the high volume of data from the sensors and an effective and efficient way, so that producing and extracting valuable knowledge from them.

New challenges emerge in this scenario as well as several opportunities to be exploited. In this context, techniques to promote knowledge discovery from the huge amount of sensing data are required to fully exploit the potential usage of the networked IoT devices. Knowledge Discovery can be achieved through current developments of Artificial Neural Networks, such as Convolutional Networks, Deep Learning, Weightless Neural Networks among others, or by adopting other paradigms as Data and Knowledge Fusion, Fuzzy and Evolutionary Computing. Employment of such knowledge discovery techniques areuseful to reveal trends in the sampled data, uncover new patterns of monitored variables, make predictions, thus improving decision making process, reducing decisions response times, and enabling more intelligent and immediate situation awareness.

Aim:

The goal of this Special Session (SS) is to present and discuss the recent advances in the interdisciplinary Knowledge Discovery research area applied to the domain of the Internet of Things (IoT). We aim to bring together specialists from academia and industry in different fields to discuss further developments and trends in the Artificial Neural Networks, Fuzzy systems, Data Fusion and evolutionary computing when employed to solve issues and leverage the IoT paradigm. We will take a data-centric perspective and discuss recent trends and approaches for managing and analyzing data, in order to bring up relevant information and produce useful knowledge from the myriad of raw data generated by the “things”.
Topics appropriate for this SS include (but are not necessarily limited to):

  • Data collection and abstractions in IoT
  • Data mining for IoT
  • Data and Knowledge Discovery in IoT
  • Deep Learning in IoT
  • Distributed neural networks for IoT
  • Machine learning for IoT
  • Data streams analysis in IoT
  • Weightless Neural Networks in IoT
  • Dynamic analysis in IoT
  • Probabilistic reasoning in IoT
  • Decision systems in IoT
  • Fuzzy systems in IoT
  • Data Fusion in IoT
  • Convolutional Neural Networks for IoT
  • Recurrent Neural Networks in IoT
CDSS-25 Computational Intelligence in Autonomous Vehicles, Driverless Car and Advanced Vehicle Systems

 

Organizers: Session Chair: Yi Lu Murphey, Professor and Associate Dean for Graduate Education and Research, College of Engineering and Computer Science, University of Michigan-Dearborn, Dearborn, Michigan 48128, USA, yilu@umich.edu

 

Special session objectives and topics

 

Computational Intelligence is playing a critical role in building autonomous vehicles, driverless car and advanced driver assistance systems (ADAS). Automotive industry is making big strides in building autonomous vehicles at all levels. Automakers are joining with tech giants such as Google, Uber, Intel, and high profile start-ups to harness the technological advances, such as advanced sensors, artificial intelligence, cloud computation, machine learning, connected vehicles, that will power next-generation autonomous vehicle. Computational intelligence has also been applied to building a host of intelligent devices in conventional cars including airbag control, unwelcome intrusion detection, collision detection, warning and avoidance, power management and navigation, and driver state detection and driver intent prediction.

 

The objective of this special session is to provide a forum for researchers and practitioners to present advanced research in computational intelligence applied to building all types and levels of autonomy in advanced vehicle systems. This session seeks contributions on the latest developments and emerging research related, but are not limited to:

• Cloud computing and data security in connected and automated vehicles
• Computational intelligence in building personalized driving and traveling support systems
• Computational intelligence techniques for driver state detection and monitoring, driver intent prediction, driver assistance and automated vehicle systems
• Computational intelligence in vehicle fault diagnostics and health monitoring
• Computer vision and machine learning technologies developed for V2V, V2P and V2X collision detection and avoidance
• Deep learning algorithms in automated driving systems and driverless cars
• Machine learning algorithms for driving route prediction, driver speed prediction, and
optimum path planning
• Machine learning algorithm for multivariate time series analysis
• Machine learning algorithms for vehicle energy management and optimization in hybrid vehicles
• Machine learning from big data with applications to ADAS and driverless cars
• Object recognitions such as pedestrian, bicyclist, motorcyclist, traffic sign detection and recognition
• Perception in driverless cars using integrated sensors of cameras, radar, high-performance GPS, Light Detection and Ranging (LIDAR)
• Reinforcement learning, neural dynamics and adaptive control in vehicle systems

CDSS-26 Open Datasets for Computational Intelligence

Organizers: Giovanni Acampora (University of Naples Federico II, Italy giovanni.acampora@unina.it), Genoveffa Tortora (University of Salerno, Italy tortora@unisa.it) and Autilia Vitiello (University of Salerno, Italy avitiello@unisa.it)

Aim and Scope

Reproducibility is a crucial requisite for computational intelligence studies. In order to reproduce a study, a researcher needs full information about the datasets involved in the study and the form and the order as they are used. Indeed, often, researchers do not exploit a whole dataset, but, their experiments involve a selection of records or a projection of properties based on defined parameters. Unfortunately, the experience shows that researchers rarely give open access to the used datasets and sufficient details about how they are managed. The aim of this special session is to provide a forum for researchers to exchange information about datasets useful for carrying out reproducible studies in computational intelligence. Those who have created a new dataset that can be relevant to the computational intelligence community should consider submitting its description (including motivation, design, and usage, as well as utility to the community) to this special session. The scope of this special session covers, but is not limited to:

  • New Datasets in Bio-medicine, Power systems, Security and Privacy, Image processing, Commerce and Marketing, Smart environments, Crime investigations and so on;
  • Analysis of the current state of reproducibility in computational intelligence studies;
  • Tools to help the increase of the reproducibility.