i-Sense Dissemination



Publications

The scientific results and technological innovations resulting from the research undertaken in the project, International Journals papers, International Conference publications, Books and book chapters and Tutorials, are presented in the publications section.

The abstracts of the papers are available online and the full-text articles may be requested to authors contacting them by mail (see team section for each author contact).

 

Awards

EU funded project iSense wins "Best Paper Award 2014"

Following a competitive selection from over 1.500 papers the EU funded project iSense received the Best Paper Award 2014 from the international journal Building and Environment granted annually by the publisher Elsevier. The awarded paper is entitled “Contaminant event monitoring in multi-zone buildings using the state-space method” and investigates the problem of monitoring contaminants in intelligent buildings.

 

Open source core results

Sharing the software implementation of some core results, complementing what we published in top international journals, is one of iSense project dissemination policies. As such we offer, firstly, as open and free software the core of the algorithms developed within iSense around which you can build your own embedded solution, as well as advance techniques and methods. Moreover, we are committed to maintaining a public repository of i-Sense benchmarks that may be freely downloaded for non-commercial research and educational purposes. 

This open source policy is aligned with the digital open access guidelines to EU funded research oriented towards the dissemination of results and outcomes.

Please visit the Open Library section for more details and to download the software package, and the Benchmark section where you can download the i-Sense Benchmark datasets.


All software, datasets and benchmarks in this website are published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.

Creative Commons License

 

i-Sense Workshop


The 1st International Workshop on Learning stratEgies and dAta Processing in nonStationary environments (LEAPS 2013) was organized during the 9th International Conference on Artifical Intelligence Applications and Innovations (IAIA 2013) that was held on september, 30 - october, 2 2013 in Paphos - Cyprus.

Download the workshop flyer


Workshop Aims


Most machine learning techniques assume, either explicitly or implicitly, that the data-generating process is stationary. This assumption guarantees that the model learnt during the initial training phase remains valid over time and that its performance is in line with our expectations. Unfortunately, this assumption does not truly hold in the real world representing, in many cases, a simplistic approximation of the reality.
 
Data from real-world scenarios are often affected by nonstationarities and, during operational life, their describing model (or distribution) diverges from the one that yielded the training set. Among the causes generating nonstationarity we mention natural (and unknown) evolutions of the data-generating process, faults/aging affecting sensing and processing devices and model bias introduced by a poor training set. Learning-based systems have to be up-to-date to be effective, thereby requiring adaptation mechanisms to deal with nonstationary environments.
 
In machine learning nonstationarity is referred to as concept drift and several techniques to detect and adapt to concept drift have been presented in different application domains e.g., fraud detection in electronic transactions, sensor networks, intelligent vehicles and recommender systems. Other relevant scenarios are classification systems designed to cope with concept drift, such as those addressing email/spam filtering, internet events log analysis, stock market forecasting, context-aware and ubiquitous computing.
 
The workshop focuses on intelligent solutions to analyze/process data acquired in nonstationary environments. Original contributions in the field of fault detection and diagnosis, as well as cognitive approaches for learning characteristics of the process to handle nonstationarity are particularly welcome.
 
We encourage submissions presenting novel theoretical, methodological or experimental results.

Topics

Papers must present original work or review the state-of-the-art in the following non-exhaustive list of topics:
 

  1. Computational Intelligent solution for Fault Detection/ Isolation/ Identification;
  2. Change-Detection Tests (or Novelty-Detection Tests)
  3. Change Detection exploiting contextual information
  4. Adaptive Classifiers for Concept Drift
  5. Concepts Drift and Recurring Concept management
  6. Embedded systems implementing computational intelligence techniques to achieve intelligent behavior in nonstationary environments;
  7. Adaptive solutions to operate in evolving/faulty environments;
  8. Intelligent embedded systems for applications such as:
    1. intelligent buildings,
    2. robotics,
    3. homeland security,
    4. environmental monitoring,
    5. sensor networks,
    6. water distribution networks,
    7. Intrusion detection in computer networks.
  9. Application domains where data are affected by concept drift

Workshop Organizers:

  1. Giacomo Boracchi (Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy)
  2. Manuel Roveri (Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy)

Technical Program Committee:

  1. Rami Abielmona (University of Ottawa, Canada)
  2. Haibo He (University of Rhode Island, US)
  3. Vasso Reppa (KIOS Research Center for Intelligent Systems and Networks, Cyprus)
  4. Michalis P. Michaelides (Department of Electrical Engineering and Information Technologies, Cyprus University of Technlolgy)
  5. Stefano Zanero (Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy) 
  6. Peter Tino (School of Computer Science, University of Birmingham, UK)
  7. Vicenç Puig (Universitat Politècnica de Catalunya, Department of Automatic Control, Spain)
  8. Maurizio Bocca (Electrical and Computer Engineering Department, University of Utah)
  9. Vincent Lemaire (Orange Labs, France) 
  10. Alessandro Lazaric (INRIA Lille, France)
  11. Daniele Caltabiano (STMicroelectronics, Milano Italy)
  12. Leandro L. Minku (School of Computer Science, The University of Birmingham, UK)

Publication

Submitted papers will be refereed for quality, correctness, originality, and relevance. Notification and reviews will be communicated via email. Accepted papers will be presented at the workshop and published in the Proceedings of the main event (by Springer). They will also be considered for potential publication in the Special Issues of the Conference.

Registration fees and benefits for the workshops’ participants are exactly identical with the ones of the main AIAI 2013 event.

Visit the workshop website



Youtube Channel


 

Follow us on i-Sense Youtube Channel where are posted videos related to the project activities, the project results with illustrations of the main public and open-source methodologies, matlab packages and robotic platform developed whithin our project.


In this way you can explore the project in a more interactive way, and know the project team.
I-sense is social !

 

 


Flyer and Newsletter


A flyer was published in march 2011 in order to present, in a preliminary step, the main activities and objectives of the project.

The first issue of the annual newsletter was published in January 2012 presenting the main results reached by the activities of the project. The newsletter is also available in the project website.

The second issue of the annual newsletter was released in January 2013 with the second year main results of the project.

The third issue of the annual newsletter was released in January 2014 with the main public results of the projects and activities updates.

 

 

International technical dissemination events

 

Related International Conference


11th International Conference on DIAGNOSTICS OF PROCESSES AND SYSTEMS
8–11 September 2013, Łagów Lubuski, Poland

www.dps2013.uz.zgora.pl

The International Conference on Diagnostics of Processes and Systems
(DPS) has been organized every two years since 1996 by the Warsaw
University of Technology, the University of Zielona Góra and the Gdańsk
University of Technology. The conference provides an excellent forum for
exchanging knowledge and experience, and for sharing solutions within
the academic and industrial community. The conference is addressed to
both experts and young researchers, who can introduce their newest
research achievements to the audience of leading scientists in technical
diagnostics. The subject matter of the conference is replying to the
expectations and demands of research and industrial centres for modern
and safe diagnostics systems, process monitoring systems and expert
systems.

KEY TOPICS
Fault detection, isolation and identification
Fault-tolerant control systems
Industrial applications
Medical diagnostics

(All topics of interest are available on the conference website)

CONTRIBUTIONS & PROCEEDINGS
Prospective participants are invited to submit their works in English or
in Polish. The submitted papers will undergo a peer review process.
Selected works will be published by Springer-Verlag in a collective
monograph while others will be recommended for publication in the
following journals: Pomiary, Automatyka, Kontrola; International Journal
of Applied Mathematics and Computer Science; Pomiary, Automatyka, Robotyka.

SCHEDULE
10 March 2013: Deadline for submission of full-length papers
14 April 2013: Paper acceptance notification
5 May 2013: Paper final versions due in electronic form + payment of the
conference fee
8–11 September 2013: Conference meeting

PROGRAMME COMMITTEE
Józef Korbicz (Zielona Góra) – Chair
Jan M. Kościelny (Warsaw) – Vice-Chair
Zdzisław Kowalczuk (Gdańsk) – Vice-Chair

ORGANISING COMMITTEE
Marek Kowal (Zielona Góra) – Chair
Krzysztof Patan (Zielona Góra) – Vice-Chair

CONTACT
University of Zielona Góra
Institute of Control & Computation Engineering
ul. Ogrodowa 3b
65-246 Zielona Góra, Poland

phone: +48 683282422
fax: +48 683284751
email: dps2013@uz.zgora.pl


Workshop


3rd KIOS Workshop , 25th June 2013, organized by UCY & KIOS – Nicosia, Cyprus

One of the key challenges in Information and Communication Technologies (ICT) is the integration of networked computing with physical systems and processes. In this framework, embedded computers add to physical systems significant new capabilities which are further extended when computers are networked and communicate with each other. Such integration is leading to a new generation of devices and intelligent systems that can adapt to malfunctions, cooperate and evolve during operation to become more efficient, fault tolerant and trustworthy.

This exciting and challenging new research area is the main focus of the KIOS Research Center, both in terms of fundamental (basic) research as well as in the application of such systems in various contexts. On one hand, there is a wide spectrum of relevant applications, ranging from small devices for healthcare delivery, to large-scale critical infrastructure systems such as the electric power grid. On the other hand, the new system capabilities enable emerging new behaviors that necessitate rigorous investigation in order to be better understood and applied in practical settings. Successful solutions in the area of intelligent networked embedded systems require the harmonious integration of hardware and embedded software/algorithmic components. Therefore, the key objective of the KIOS Center is to blend theory with applications and integrate embedded software/algorithms with hardware devices.

Recognizing that research in intelligent networked embedded systems requires multidisciplinary expertise, the KIOS research team specializes in a wide range of fundamental areas, such as systems and control, distributed systems and algorithms, graph theory and optimization, computational intelligence, electronic hardware design, testing and diagnosis, as well as in the application of these techniques to healthcare delivery, power systems, communication networks, water distribution networks and transportation systems.

The 3rd KIOS Workshop will include keynote presentations from internationally accomplished experts and pioneers, followed by poster sessions demonstrating specific research activity covering a variety of research fields and spanning the range of expertise of the Center.

Workshop details at the link

 

Special issues


Special issue on Learning in Nonstationary and Evolving Environments
IEEE Transctions on Neural Networks and Learning Systems

A special issue that discusses the state-of-the-art and latest results on detecting and adapting to changes in underlying data distributions is very timely. We invite original and unpublished contributions in all areas relevant to learning in a changing environment.

Papers must present original work or review the state-of-the-art in the following non-exhaustive list of topics:
• Learning in non-stationary, drifting or dynamic environments
• Adaptive learning in a missing, faulty, limited or unbalanced data context
• Incremental, lifelong and cumulative learning from nonstationary data
• Faults, changes or anomaly detection in data streams
• Domain adaptation
• Data mining from streams of data
• Architectures, techniques and algorithms for learning in such environments
· Applications requiring learning in dynamic and nonstationary environments

Important dates
15 April 2012 – Deadline for manuscript submission
15 August 2012 – Notification of authors
15 September 2012 – Deadline for submission of revised manuscripts
30 September 2012 – Final decision
January/February 2013 – Special issue publication in the IEEE TNNLS
The full call for papers can be found at the link

 


Special Sessions


Special session on Computational Intelligence for Cognitive Fault Diagnosis at the IEEE World Congress on Computational Intelligence  (WCCI14), Beijing, China, July 6-11, 2014  

The proliferation of wireless sensor networks, machine-to-machine communication and Internet of Things (IoT) has made it possible to build large scale, distributed autonomous systems.  These systems which are also referred to as Cyber-Physical Systems, typically involve a large number of individual components such as sensors and actuators, communication links as well as complex software and controllers.  During normal operation, these components generate large volumes of data that need to be processed to extract useful information and to make decisions.  At any point in time, it is likely that one or more components may fail and as a result the generated data may be inconsistent which may lead to wrong decisions.  The aim of this special session is to present recent advances in computational intelligence that can used in fault diagnosis. 

Topics
Topics include but are not limited to

  • computational intelligence for detecting faulty components
  • computational intelligence detecting and correcting inconsistent data
  • computational intelligence for fault detection/identification/isolation
  • learning and adaptation for fault diagnosis
  • learning from imbalanced data
  • fault diagnosis in time evolving environments   
  • change detection test
  • applications

Organizers
M. Polycarpou (University of Cyprus, Electrical and Computer Engineering Department, Cyprus)
C.G. Panayiotou (University of Cyprus, Electrical and Computer Engineering Department, Cyprus)
C. Alippi (Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy)

 


Special Session on Concept Drift, Domain Adaptation & Learning in Dynamic Environments at the 2014 International Joint Conference on Neural Networks (IJCNN 2014), Beijing, China, July 6-11, 2014  

One of the fundamental goals in computational intelligence is to achieve brain like intelligence, a remarkable property of which is the ability to incrementally learn from noisy and incomplete data, and ability to adapt to changing environments. The ability of a computational model to learn under various environments have been well-researched with promising progress, but a vast majority of these efforts make two fundamental assumptions: i) there is sufficient and representative training data; and ii) such data are drawn from a fixed – albeit unknown – distribution. Alas, these assumptions often do not hold in many applications of practical importance. Recent efforts towards incremental and online learning may allow us to relax the “sufficiency” requirement by continuously updating a model to learn from small batches of data. Yet, in many incremental learning algorithms the second assumption still remains: the data that may incrementally become available are still drawn from a fixed – but yet unknown – distribution.  More recently, other incremental approaches – such as concept drift and domain adaptation algorithms - have attempted to remove this assumption, by allowing a stream or batches of data whose underlying distribution change over time. These early approaches, however, make other assumptions such as restricting the type of change in the distribution, are primarily of heuristic in nature with many free parameters requiring fine-tuning, and have not been evaluated on large scale real-world applications.

Considering that our ultimate goal in computational intelligence is to attain brain-like intelligence, and that the plasticity of brain-like intelligence can, and routinely does, learn incrementally and in nonstationary dynamic environments, the need for a framework for learning from – and adapting to – a nonstationary environment is very real. Combined with a growing number of real-world applications that can immediately benefit from such algorithms, such as learning from financial data, climate data, etc., it is clear that there is much work to be done for solving the nonstationary learning problems. 

The proposed session on concept drift / nonstationary learning has three main goals:
1.   Introduce the problem of concept drift, domain adaptation, nonstationary learning and more generally dynamic learning, and its associated issues, to the greater neural network & computational intelligence community who may not have been familiar with the topic, yet would like to familiarize themselves with the most recent approaches for solving this problem;
2.   Provide a forum for researchers who have been actively working in this area to exchange new ideas with each other, as well as with the rest of the neural network & computational intelligence community
3.   Present recent approaches to learning in dynamically changing environments from two perspectives: first, the more traditional and theoretical view of machine learning and computational intelligence; and second, from the more practical and application oriented view of using neural networks.

Topics

The scope of the proposed session includes, but not limited to

  • Incremental learning / lifelong learning / cumulative learning
  • Fault, change or anomaly detection algorithms
  • Data mining from streams of data
  • Domain adaptation, dataset shift, covariance shift
  • Learning in non-stationary environments / concept –drift environments / dynamic environments
  • Architectures / techniques / algorithms for learning in such environments
  • Applications that call for incremental learning or learning in nonstationary environments
  • Adaptive classifiers able to cope with concept drift and recurring concepts
  • Development of test-sets / benchmarks for evaluating algorithms learning in such environments
  • Issues relevant to above mentioned or related fields

Keywords
Concept drift, nonstationary environment, domain adaptation, incremental learning, change/anomaly detection, data streams

Organizers

Robi Polikar( Rowan University, Department of Electrical and Computer Engineering, Glassboro, USA)
Manuel Roveri (Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy)
Giacomo Boracchi (Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy)

Program Committee

  • Robi Polikar, Rowan University, NJ, USA
  • Cesare Alippi, Politecnico Milano, Italy
  • Haibo He, University of Rhode Island, RI, USA
  • Gavin Brown, University of Manchester, England, UK
  • Ludmilla Kuncheva, University of Bangor, Wales, UK
  • Manuel Roveri, Politecnico Milano, Italy
  • Giacomo Boracchi, Politecnico Milano, Italy
  • Gregory Ditzler, Drexel University, PA, USA
  • Indre Žliobaite, Bournemouth University, England, UK
  • Shengxiang Yang, Brunel University, England, UK
  • Yaochu Jin, University of Surrey, England, UK
  • Mykola Pechenizkiy , Eindhoven University of Technology, the Netherlands
  • Nikola Kasabov, Auckland University of Technology, New Zealand

 

Special session on Intelligent Embedded Systems at the 2013 International Joint Conference on Neural Networks (IJCNN 2013), Dallas, Texas, USA, , August 4-9, 2013

Intelligent embedded systems, i.e., embedded processing systems with sensors, actuators and intelligent computing ability, permeate our daily life. This special session follows the successful editions of IJCNN 2012 and 2011, aiming at disseminating recent achievements in computational intelligence and machine learning to provide embedded systems with intelligent capabilities.
The session aspire at building a bridge between academic and industrial research, as well as among researchers working in different fields with the specific purpose of designing embedded systems able to adapt and interact with evolving environments.

Submissions addressing intelligent fault-diagnosis systems are particularly encouraged, as well as intelligent solutions for fault detection, identification, isolation and accommodation.

Topics
Papers must present original work or review the state-of-the-art in the following non-exhaustive list of topics:

  • embedded systems implementing computational intelligence and machine learning techniques to achieve intelligent behavior;
  • intelligent solutions for fault diagnosis (detection/isolation/identification);
  • adaptive solutions to operate in evolving/faulty environments;
  • intelligent sensors;
  • intelligent wired and wireless sensor networks;
  • computational intelligence methodologies and techniques for adaptive measurement or processing systems;
  • intelligent embedded systems for applications such as: intelligent buildings, robotics, homeland security, environmental monitoring, sensor networks, water distribution networks.

Keywords
Computational intelligence, embedded systems, machine learning, fault detection, fault isolation, fault diagnosis, intelligent applications, smart sensors, intelligent sensor networks.

Program Committee

  • Manuel Roveri (Politecnico di Milano, Dipartimento di Elettronica e Informazione, Italy)
  • Giacomo Boracchi (Politecnico di Milano, Dipartimento di Elettronica e Informazione, Italy)
  • Cesare Alippi (Politecnico di Milano, Dipartimento di Elettronica e Informazione, Italy)
  • Vasso Reppa (KIOS Research Center for Intelligent Systems and Networks, Cyprus)
  • Peter Tino (School of Computer Science, University of Birmingham, UK)
  • Vicenç Puig (Universitat Politècnica de Catalunya, Department of Automatic Control, Spain)
  • Daniele Caltabiano (STMicroelectronics, Milano Italy)
  • Leandro L. Minku (School of Computer Science, The University of Birmingham, UK)
  • Maurizio Bocca (Electrical and Computer Engineering Department, University of Utah)
  • Vincent Lemaire (Orange Labs, France)
  • Alessandro Foi (Department of Signal Processing, Tampere University of Technology)
  • Alessandro Lazaric (INRIA Lille, France)

 

Special session on Data Regularisation, Fault and Anomaly Detection, Isolation and Mitigation
at the 2012 IEEE World Congress on Computational Intelligence (IEEE WCCI 2012), Brisbane, Australia, June 10-15, 2012

The aim of this special session is to foster research on robust learning/control/monitoring in such challenging scenarios. It will be a platform to exchange ideas on novel approaches to fault tolerant modeling, monitoring and/or control that can learn characteristics of the monitored environment and adapt their behaviour as well as successfully deal with missing or perturbed data. Papers must present original work or review the state-of-the-art in the following non-exhaustive list of topics:

  • Data regularization and validation
  • Unsupervised, semi-supervised and supervised machine learning techniques for signal compensation and missing data reconstruction
  • Online learning and imbalanced learning
  • Clustering techniques
  • Cognitive Fault Diagnosis
  • Fault Tolerant Control: Passive Fault Tolerant Control, Active Fault Tolerant Control and Fault Tolerant Model Predictive Control
  • Fault Modelling
  • Centralized, hierarchical and distributed architecture
  • Exploiting spatial and temporal dependencies and redundancies in developing robust monitoring and/or control systems
  • Accommodation and mitigation of new "never seen before" faults

Paper submission: 18 January 2012
Final paper submission deadline: April 2, 2012
Early registration: April 2, 2012

The call for papers can be found at the link

 

Press release

  • Television news coverage of the press conference for the i-Sense project in Cyprus, (video, 26/01/2011)
  • Press coverage for the iSense Project (article in kathimerini.gr, article in Politis Newspaper, article in Haravgi Newpaper, 27/01/2011)
  • Article in the Newspaper “Phileleftheros” (article: Huge steps in research )
  • European news article on the iSense project coordinated by KIOS (article)

 

Invited talks and plenary speechs

  • Keynote Plenary Speaker at the 4th International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO 2013), 18-21 December 2013, Chennai, India, on “Distributed Fault Diagnosis of Cyber-Physical Systems”, by Prof. Marios Polycarpou.
  • Invited keynote speaker at the 5th International Conference on Awareness Science and Technology (iCAST 2013), Aizu-Wakamatsu, Japan, 2-4 November 2013, by Prof. Xin Yao, on “Self-awareness and Self-expression in Computing”.
    Abstract

  • Keynote Plenary Speaker at the 2nd IEEE Hellenic Student Branch and GOLD Congress (IEEE HSBC’2013), 1-3 November 2013, Nicosia, Cyprus, on “Cognitive Fault Diagnosis in Big Data Environments”, by Prof. Marios Polycarpou.
  • Keynote Plenary Speaker at the 14th Conference on Engineering Applications of Neural Networks (EANN’2013), 13-16 September 2013, Halkidiki, Greece, on “Distributed Sensor Fault Diagnosis in Big Data Environments”, by Prof. Marios Polycarpou.
    Abstract

  • Invited plenary speaker at the 1st BRICS Countries and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI& CBIC 2013), Recife ('Porto de Galinhas' Beach), Brazil, 8-11 September 2013, by Prof. Xin Yao, on “Cooperative Co-evolution in Tackling Large Optimisation Problems”.
    Abstract

  • Keynote Plenary Speaker at the 2013 IEEE Multi-Conference on Systems and Control (MSC’2013), 28-30 August 2013, Hyderabad, India, on “Sensor Fault Detection and Isolation in Big Data Environments”, by Prof. Marios Polycarpou.
    Abstract
  • Invited keynote speaker at the 5th Symposium on Search-Based Software Engineering (SSBSE'13), St. Petersburg, Russia, 24-26 August 2013, by Prof. Xin Yao, on “Multi-objective Approaches to Search-Based Software Engineering”.
    Abstract

  • Invited keynote speaker at the 2013 Genetic and Evolutionary Computation Conference (GECCO 2013), Amsterdam, The Netherlands, 6-10 July 2013, by Prof. Xin Yao, on “Challenges and Opportunities in Dynamic Optimisation”.
    Abstract

  • Keynote speaker at the 10th International Symposium Neural Networks (ISNN2013), Dalian, China, July 4-6, 2013, on "Cognitive Fault Detection in Sensor Networks", by Prof. Cesare Alippi.
    Abstract


  • Invited keynote speaker at the 2013 Unconventional Computation and Natural Computation Conference (UCNC 2013), Milano, Italy, 1-5 July 2013, by Prof. Xin Yao, on “Efficiency of Evolutionary Algorithms”.
    Abstract

  • Keynote speaker at the 2013 International Conference on Brain Inspired Cognitive Systems (BICS 2013), Beijing, China, June 9-11, 2013, on "Active Learning with a Just-In-Time Strategy", by Prof. Cesare Alippi.
    Abstract


  • Invited keynote speaker at the Second Chinese Search Based Software Engineering Workshop, Dalian, China, 8-9 June 2013, by Prof. Xin Yao, on “Recent Advances in Computational Intelligence Approaches for Software Engineering”.
    Abstract

  • Invited Keynote Speaker at the 2013 IEEE Symposium Series on Computational Intelligence (SSCI’2013), 16-19 April 2013, Singapore, on “Multiple Sensor Fault Detection and Isolation of Complex Distributed Dynamical Systems”, by Prof. Marios Polycarpou.
    Video shared by the IEEE Computational Intelligence Education Center at the link
  • Invited keynote speaker at the Third IEEE International Conference on Information Science and Technology (ICIST 2013), Yangzhou, Jiangsu, China, 23-25 March 2013, by Prof. Xin Yao, on “Ensemble Approaches in Learning”.
    Abstract

  • Distinguished lecture by Prof. Jim Bezdek, “Every Picture Tells a Story - Visual Cluster Analysis”, 31st of October, 2012, organized by UCY - IEEE CIS & KIOS.
    Full text / Short Description


  • Invited talk at the 2011 Learning and Intelligent OptimizatioN (LION 5) Conference on 17-21 January 2011 in Rome, Italy, on "Evolving and Designing Neural Network Ensembles", by Prof. Xin Yao.
    Link

  • Keynote speaker at the 3rd International Conference on e-Business and Information System Security (EBISS2011) on 28-29 May 2011 in Wuhan, Hubei Province, China, on “Monitoring, Fault Tolerance and Control of Large-Scale Distributed Systems”, by Prof. Marios M. Polycarpou.
    Abstract

  • Keynote speaker at the 2011 The 2011 International Conference on Information Security and Intelligence Control (ISIC2011) on 13-15 August 2011 in Jilin, China, on “Intelligent Monitoring, Control and Security of Large-Scale Interconnected Dynamical Systems”, by Prof. Marios M. Polycarpou.

  • Keynote speaker at the 2011 International Conference on Intelligent Computing (ICIC2011) on 11-14 August 2011 in Zhengzhou, Henan Province, China, on “Intelligent Distributed Fault Diagnosis of Interconnected Dynamical Systems”, by Prof. Marios M. Polycarpou.

  • Invited plenary speaker at the 2011 International Conference on Neural Information Processing (ICONIP 2011), 14-17 November 2011, Shanghai, China, on "Evolving, Training and Designing Neural Network Ensembles", by Prof. Xin Yao.
    Abstract

  • Workshop speaker at the ICONIP'2011 Workshop on Recent Advances in Nature Inspired Computation and Its Applications, 14-17 November 2011, Hangzhou, China, on " Computational Intelligence and Fault Diagnosis:  iSense Project ", by Dr. Huanhuan Chen.