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IEEE CIS Newsletter, Issue 96, January 2021
 
 
 
 
 
 
Announcements
 
 
 
 

2021

 
 
 
 

Message From The President

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I hope that you had a pleasant holiday season despite the pandemic.

The year 2020 has been very difficult and we have learned to live in a virtual world. All major CIS conferences and other activities have been successfully held, thanks to the efforts of all organizers, although we missed the pleasure of meeting you all in person.

I sincerely wish you and your family a Happy New Year 2021 and I look forward to a dynamic scientific, technical and managerial life, as well as, a friendly network.

I hope to be able to meet you physically at one of our face-to-face events and even sooner virtually.

Take care of yourself and your loved ones.

Bernadette Bouchon-Meunier

 
 
 
 

Newly-Elected Members of the CIS AdCom

We are pleased to inform you that the newly-elected members of the CIS Adcom for the three-year term 1 January 2021 – 31 December 2023 are:

  • Piero Bonissone
  • Oscar Cordon
  • Pauline Haddow
  • Haibo He
  • Hisao Ishibuchi
 
 
 
 
Research Frontier
 
 
 
 

Modeling of Complex System Phenomena via Computing With Words in Fuzzy Cognitive Maps

paper planes in a line with one going atray behind a blue backgroundFuzzy cognitive maps (FCMs) play an important role in high-level reasoning but are limited in their ability to model complex systems with singularities. We are interested in systems that exhibit discontinuous behaviors as one or more of their internal node states approach a threshold. In a new approach to FCM dynamics, we define general classes of aggregation functions which “jump” to a boundary value when any input crosses a threshold, or when all inputs do. The threshold value is a context-dependent parameter which can be readily understood by subject matter experts. Aggregation functions are applied separately to positively and negatively causal antecedents to each node then combined to form the nodal state. This modeling is applied in Computing with Words (CWW) settings, in which link strengths and activation levels are elicited using vocabulary words represented by interval type-2 fuzzy membership functions. We illustrate the behaviors of these novel FCM systems in comparison with their nonsingularity versions. Read More

IEEE Transactions on Fuzzy Systems, Dec. 2020

 
 
 
 

Landscape-Aware Performance Prediction for Evolutionary Multiobjective Optimization


floating black box with white background We expose and contrast the impact of landscape characteristics on the performance of search heuristics for black-box multiobjective combinatorial optimization problems. A sound and concise summary of features characterizing the structure of an arbitrary problem instance is identified and related to the expected performance of global and local dominance-based multiobjective optimization algorithms. We provide a critical review of existing features tailored to multiobjective combinatorial optimization problems, and we propose additional ones that do not require any global knowledge from the landscape, making them suitable for large-size problem instances. Their intercorrelation and their association with algorithm performance are also analyzed. This allows us to assess the individual and the joint effect of problem features on algorithm performance, and to highlight the main difficulties encountered by such search heuristics. By providing effective tools for multiobjective landscape analysis, we highlight that multiple features are required to capture problem difficulty, and we provide further insights into the importance of ruggedness and multimodality to characterize multiobjective combinatorial landscapes. Read More


IEEE Transactions on Evolutionary Computation, Dec. 2020

 
 
 
 

Heterogeneous Domain Adaptation: An Unsupervised Approach


floating black box with white background Domain adaptation leverages the knowledge in one domain—the source domain—to improve learning efficiency in another domain—the target domain. Existing heterogeneous domain adaptation research is relatively well-progressed but only in situations where the target domain contains at least a few labeled instances. In contrast, heterogeneous domain adaptation with an unlabeled target domain has not been well-studied. To contribute to the research in this emerging field, this article presents: 1) an unsupervised knowledge transfer theorem that guarantees the correctness of transferring knowledge and 2) a principal angle-based metric to measure the distance between two pairs of domains: one pair comprises the original source and target domains and the other pair comprises two homogeneous representations of two domains. The theorem and the metric have been implemented in an innovative transfer model, called a Grassmann–linear monotonic maps–geodesic flow kernel (GLG), which is specifically designed for heterogeneous unsupervised domain adaptation (HeUDA). The linear monotonic maps (LMMs) meet the conditions of the theorem and are used to construct homogeneous representations of the heterogeneous domains. The metric shows the extent to which the homogeneous representations have preserved the information in the original source and target domains. By minimizing the proposed metric, the GLG model learns the homogeneous representations of heterogeneous domains and transfers knowledge through these learned representations via a geodesic flow kernel (GFK). To evaluate the model, five public data sets were reorganized into ten HeUDA tasks across three applications: cancer detection, the credit assessment, and text classification. The experiments demonstrate that the proposed model delivers superior performance over the existing baselines. Read More


EEE Transactions on Neural Networks and Learning Systems, Dec. 2020

 
 
 
 
Member Activities
 
 
 
 

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Webinar Speaker: Prof. Pau-Choo Chung (Julia)

Webinar Chair: Dr. Sansanee Auephanwiriyakul

Webinar Title:  Convolutional Networks for Medical Image Analysis: Its Past, Future, and Issues

Date and Time:  Tue, 12 Jan 2021, 9:00 PM - 10:00 PM EST

Registration URLhttps://attendee.gotowebinar.com/register/8765948123600554251  

Abstract: Recent advancement of image understanding with deep learning neural networks has brought great attraction to those in image analysis into the focus of deep learning networks. While researchers on video/image analysis have jumped on the bandwagon of deep learning networks, medical image analyzers would be the coming followers. The characteristics of medical images are extremely different from those of photos and video images. The application of medical image analysis is also much more critical. For achieving the best effectiveness and feasibility of medical image analysis with deep learning approaches, several issues have to be considered. In this talk we will give a brief overview of the development of neural networks for medical image analysis in the past and the future trends with deep learning. Several issues in regards to the data preparation, techniques, and clinic applications will also be discussed.

Biography: Pau-Choo (Julia) Chung (S’89-M’91-SM’02-F’08) received a Ph.D. degree in electrical engineering from Texas Tech University, USA, in 1991. She then joined the Department of Electrical Engineering, National Cheng Kung University (NCKU), Taiwan, in 1991 and became a full professor in 1996. She served as the Head of Department of Electrical Engineering (2011-2014), the Director of Institute of Computer and Communication Engineering (2008-2011), the Vice Dean of College of Electrical Engineering and Computer Science (2011), the Director of the Center for Research of E-life Digital Technology (2005-2008), and the Director of Electrical Laboratory (2005-2008), NCKU. She was elected Distinguished Professor of NCKU in 2005 and received the Distinguished Professor Award of Chinese Institute of Electrical Engineering in 2012. She also served as Program Director of the Intelligent Computing Division, Ministry of Science and Technology (2012-2014), Taiwan. She served as the Director of the Department of Information and Technology Education, Ministry of Education.

Dr. Chung’s research interests include computational intelligence, medical image analysis, video analysis, and pattern recognition. She applies most of her research results to healthcare and medical applications. Dr. Chung participated in many international conferences and society activities. She served as the program committee member in many international conferences. She served as the Publicity Co-Chair of WCCI 2014, SSCI 2013, SSCI 2011, and WCCI 2010. She served as an Associate Editor of IEEE Transactions on Neural Network and Learning Systems(2013-2015) and IEEE Transactions on Biomedical Circuits and Systems.

Dr. Chung was the Chair of IEEE Computational Intelligence Society (CIS) (2004-2005) in Tainan Chapter, the Chair of the IEEE Life Science Systems and Applications Technical Committee (2008-2009). She was a member in BoG of CAS Society (2007-2009, 2010-2012). She served as an IEEE CAS Society Distinguished Lecturer (2005-2007) and the Chair of CIS Distinguished Lecturer Program (2012-2013). She served on two terms of ADCOM member of IEEE CIS (2009-2011, 2012-2014), the Chair of IEEE CIS Women in Engineering (2014). She is a Member of Phi Tau Phi honor society and is an IEEE Fellow since 2008. She also served as the Vice President for Members Activities of IEEE CIS (2015-2018).

 
 
 
 
Educational Activities
 
 
 
 

2021 Graduate Student Research Grants: Call for Applications

The IEEE Computational Intelligence Society (CIS) funds scholarships for deserving undergraduate, graduate and PhD students who need financial support to carry out their research during an academic break period. The primary intent of these scholarships is to cover the expenses related to a visit to another university, institute or research agency for collaboration with an identified researcher in the field of interest of the applicant. Funds can be used to cover travel expenses as well as certain living expenses (such as housing). The field of interest of applicants is open but should be connected with an identifiable component of the CIS (neural networks, fuzzy systems, or evolutionary computation). The call for the next round of applications will be announced soon and will have a deadline for submission of 15 Mar 2021.

More information on the scheme can be found on the CIS Graduate Student Research Grants webpage

 
 
 
 
Technical Activites
 
 
 
 

FUZZ-IEEE Competition on Explainable Energy Prediction

In early 2020, the Computational Intelligence Society (CIS) partnered up with one of the leading international energy providers, E.ON SE, seeking the best solutions for energy prediction using Smart meters, and held a competition with great success. The goal of that competition was to predict monthly and yearly consumption from a limited amount of data, in which the evaluation has been focused on relative errors at predicting energy consumption. In this competition, the same prediction problem applies. However, the challenge is building not only accurate but also explainable predictions, so that accurate predictions come along with a narrative explanation in natural language easy to understand by customers.

Important Dates

For further details visit FUZZZ-IEEE Competition on Explainable Energy Predication page

Organisers

  • Isaac Triguero (University of Nottingham)
  • Jose María Alonso (University of Santiago de Compostela)
  • Luis Magdalena (Universidad Politécnica de Madrid)
  • Christian Wagner (University of Nottingham)
  • Juan Bernabé-Moreno (Chief Data Officer E.ON SE) 
 
 
 
 

IEEE CIS Technical Challenge: Final results Announcement

Once the review process by the Scientific Committee was concluded, and after a quite interesting session taking place as part of the 2020 IEEE Symposium Series on Computational Intelligence, the Technical Challenge Committee is delighted to announce the final ranking and awards of the 2nd Technical Challenge of the IEEE Computational Intelligence Society, an exciting competition on energy prediction from Smart Meter Data!

The final ranking is as follows: First prize of US$7000 to Wenlong Wu (USA), second prize of US$5000 to Steffen Limmer (Germany), 3rd prize of US$3000 to Jesus Lago (Belgium), Runner-up recognitions of US$1000 each to three teams: Kasun Bandara, Hansika Hewamalage and Rakshitha Godahewa from Australia, Sven Rebhan and Nils Einecke from Germany, and Alexander Dokumentov and Fedor Dokumentov from Australia.

The competition began on 15 August and finished on 15 November, with more than 70 different teams participating in this challenge.

In a nutshell, the challenge consisted of predicting the next 12 months of energy consumption for 3248 households (for 2018) based on up to 12-months’ worth of data from 2017. Note that different smart meters had a different number of months available (from only one month to 12 months). In addition to consumption data, weather data for 2017 was provided, and for a limited number of households, we also provided some characteristics. For details about the challenge please click here.

Congratulations to the six winners!!

With kind regards,

The Technical Challenge Committee

Isaac Triguero (Chair, Associate Professor, University of Nottingham)
Luis Magdalena (CIS VP TA, Universidad Politécnica de Madrid)
Catherine Huang (Principal Engineer, McAfee LLC)
Hussein Abbass (Professor, University of New South Wales)
Juan Bernabé-Moreno (Chief Data Officer E.ON SE)
Manuel Roveri (Professor, Politecnico di Milano)
 
 
 
 
Journal Special Issues
 
 
 
 
 
 
 
 
CIS Conferences
 
 
 
 
Due to the outbreak of the COVID-19 pandemic, dates and details of CIS sponsored conferences should be monitored closely.

The situation is changing very quickly. Please consult the conference web pages frequently to obtain the latest information.

You can find the most recent announcements and updates from all of our Society’s conferences and events at https://cis.ieee.org/volunteer-resources/covid-19-notice as our organizers make decisions.
 
 

2021 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

Vienna, Austria

4-6 Feb 2021


2021 International Conference on Machine Vision and Augmented Intelligence (MAI 2021)

IIITDM Jabalpur, India

11-14 Feb 2021 


2021 12th International Conference on Agents and Artificial Intelligence (ICAART)

Vienna, Austria

4-6 Feb 2021


2021 IEEE Congress on Evolutionary Computation (CEC)

Kraków, Poland

28 Jun - 1 Jul 2021

Full paper submission deadline: 31 January 2021


2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (IEEE CIVEMSA 2021)

Virtual Conference

18-20 Jun 2021


2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Luxembourg

11-14 Jul 2021

Full paper submission deadline: 10 Feb 2021


2021 IEEE International Conference on Development and Learning (ICDL)

Beijing, China

23-26 Aug 2021

Full paper submission deadline: 15 Feb 2021



2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)

Hyderabad, India

24-26 Aug 2021

Full paper submission deadline: 21 Jan 2021



2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

Melbourne, Australia

13-15 Oct 2021


6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA CECNSM 2021)

Preveza, Greece

24-26 Sept 2021


2020 IEEE Latin American Conference on Computational Intelligence (LA-CCI)

Temuco, Chile

2-4 Nov 2021 


2021 IEEE Symposium Series on Computational Intelligence (SSCI)

Orlando, FL USA

5-8 Dec 2021


2022 IEEE World Congress on Computational Intelligence (IEEE WCCI)

Padua, Italy

18-23 Jul 2022

 

2022 IEEE Conference on Games (IEEE CoG 2022)

21-24 Aug 2022

Beijing, China

 

2022 IEEE Symposium Series on Computational Intelligence (SSCI)

Singapore, Singapore

4-7 Dec 2022

 
 
 
CIS sponsors and co-sponsors a number of conferences across the globe. 
 
 
 
 
 
 
Editor
Leandro L. Minku
University of Birmingham, UK
Email: l.l.minku@cs.bham.ac.uk

 
 
 
 
 
 
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