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IEEE CIS Newsletter, Issue 105, October 2021 (1/2)

A Message from the CIS Newsletter Editor

To best deliver the most recent news from IEEE Computational Intelligence Society (CIS) to all members we will now split the newsletter into two emails per month. The first email will include Announcements, Member Activities, and Research Frontiers. The second email will include Educational Activities, Technical Activities, Journal Special Issues, and CIS Conferences. 

We hope you continue to enjoy CIS Newsletters and have a better reading experience.


Bing Xue

IEEE CIS Newsletter Editor


time to renew text and picture of watch with the IEEE CIS logo

You may now renew your membership(s) and subscription(s) for the 2022 membership year.

Your 2022 IEEE Computational Intelligence Society (CIS) Membership includes:

IEEE Computational Intelligence Magazine (electronic & digital)
IEEE Transactions on Emerging Topics in Computational Intelligence (electronic)
IEEE Transactions on Evolutionary Computation (electronic)
IEEE Transactions on Fuzzy Systems (electronic)
IEEE Transactions on Neural Networks and Learning Systems (electronic)
● Society Newsletter

Additionally, CIS will now offer a Low-to-Middle-Income Countries discount to those who reside in countries where the per capita gross national income (GNI) does not exceed US$15,000 (per World Bank guidelines, using a three-year average). To receive the discount, you must select the IEEE e-membership option. Special Circumstance discounts cannot be used in addition to the Low-to-Middle-Income Countries discount.

We look forward to you continuing to be a part of our CIS family.

Sincerely yours,

Bernadette R Bouchon Meunier
IEEE Computational Intelligence Society


IEEE Digital Privacy Workshop

Digital privacy has become a top social concern for the expanding digital world. Societies are trying to define and navigate the future of digital privacy and determine technological solutions, new policies, and frameworks needed to protect individuals while maintaining support for digital services. There are several broad areas where the landscape is undefined, where policies still need to be developed, and where IEEE expertise may greatly impact the future of “privacy” online. The Digital Privacy Project is exploring how IEEE can best add to this discussion – bringing the perspective of technologists – to help advance solutions to protect personal and private information.

The goal of this two-day workshop is to facilitate discussion on the definition, scope, and common interests in the area of digital privacy for potential future collaboration. We have distinguished keynotes to stimulate your thinking, policy experts to share their global insights, and standards leaders to discuss privacy challenges. Several IEEE Societies and Councils will also share their perspective of digital privacy and why it is important to their community.

Come join us for this exciting two-day event. Registration is FREE to all. For more information please visit the the Digital Privacy Workshop website.

Research Frontier

Can Boosted Randomness Mimic Learning Algorithms of Geometric Nature? Example of a Simple Algorithm That Converges in Probability to Hard-Margin SVM

Artificial deep neural network, schematic model isolated on white, frontal view, 3d renderIn light of the general question posed in the title, we write down a very simple randomized learning algorithm, based on boosting, that can be seen as a nonstationary Markov random process. Surprisingly, the decision hyperplanes resulting from this algorithm converge in probability to the exact hard-margin solutions of support vector machines (SVMs). This fact is curious because the hard-margin hyperplane is not a statistical solution, but a purely geometric one—driven by margin maximization and strictly dependent on particular locations of some data points that are placed in the contact region of two classes, namely the support vectors. The proposed algorithm detects support vectors probabilistically, without being aware of their geometric definition. We give proofs of the main convergence theorem and several auxiliary lemmas. The analysis sheds new light on the relation between boosting and SVMs and also on the nature of SVM solutions since they can now be regarded equivalently as limits of certain random trajectories. In the experimental part, correctness of the proposed algorithm is verified against known SVM solvers: libsvm, liblinear, and also against optimization packages: cvxopt (Python) and Wolfram Mathematica. Read More

IEEE Transactions on Neural Networks and Learning Systems, September 2021


Realizing Behavior Level Associative Memory Learning Through Three-Dimensional Memristor-Based Neuromorphic Circuits

Presentation about machine learning technology with scientist touching screen with artificial intelligence (AI), neural network, automation, and data mining 
words and computer iconsAssociative memory is a widespread self-learning method in biological livings, which enables the nervous system to remember the relationship between two concurrent events. The significance of rebuilding associative memory at a behavior level is not only to reveal a way of designing a brain-like self-learning neuromorphic system but also to explore a method of comprehending the learning mechanism of a nervous system. In this paper, an associative memory learning at a behavior level is realized that successfully associates concurrent visual and auditory information together (pronunciation and image of digits). The task is achieved by associating the large-scale artificial neural networks (ANNs) together instead of relating multiple analog signals. In this way, the information carried and preprocessed by these ANNs can be associated. A neuron has been designed, named signal intensity encoding neurons (SIENs), to encode the output data of the ANNs into the magnitude and frequency of the analog spiking signals. Then, the spiking signals are correlated together with an associative neural network, implemented with a three-dimensional (3-D) memristor array. Furthermore, the selector devices in the traditional memristor cells limiting the design area have been avoided by our novel memristor weight updating scheme. With the novel SIENs, the 3-D memristive synapse, and the proposed memristor weight updating scheme, the simulation results demonstrate that our proposed associative memory learning method and the corresponding circuit implementations successfully associate the pronunciation and image of digits together, which mimics a human-like associative memory learning behavior. Read More

IEEE Transactions on Emerging Topics in Computational Intelligence, August 2021


Increase in Brain Effective Connectivity in Multitasking but not in a High-Fatigue State

Busy Young 
Smiling Businesswoman With Six Arms Doing Different Type Of Work In OfficeMultitasking has become omnipresent in daily activities and increased brain connectivity under high workload conditions has been reported. Moreover, the effect of fatigue on neural activity has been shown in participants performing cognitive tasks, but the effect of fatigue on different cognitive workload conditions is unclear. In this article, we investigated the effect of fatigue on changes in effective connectivity (EC) across the brain network under distinctive workload conditions. There were 133 electroencephalography (EEG) data sets collected from 16 participants over a five-month study, in which high-risk, reduced, and normal states of real-world fatigue were identified through a daily sampling system. The participants were required to perform a lane-keeping task (LKT) with/without multimodal dynamic attention-shifting (DAS) tasks. The results show that the EC magnitude is positively correlated with the increased workload in normal and reduced states. However, low EC was discovered in the high-risk state under high workload conditions. To the best of our knowledge, this investigation is the first EEG-based longitudinal study of real-world fatigue under multitasking conditions. These results could be beneficial for real-life applications, and adaptive models are essential for monitoring important brain patterns under varying workload demands and fatigue states. Read More

IEEE Transactions Cognitive and Developmental Systems, September 2021

Member Activities

Meet IEEE CIS Members

MYi Meieet: Yi Mei, CIS member, Chair of IEEE New Zealand Central Section

What is your title, and place of work? ( or Technical Field of Research)?

I am a Senior Lecturer in Computer Science in Victoria University of Wellington, New Zealand. My research areas include evolutionary computation and learning for scheduling and combinatorial optimisation, hyper-heuristics and automatic heuristic/algorithm design.

How long have you been a member of CIS and what was the reason you chose to join IEEE CIS?

I have been a CIS member since 2012. I joined CIS as I felt there are a lot of opportunities to meet with the world-leading researchers in my research area, learn from the community and contribute back to the community.

What Computational Intelligence society committee do you serve?

I served as a Vice Chair of the Emergent Technologies Technical Committee and a member of the Intelligent Systems Applications Technical Committee in 2017-2018. I am also a member of a number of Task Forces.

What have you learned from your experience and how has it helped you professionally?

Leading is about serving. A good leadership is how much you serve the community and seek for the benefits of its members. This has helped me a lot for my role of Chair of IEEE New Zealand Central Section.

What has been the most fun/rewarding thing about being a volunteer for the IEEE Computational Intelligence Society? What have you enjoyed the most?

I have enjoyed serving for the CIS, especially as the Vice-Chair of the Emergent Technologies Technical Committee and the Acting Chair for organising the mid-year committee meeting in the WCCI 2018 conference. It’s been a great pleasure to discuss the plan to improve the committee with the more experienced colleagues and learn from them. 

Tell us something about you that we don't know.

I have been a fan of Lionel Messi, because we share the same family name (in Chinese). I scored the first goal of our school soccer team in university.


Live Webinar

The Rise of Machine Reasoning

Date: Friday, 12 November 2021
Time: 10:00 AM - 11:00 AM EST


The below citation from J. Pearl’s works expresses the current trend in Computational Intelligence (CI) well: "Causal reasoning is an indispensable component of human thought that should be formalized ... toward achieving human-level machine intelligence." Causal reasoning is, perhaps, the most advanced type of inference used in decision support systems. It currently amalgamates the evolution of inference which started with a rule-based reasoning and continued to the case-based and probabilistic reasoning. It has emerged from a Bayesian approach, and advanced to efficient representation such as Bayesian belief networks. Their role in multiple CI applications will be reviewed in this talk. Most recently, the much-developed area of pattern recognition, also known as machine learning, and its most advanced implementation, ‘deep learning’, have come to understanding of importance of ‘relations’ in pattern recognition, and even created the term ‘deep reasoning’. In this talk, we will focus on examples of causal reasoning for biometric-enabled decision support, and for the performance assessment of decision support systems.


Featured Speaker

Dr Svetlana Yanushkevich
Dr. Svetlana Yanushkevich

Dr. Svetlana Yanushkevich (SM IEEE 2004) is a full professor in the Department of Electrical & Software Engineering at Schulich School of Engineering (SSE), University of Calgary (UofC). She holds a Dr. Habilitated in Tech. Sci. (1999) from the Technical University of Warsaw, and joined the UofC in 2001. She is a founder of the Biometric Technologies Laboratory at the UofC.

Editor Bing Xue
Victoria University of Wellington, New Zealand

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