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IEEE CIS Newsletter, Issue 106, December 2021 (1/2)
 
 
 
 
 
 
Announcements
 
 
 
 

In Memoriam Dr Ingo Rechenberg 

Rechenberg.JPGThe evolutionary computation community mourns the death of Professor Dr. Ingo Rechenberg, who is a pioneer of the fields of evolutionary computation and artificial evolution. In the 1960s and 1970s, he invented one of the earliest and very influential evolutionary computation methods, i.e. evolution strategies. His awards include the Lifetime Achievement Award of the Evolutionary Programming Society in 1995 and the Evolutionary Computation Pioneer Award of the IEEE Neural Networks Society in 2002. In 1954, Rechenberg was also the world champion in the field of model aeroplanes.

 
 
 
 

IEEE CIS on LinkedIn

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And if you want to get involved in the conversation, don’t forget to follow our Twitter account @IEEECIS For our conference organizers, if you are supporting the development of websites or social media accounts, don’t forget to check out important IEEE information for volunteers.

 
 
 
 

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

 
 
 
 
Research Frontier
 
 
 
 

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

A visual representation of XAI.
 A clear white box model containing a digitized brain, with the letters X, A & I etched on the top of the boxRecently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require a high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them. The different categories show different dimensions in interpretability research, from approaches that provide “obviously” interpretable information to the studies of complex patterns. By applying the same categorization to interpretability in medical research, it is hoped that: 1) clinicians and practitioners can subsequently approach these methods with caution; 2) insight into interpretability will be born with more considerations for medical practices; and 3) initiatives to push forward data-based, mathematically grounded, and technically grounded medical education are encouraged. Read More

IEEE Transactions on Neural Networks and Learning Systems, November 2021

 
 
 
 

An Empirical Study of Trends of Popular Virtual Reality Games and Their Complaints


asian woman wears vr glasses and play car racing online video gamesThe market for virtual reality (VR) games is growing rapidly and is expected to grow from 3.3 billion in 2018 to 13.7 billion in 2022. Due to the immersive nature of such games and the use of VR headsets, players may have complaints about VR games, which are distinct from those about traditional computer games, and an understanding of those complaints could enable developers to better take advantage of the growing VR market. We conduct an empirical study of 750 popular VR games and 17 635 user reviews on Steam in order to understand trends in VR games and their complaints. We find that the VR games market is maturing. Fewer VR games are released each month, but their quality appears to be improving over time. Most games support multiple headsets and play areas, and support for smaller scale play areas is increasing. Complaints of cybersickness are rare and declining, indicating that players are generally more concerned with other issues. Recently, complaints about game-specific issues have become the most frequent type of complaint, and VR game developers can now focus on these issues and worry less about VR-comfort issues such as cybersickness. Read More


IEEE Transactions on Games, September 2021

 
 
 
 

Towards Generalized Resource Allocation on Evolutionary Multitasking for Multi-Objective Optimization


Allocation of Resources.
 Marketing Planning Strategy Concept.
 Business Technology.Evolutionary multitasking optimization (EMTO) is an emerging paradigm for solving several problems simultaneously. Due to the flexible framework, EMTO has been naturally applied to multi-objective optimization to exploit synergy among distinct multi-objective problem domains. However, most studies barely take into account the scenario where some problems cannot converge under restrictive computational budgets with the traditional EMTO framework. To dynamically allocate computational resources for multi-objective EMTO problems, this article proposes a generalized resource allocation (GRA) framework by concerning both theoretical grounds of conventional resource allocation and the characteristics of multi-objective optimization. In the proposed framework, a normalized attainment function is designed for better quantifying convergence status, a multi-step nonlinear regression is proposed to serve as a stable performance estimator, and the algorithmic procedure of conventional resource allocation is refined for flexibly adjusting resource allocation intensity and including knowledge transfer information. It has been verified that the GRA framework can enhance the overall performance of the multi-objective EMTO algorithm in solving benchmark problems, complex problems, many-task problems, and a real-world application problem. Notably, the proposed GRA framework served as a crucial component for the winner algorithm in the Competition on Evolutionary Multi-Task Optimization (Multi-objective Optimization Track) in IEEE 2020 World Congress on Computational Intelligence. Read more


IEEE Computational Intelligence Magazine, November 2021

 
 
 
 

IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning


3D illustration of audience holding up cell phones to record eventThe prosperity of smart mobile devices has made mobile crowdsensing (MCS) a promising paradigm for completing complex sensing and computation tasks. In the past, great efforts have been made on the design of incentive mechanisms and task allocation strategies from MCS platform's perspective to motivate mobile users’ participation. However, in practice, MCS participants face many uncertainties coming from their sensing environment as well as other participants’ strategies, and how they interact with each other and make sensing decisions is not well understood. In this paper, we take MCS participants’ perspectives to derive an online sensing policy to maximize their payoffs via MCS participation. Specifically, we model the interactions of mobile users and sensing environments as a multi-agent Markov decision process. Each participant cannot observe others’ decisions, but needs to decide her effort level in sensing tasks only based on local information, e.g., her own record of sensed signals’ quality. To cope with the stochastic sensing environment, we develop an intelligent crowdsensing algorithm IntelligentCrowd by leveraging the power of multi-agent reinforcement learning (MARL). Our algorithm leads to the optimal sensing policy for each user to maximize the expected payoff against stochastic sensing environments, and can be implemented at the individual participant's level in a distributed fashion. Numerical simulations demonstrate that IntelligentCrowd significantly improves users’ payoffs in sequential MCS tasks under various sensing dynamics. Read More


IEEE Transactions on Emerging Topics in Computational Intelligence, October 2021

 
 
 
 

Learn2Evade: Learning-Based Generative Model for Evading PDF Malware Classifiers


colored 
legosRecent research has shown that a small perturbation to an input may forcibly change the prediction of a machine learning (ML) model. Such variants are commonly referred to as adversarial examples . Early studies have focused mostly on ML models for image processing and expanded to other applications, including those for malware classification. In this article, we focus on the problem of finding adversarial examples against ML-based portable document format (PDF) malware classifiers. We deem that our problem is more challenging than those against ML models for image processing because of the highly complex data structure of PDF and of an additional constraint that the generated PDF should exhibit malicious behavior. To resolve our problem, we propose a variant of generative adversarial networks that generate evasive variant PDF malware (without any crash), which can be classified as benign by various existing classifiers yet maintaining the original malicious behavior. Our model exploits the target classifier as the second discriminator to rapidly generate an evasive variant PDF with our new feature selection process that includes unique features extracted from malicious PDF files. We evaluate our technique against three representative PDF malware classifiers (Hidost’13, Hidost’16, and PDFrate-v2) and further examine its effectiveness with AntiVirus engines from VirusTotal. To the best of our knowledge, our work is the first to analyze the performance against the commercial AntiVirus engines. Our model finds, with great speed, evasive variants for all selected seeds against state-of-the-art PDF malware classifiers and raises a serious security concern in the presence of adversaries. Read more


IIEEE Transactions on Artificial Intelligence, August 2021

 
 
 
 

Concept Drift Detection: Dealing With Missing Values via Fuzzy Distance Estimations


searching puzzleIn data streams, the data distribution of arriving observations at different time points may change—a phenomenon called concept drift. While detecting concept drift is a relatively mature area of study, solutions to the uncertainty introduced by observations with missing values have only been studied in isolation. No one has yet explored whether or how these solutions might impact drift detection performance. We, however, believe that data imputation methods may actually increase uncertainty in the data rather than reducing it. We also conjecture that imputation can introduce bias into the process of estimating distribution changes during drift detection, which can make it more difficult to train a learning model. Our idea is to focus on estimating the distance between observations rather than estimating the missing values, and to define membership functions that allocate observations to histogram bins according to the estimation errors. Our solution comprises a novel masked distance learning (MDL) algorithm to reduce the cumulative errors caused by iteratively estimating each missing value in an observation and a fuzzy-weighted frequency (FWF) method for identifying discrepancies in the data distribution. The concept drift detection algorithm proposed in this article is a singular and unified algorithm that can handle missing values, but not an imputation algorithm combined with a concept drift detection algorithm. Experiments on both synthetic and real-world datasets demonstrate the advantages of this method and show its robustness in detecting drift in data with missing values. The results show that compared to the best-performing algorithm that handles imputation and drift detection separately, MDL-FWF reduced the average drift detection difference from 10.75% to 5.83%. This is a nearly 46% improvement. These findings reveal that missing values exert a profound impact on concept drift detection, but using fuzzy set theory to model observations can produce more reliable results than imputation. Read more


IEEE Transactions on Fuzzy Systems, November 2021

 
 
 
 
Member Activities
 
 
Meet:  Bernadette Bouchon-Meunier, IEEE CIS President (2020-2021)
 
What is your title, and place of work? ( or Technical Field of Research)?
I am Director of Research Emeritus at the National Center for Scientific Research (CNRS), in the laboratory LIP6 of Sorbonne Université in Paris.

How long have you been a member of CIS and what was the reason you chose to join IEEE CIS?
I have been a member of CIS since its inception. I was a member of the Neural Network Society before. The reason is that I have been working on fuzzy and occasionally hybrid systems for 40 years and the CIS is my natural home.

What Computational Intelligence society committee do you serve?
Being the current President of the CIS, I chair the CIS Executive and Administrative committees. Before this, I was the CIS Vice President for Conferences from July 2014 to 2018 and CIS President Elect in 2019.

I have been elected to the CIS Adcom several times between 2004 and 2014. During this period, I chaired several committees of the CIS: Women in Computational Intelligence, CIS Graduate Student Research Grants, CIS Fuzzy Systems Technical Committee, and I was a member of several others.

I was also involved in the organization of several conferences. In particular I was Program Chair of FUZZ-IEEE 2010 held in Barcelona, General Chair of IEEE SSCI 2011 held in Paris, Conference chair of FUZZ-IEEE 2012 in Brisbane, Program Chair of FUZZ-IEEE 2013 in Hyderabad.

What have you learned from your experience and how has it helped you professionally?
This experience was an opportunity to discover various facets of the Computational Intelligence community and to develop networking, which is a major component of CIS membership, in addition to the scientific quality of its conferences and publications.

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 was first elected to the CIS Adcom in 2004 and was invited to launch the Women in Computational Intelligence Committee. There were around fifteen CIS members interested, women and men. It was very exciting to develop this activity and to see the interest of the whole Society for gender balance and the support to women. It quickly became a tradition of the CIS to involve women in all CIS committees, conference committees, editorial boards, lists of keynote speakers. I am very happy to see the hundreds of  CIS members who are now participating in this movement and proud that the CIS decided last year to take up the IEEE Women in Engineering pledge to support gender-diversified panels at all its meetings, conferences, and events.

Tell us something about you that we don’t know.
From 1980 to 2013, fifty-two PhD students defended their thesis under my supervision or co-supervision. Among them, 13 were women, 39 were men, 24 were from Africa or the Middle-East, 4 from Asia and 1 from South America. During their PhD, 25 of them were financially supported by a grant from industry. After their PhD, 21 became academics in France or abroad, while 31 worked in companies. This is my contribution to diversity and inclusion in Computational Intelligence!

bernadette-students.jpg
With a part of my former PhD students and some of their own PhD students


 
 
 
 

Live Webinar

Machine Learning Models in Autonomous Driving: From Theory to Practice

Date: Tuesday, 14 December 2021
Time: 11:00 AM - 12:00 PM EST

Abstract:

In the first Mojave Desert DARPA challenge in 2004 the best self-driving car could only manage about 7 miles. In 2021 however, after years of advancements in autonomous driving, several companies’ vehicles covered hundreds of miles with minor intervention. Machine learning (ML) in autonomous driving makes it possible for a vehicle to collect data on its surroundings using sensors, interpret the data and then decide what action to take. Recent advancements in ML allow self-driving systems to perform various tasks as well as or even better than humans. Read more

 
 
 

Featured Speaker

 
Kanishka Tyagi
Kanishka Tyagi


Dr. Kanishka Tyagi works as a lead machine learning autonomous driving scientist at Aptiv corporation in Agoura Hills, California.

 
 
 
 
 
Educational Activities
 
 
 
 
2022 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 March 2022.

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

 
 
 
 
 
 
Editor Bing Xue
Victoria University of Wellington, New Zealand
Email: Bing.Xue@ecs.vuw.ac.nz

 
 
 
 
 
 
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