Live Webinar with Professor Anna Wilbik

Anna Wilbik is currently Professor in Data Fusion and Intelligent Interaction in the Department of Advanced Computing Sciences of Maastricht University, in the Netherlands. Currently she is also a chair of The Fuzzy Systems Technical Committee (FSTC) within IEEE CIS. 

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Exploring Dynamic Pandemic Containment Strategies Using Multi-Objective Optimization

The SARS-CoV-2 pandemic demonstrates the vulnerability of societies in a globalized world. As pathogens spread at exponential rates, rapid development of appropriate medical treatments and distribution of vaccinations are major challenges. Under these circumstances, authorities employ non-pharmaceutical interventions (NPIs) against the spread, which can impact the economy strongly. Hence, there is a need for strategies that help minimize infection without sacrificing the economy’s wellbeing. This study explores the inherent trade-off characteristics of optimal control strategies by utilizing the well-known SEIR (susceptible, exposed, infectious, recovered) pandemic model with an integrated economic compartment. Read More

IEEE Computational Intelligence Magazine, August 2022

Wavefront-Based Multiple Rumor Sources Identification by Multi-Task Learning

Identifying rumor sources in social networks is one of the key tasks for defeating rumors automatically. Many efforts have been devoted to locating rumor sources with an assumption that the infected status of each node is known in advance, while other efforts focus on identifying sources with partial infection knowledge, such as wavefront, sparse observers, and snapshots. Wavefront is a set of nodes that are infected at the latest propagation in social networks, which is originally defined for analyzing the SARS epidemic, and shows considerable importance in information source locating task. Read More

Transactions on Emerging Topics in Computational Intelligence, October 2022

EDA++: Estimation of Distribution Algorithms With Feasibility Conserving Mechanisms for Constrained Continuous Optimization

Handling nonlinear constraints in continuous optimization is challenging, and finding a feasible solution is usually a difficult task. In the past few decades, various techniques have been developed to deal with linear and nonlinear constraints. However, reaching feasible solutions has been a challenging task for most of these methods. In this article, we adopt the framework of estimation of distribution algorithms (EDAs) and propose a new algorithm (EDA++) equipped with some mechanisms to deal with nonlinear constraints.  Read more

IEEE Transactions on Evolutionary Computation, October 2022

What Causes Wrong Sentiment Classifications of Game Reviews?

Sentiment analysis is a popular technique to identify the sentiment of a piece of text. Several different domains have been targeted by sentiment analysis research, such as Twitter, movie reviews, and mobile app reviews. Although several techniques have been proposed, the performance of current sentiment analysis techniques is still far from acceptable, mainly when applied in domains on which they were not trained. In addition, the causes of wrong classifications are not clear. In this article, we study how sentiment analysis performs on game reviews. Read More

IEEE Transactions on Games, September 2022

Multiagent Meta-Reinforcement Learning for Adaptive Multipath Routing Optimization

In this article, we investigate the routing problem of packet networks through multiagent reinforcement learning (RL), which is a very challenging topic in distributed and autonomous networked systems. In specific, the routing problem is modeled as a networked multiagent partially observable Markov decision process (MDP). Since the MDP of a network node is not only affected by its neighboring nodes’ policies but also the network traffic demand, it becomes a multitask learning problem. Inspired by recent success of RL and metalearning, we propose two novel model-free multiagent RL algorithms, named multiagent proximal policy optimization (MAPPO) and multiagent metaproximal policy optimization (meta-MAPPO), to optimize the network performances under fixed and time-varying traffic demand, respectively. Read More

IEEE Transactions on Neural Networks and Learning Systems, October 2022

An End-to-End Spiking Neural Network Platform for Edge Robotics: From Event-Cameras to Central Pattern Generation

Learning to adapt one’s gait with environmental changes plays an essential role in the locomotion of legged robots which remains challenging for constrained computing resources and energy budget, as in the case of edge-robots. Recent advances in bio-inspired vision with dynamic vision sensors (DVSs) and associated neuromorphic processing can provide promising solutions for end-to-end sensing, cognition, and control tasks. However, such bio-mimetic closed-loop robotic systems based on event-based visual sensing and actuation in the form of spiking neural networks (SNNs) have not been well explored.Read More

IEEE Transactions on Cognitive and Developmental Systems, September 2022

Cross-Domain Modality Fusion for Dense Video Captioning

Dense video captioning requires localization and description of multiple events in long videos. Prior works detect events in videos solely relying on the visual content and completely ignore the semantics (captions) related to the events. This is undesirable because human-provided captions often also describe events that are visually nonpresent or subtle to detect. In this research, we propose to capitalize on this natural kinship between events and their human-provided descriptions. Read More

IEEE Transactions on Artificial Intelligence, October 2022

Call For Papers for IEEE Computational Intelligence Magazine (CIM) special issue on Artificial Intelligence eXplained (AI-X)

AI solutions have demonstrated excellent performances in different fields and applications and attracted numerous people of studying. Hence, the need to explain the ideas and methods of AI to a broad audience arises. This special issue focuses on web-based interactive articles to explain AI concepts or methods, demonstrate the outcome of AI systems, and introduce non-experts to the field of AI.

This is a new type of publication, supported by IEEE Xplore. Interactive components (WebGL, Javascript) can be used for interactively analyzing a data set or a learned model. In this way, the audiences will gain access to immersive learning experiences for better understanding the methods.

Authors are required to submit a 2-page extended abstract, which describes the main concepts or methods the authors want to explain to the readers. In addition, the authors need to build a webpage to publish the full article on IEEE Xplore where they explain the concepts or methods of AI to the readers through interactive approaches.

Deadline for paper submission is 31 January 2023. For more information, please refer to CIM Information for Authors.

• IEEE TCDS Special Issue on Movement Sciences in Cognitive Systems (6 January 2023)
• IEEE CIM Special Issue on Artificial Intelligence eXplained (AI-X) (31 January 2023)
• IEEE TCDS Special Issue on Advancing Machine Intelligence with Neuromorphic Computing (31 January 2023)
• IEEE TETCI Special Issue on Resource Sustainable Computational and Artificial Intelligence (1 February 2023)
• IEEE TNNLS Special Issue on Graphic Learning (1 March 2023)
• IEEE TEVC Special Issue on Evolutionary Neural Architecture Search (28 March 2023 )
• IEEE TNNLS Special Issue on Learning Theories and Methods with Application to Digitized Process Manufacturing (31 March 2023)

IEEE 2023 Congress on Evolutionary Computation 

IEEE CEC 2023 is a world-class conference that brings together researchers and practitioners in the field of evolutionary computation and computational intelligence from around the globe. The conference program includes plenary lectures, regular and special sessions, tutorials, competitions, workshops and panel discussions. Currently calls includes:

• Call for Competitions
• Call for Tutorials
• Call for Workshops
• Call for Sponsors
• Call for Participation Grants
• Call for Papers