IEEE Conference on Artificial Intelligence (IEEE CAI)

The IEEE Conference on Artificial Intelligence (IEEE CAI) will take place in Santa Clara, CA, USA on 5-6 June 2023. This new conference series emphasizes AI applications in specific verticals that impact industrial technology and innovation. Key verticals include Energy, Healthcare and Life Science, Transportation and Aerospace, Earth Systems Decision Support, Industrial AI, and Ethical and Societal Implications of AI.

IEEE CAI seeks original, high-quality poster papers describing the research and results that contribute to advancements in AI applications. Access all submission details here.

New Editors-in-Chief for IEEE Transactions on Fuzzy Systems and IEEE Transactions on Emerging Topics in Computational Intelligence

Professor Jon Garibaldi, the current Editor-in-Chief of IEEE Transactions on Fuzzy Systems, has completed his maximum six years of service, and will be succeeded by Professor Dongrui Wu from Huazhong University of Science and Technology from January 2023.

Professor Yew-Soon Ong, the founding and current Editor-in-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence, will conclude his tenure at the helm of the journal. He will be succeeded by Professor Yiu-ming Cheung of the Hong Kong Baptist University.

Immersive Articles 

Immersive Articles are finally here! This strong collaboration between the Publications and Education Committees of CIS was made possible by a wonderful partnership with the IEEE Xplore team in Marketing & Sales. Take a glance at the first instance of this new publication format implemented in IEEE Xplore. Using interactive components, authors can now directly involve readers of their papers in analyzing a data set or a learned model. In this way, the audiences will gain access to immersive learning experiences for a better understanding of the methods discussed.

This new publication format will be highlighted by a special issue of the IEEE Computational Intelligence Magazine. The deadline for paper submission is 31 March 2023. For more information, please refer to the call for papers of the special issue on Artificial Intelligence eXplained (AI-X) and the submission instructions to be found on CIM Information for Authors.

If you want to learn more about how to create immersive articles, watch out for an upcoming webinar announcement in January.

IEEE CIS Argentina Chapter Celebrates 20th Anniversary

Vision-Action Semantic Associative Learning Based on Spiking Neural Networks for Cognitive Robot

Establishing cognitive environments help cognitive robots understand human actions, languages and observed objects. In this paper, a cognitive robotic model based on a novel spiking bidirectional associative memory (BAM) method is presented to establish a cognitive environment. The spiking BAM network uses a supervised spike-based learning rule to learn the relationship between the semantic information of vision and action, where a new coding scheme is proposed to encode and decode vision and action semantic information.  Read More

IEEE Computational Intelligence Magazine, November 2022

Self-Consistent Learning of Neural Dynamical Systems From Noisy Time Series

We introduce a new method which, for a single noisy time series, provides unsupervised filtering, state space reconstruction, efficient learning of the unknown governing multivariate dynamical system, and deterministic forecasting. We construct both the underlying trajectories and a latent dynamical system using deep neural networks. Under the assumption that the trajectories follow the latent dynamical system, we determine the unknowns of the dynamical system, and filter out stochastic outliers in the measurements. In this sense the method is self-consistent. Read More

Transactions on Emerging Topics in Computational Intelligence, October 2022

Evaluating Mixed-Initiative Procedural Level Design Tools Using a Triple-Blind Mixed-Method User Study

Results from a triple-blind mixed-method user study into the effectiveness of mixed-initiative tools for the procedural generation of game levels are presented. A tool which generates levels using interactive evolutionary optimization was designed for this study which 1) is focused on supporting the designer to explore the design space and 2) only requires the designer to interact with it by designing levels. The tool identifies level design patterns in an initial hand-designed map and uses that information to drive an interactive optimization algorithm.  Read more

IEEE Transactions on Games, September 2022

Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-Based Approach

In this article, we propose a general framework for the unsupervised fuzzy rule-based dimensionality reduction primarily for data visualization. This framework has the following important characteristics relevant to the dimensionality reduction for visualization: preserves neighborhood relationships; effectively handles data on nonlinear manifolds; capable of projecting out-of-sample test points; can reject test points, when it is appropriate; and interpretable to a reasonable extent. We use the first-order Takagi–Sugeno model. Read More

IEEE Transactions on Fuzzy Systems, July 2022

Robotic Manipulation Skill Acquisition Via Demonstration Policy Learning

Current robots can perform repetitive tasks well, but are constrained to environment and task variations. Teaching a robot by demonstration is a powerful approach to solve the problem. The learning methods using large sensory and joint state information are extremely difficult to efficiently learn the demonstration policy. This article proposes a learning-by-imitation approach that learns demonstration policy for robotic manipulation skill acquisition from what-where-how interaction data. Read More

IEEE Transactions on Cognitive and Developmental Systems, September 2022

Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing

Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing (HU), yet their ability to simultaneously generalize various spectral variabilities (SVs) and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various SVs. Read More

IEEE Transactions on Neural Networks and Learning Systems, November 2022

• 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 TNNLS Special Issue on Deep Learning for Intelligent Media Computing and Applications (31 January 2023)

• IEEE TETCI Special Issue on Resource Sustainable Computational and Artificial Intelligence (1 February 2023)

• IEEE TETCI Special Issue on Trusted Mobile Crowdsourcing for Next Generation Intelligent Transportation Systems (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 TNNLS Special Issue on Deep Learning: When and How? (23 April 2023 )

10th IEEE Swiss Conference on Data Science (IEEE SDS 2023)

The 10th IEEE Swiss Conference on Data Science (IEEE SDS 2023), to be held in Zurich, Switzerland, 22-23 June 2023, is an international conference that brings together researchers and practitioners in the field of applied Data Science. This conference is unique as it combines cutting-edge academic research with successful business implementations, and thus creating a space where researchers and practioners can learn from each other. More details can be seen at 10th IEEE Swiss Conference on Data Science (IEEE SDS 2023).