Hangzhou, where I am now living and which is most well-known for the West Lake, has recently witnessed the highest temperature this summer, 43 degrees Celsius. I studied and worked in this city for 12 years during the 1980s and 1990s, but had no impression of ever experiencing such hot weather during that time. Clearly, climate change is making heat waves hotter and more frequent. Then, what we CIS-ers

Despite the recent advancements in dialogue systems, persona-driven chatbots are still in their infancy. Previous studies on persona-driven dialogue generation demonstrated its ability in generating responses that contain more detailed persona information. However, the challenge of maintaining persona consistency and contextual coherence still persists in persona-driven dialogue generation. Moreover

Large Language Models (LLMs) such as OpenAI’s ChatGPT have achieved surprisingly huge progresses in the field of Natural Language Processing (NLP). This paper aims to present an immersive introduction to LLMs from the perspective of generative models. The main components of the training process of LLMs are explained, and an example of LLMs for AI-generated contents is given. This short paper is a summary

The intelligent soft robot has received wide attention from both academia and the industry due to its remarkable adaptability. It performs intelligent behavioral learning and evolved morphologies in unpredictable environmental conditions. However, designing a soft robot with a well-adapted morphology involves searching through a large number of possible structures. Furthermore, to learn control tasks

Meta-heuristic algorithms, especially evolutionary algorithms, have been frequently used to find near optimal solutions to combinatorial optimization problems. The evaluation of such algorithms is often conducted through comparisons with other algorithms on a set of benchmark problems. However, even if one algorithm is the best among all those compared, it still has difficulties in determining the

The 2024 IEEE Conference on Artificial Intelligence was held from 25-27 June at the Marina Bay Sands Conference Centre in Singapore (Figure 1). IEEE CAI 2024, the second edition of this conference and exhibition, emphasised AI applications and key AI verticals impacting industrial technology and innovation. Similar to the inaugural event in June 2023 at Santa Clara, California, the 2024 edition was

“The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More specifically, we parameterize nonlinear systems through a generalized kernel representation for system modeling and the

Large variations across images make image classification a challenging task; limited training data further increases its difficulty. Genetic programming (GP) has been considerably applied to image classification. However, most GP methods tend to directly evolve a single classifier or depend on a predefined classification algorithm, which typically does not lead to ideal generalization performance when

The 2024 IEEE World Congress on Computational Intelligence (IEEE WCCI1 2024) began with this congratulation in the Opening Ceremony on July 1st, 2024, in Yokohama, Japan, as shown in Figure 1. This IEEE Computational Intelligence Society (CIS) flagship conference started in 1994 in Orlando, Florida, USA, to enhance the interdisciplinary discussion and cooperation by calling together the people in neural

The Transformer-based methods provide a good opportunity for modeling the global context of gigapixel whole slide image (WSI), however, there are still two main problems in applying Transformer to WSI-based survival analysis task. First, the training data for survival analysis is limited, which makes the model prone to overfitting. This problem is even worse for Transformer-based models which require

I am deeply honored to serve as the President of the IEEE Computational Intelligence Society (CIS) for 2024-2025. I had never imagined that I would become the President of our society when I joined IEEE at the 1998 World Congress on Computational Intelligence. I would take this opportunity to thank Bernadette Bouchon-Meunier, chair of the nomination committee and her colleagues, for their trust. I

It is a great honor and a privilege for me to serve as the Vice President for Conferences of the IEEE Computational Intelligence Society (CIS). Conferences are one of the main avenues for Computational Intelligence (CI) researchers to interact with each other and exchange ideas for the advancement of the field. CIS has a long history of providing high-quality conferences on various CI topics for our

It is a real pleasure and privilege to have been elected to and to serve as Vice President for Technical Activities of the IEEE Computational Intelligence Society (CIS) for 2024 and 2025. CIS is a vibrant home for individuals from all walks of life who are passionate about the theory, design, application, and development of a broad range of artificially intelligent systems. Just as the technology,

He is author of the books “Artificial Neural Networks for Modelling and Control of Non-linear Systems” (Springer) and “Least Squares Support Vector Machines” (World Scientific), co-author of the book “Cellular Neural Networks, Multi-Scroll Chaos and Synchronization” (World Scientific) and editor of the books “Nonlinear Modeling: Advanced Black-Box Techniques” (Springer), “Advances in Learning Theory:

The IEEE Computational Society (CIS) offers many financially-sponsored conferences each year. All of these depend on the hard work and effort of volunteers who propose and then organize these events in the pursuit of a healthy exchange of information and improvement in the application and theory of computational intelligence approaches. The last few years in particular have been a challenging time

Thanks to the many submissions we have received, we can present this second part of our special issue on “AI-eXplained.” In here, we continue our mission to demystify the intricate world of artificial intelligence and make it accessible to a broader audience. As AI continues to evolve and integrate into various aspects of our lives, it becomes increasingly important to bridge the gap between experts

The beginning of a year is a good timing to conclude on what memorable moments have happened in the preceding year. Last summer, I was invited to give talks at the 2023 IEEE CIS Summer School and Summit Forum held at the Southern University of Science and Technology (SUSTech) in Shenzhen, China. Several IEEE CIS officers, Editors-in-Chief, and I gathered to introduce CI techniques and their advancements

Ai's role within the domain of Industrial IoT (IIoT) is somewhat obscured by other limelight-stealing feats of AI, such as the creation of convincing deep fakes featuring politicians or celebrities. However, the critical importance of IIoT in domains like consumer goods manufacturing, healthcare, power generation, and transportation mandates a closer examination of the new capabilities and pitfalls

Reports on the activities and discussions that were part of the Breaking Boundaries: Industry-Academia Workshop that took place on the 27th and 28th of June 2023 at Maastricht University in The Netherlands.

The IEEE Standards Association (SA) officially published the XES Standard as IEEE Std 1849-2016: IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams on 11 November 2016. This standard has been sponsored by the IEEE Computational Intelligence Society (CIS) Standards Committee. Through the XES Standard, event data can be transported from the

“Large-scale multiobjective optimization problems (LSMOPs) are characterized as optimization problems involving hundreds or even thousands of decision variables and multiple conflicting objectives. To solve LSMOPs, some algorithms designed a variety of strategies to track Pareto-optimal solutions (POSs) by assuming that the distribution of POSs follows a low-dimensional manifold. However, traditional

This paper proposes an algorithm called Forward Composition Propagation (FCP) to explain the predictions of feed-forward neural networks operating on structured classification problems. In the proposed FCP algorithm, each neuron is described by a composition vector indicating the role of each problem feature in that neuron. Composition vectors are initialized using a given input instance and subsequently

Many real-world classification problems are becoming multi-label in nature, i.e., multiple class labels are assigned to an instance simultaneously. Multi-label classification is a challenging problem due to the involvement of three forms of interactions, i.e., feature-to-feature, feature-to-label, and label-to-label interactions. What further complicates the problem is that not all features are useful

Neuromorphic computing is an emerging research field that aims to develop new intelligent systems by integrating theories and technologies from multiple disciplines, such as neuroscience, deep learning and microelectronics. Various software frameworks have been developed for related fields, but an efficient framework dedicated to spike-based computing models and algorithms is lacking. In this work

Building and testing software for embedded systems can be challenging with an impact on delivery time, design reproducibility, and collaboration among project contributors. To accelerate project development, presented here is an automated build flow that utilizes Xilinx PetaLinux, and field programmable gate array (FPGA) hardware description and integrates with the GitLab continuous integration and

Recently, contrastive learning has shown its effectiveness in self-supervised learning by training features of augmentations of input images based on the contrastive loss. This paper aims to introduce contrastive learning and discuss the effects of augmentations, features, and parameters of contrastive learning. Interactive figures are developed to demonstrate the effective schemes proposed in contrastive

Fuzzy systems are known to provide not only accurate but also interpretable predictions. However, their explainability may be undermined if non-semantically grounded linguistic terms are used. Additional non-trivial challenges would arise if a prediction were to be explained counterfactually, i.e., in terms of hypothetical, non-predicted outputs. In this paper, we explore how both factual and counterfactual

In this paper, the idea of a fuzzy color and a fuzzy color space is shown in an interactive way. It is proposed several interactive elements, where readers can understand the different steps to build them. Furthermore, these elements allow the user to test with his/her own images via the behavior of the fuzzy colors.

Recently, contrastive learning has shown its effectiveness in self-supervised learning by training features of augmentations of input images based on the contrastive loss. This paper aims to introduce contrastive learning and discuss the effects of augmentations, features, and parameters of contrastive learning. Interactive figures are developed to demonstrate the effective schemes proposed in contrastive

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