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CIS Publication Spotlight [Publication Spotlight] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-08 Yongduan Song, Dongrui Wu, Carlos A. Coello Coello, Georgios N. Yannakakis, Huajin Tang, Yiu-Ming Cheung, Hussein Abbass
Presents a brief summary of new publications in the area of computational intelligence.
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Recent Developments in Recommender Systems: A Survey [Review Article] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-08 Yang Li, Kangbo Liu, Ranjan Satapathy, Suhang Wang, Erik Cambria
In this technical survey, the latest advancements in the field of recommender systems are comprehensively summarized. The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems. It starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and
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An Objective Space Constraint-Based Evolutionary Method for High-Dimensional Feature Selection [Research Frontier] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-08 Fan Cheng, Rui Zhang, Zhengfeng Huang, Jianfeng Qiu, Mingming Xia, Lei Zhang
Evolutionary algorithms (EAs) have shown their competitiveness in solving the problem of feature selection. However, limited by their encoding scheme, most of them face the challenge of “curse of dimensionality”. To address the issue, in this paper, an objective space constraint-based evolutionary algorithm, named OSC-EA, is proposed for high-dimensional feature selection (HDFS). Although the decision
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Diffusion Model-Based Multiobjective Optimization for Gasoline Blending Scheduling IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-08 Wenxuan Fang, Wei Du, Renchu He, Yang Tang, Yaochu Jin, Gary G. Yen
Gasoline blending scheduling uses resource allocation and operation sequencing to meet a refinery’s production requirements. The presence of nonlinearity, integer constraints, and a large number of decision variables adds complexity to this problem, posing challenges for traditional and evolutionary algorithms. This paper introduces a novel multiobjective optimization approach driven by a diffusion
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Encoding Distributional Soft Actor-Critic for Autonomous Driving in Multi-Lane Scenarios [Research Frontier] [Research Frontier] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05 Jingliang Duan, Yangang Ren, Fawang Zhang, Jie Li, Shengbo Eben Li, Yang Guan, Keqiang Li
This paper proposes a new reinforcement learning (RL) algorithm, called encoding distributional soft actor-critic (E-DSAC), for decision-making in autonomous driving. Unlike existing RL-based decision-making methods, E-DSAC is suitable for situations where the number of surrounding vehicles is variable and eliminates the requirement for manually pre-designed sorting rules, resulting in higher policy
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Don't Play Games, Optimize [President's Message] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05 Yaochu Jin
When I give a talk about evolutionary machine learning, one question I often expect is why I use an evolutionary algorithm to optimize the hyperparameters and structure of a neural network, rather than using a reinforcement learning algorithm. A quick answer might be, well, I am an evolutionary computation guy. I know this is a sloppy answer. Often, I attempt to explain the potential benefits of using
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IEEE Fellows–Class of 2024 [Society Briefs] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05 Gary G. Yen
Presents a listing of CIS members who were elevated to the status of IEEE Fellow.
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Call for Participation: IEEE Conference on Artificial Intelligence IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05
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Newly Elected CIS Administrative Committee Members [Society Briefs] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05 Yaochu Jin
Pauline Haddow is a Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway. She received a first-class honours degree from the University of Glasgow, Scotland in 1991 and her PhD in 1998 from NTNU, Norway. She has chaired the complex, reliable and adaptive systems lab (CRAB lab) at NTNU for around 25 years. She has supervised/co-supervised
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A Self-Learning Framework for Large-Scale Conversational AI Systems IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05 Xiaohu Liu, Chenlei Guo, Benjamin Yao, Ruhi Sarikaya
In the last decade, conversational artificial intelligence (AI) systems have been widely employed to address people’s real-life needs across various different environments and settings. At the same time, users’ expectations of these systems have been on the rise as they expect more contextual and personalized interactions with continuous learning systems, akin to their expectation in human-human interactions
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Redefining Efficiency: The Rise of AI/CI-Assisted Innovations [Editor's Remarks] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05 Chuan-Kang Ting
In sci-fi novels and movies, AI is often portrayed as a symbol representing either an ultimate adversary threatening human existence or a focal point provoking ethical and societal debate. While public perception of AI oscillates between recognizing its widespread benefits and fearing the chaos it could unleash upon humanity, it is undeniable that AI and CI technologies are increasingly integrating
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FairerML: An Extensible Platform for Analysing, Visualising, and Mitigating Biases in Machine Learning [Application Notes] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05 Bo Yuan, Shenhao Gui, Qingquan Zhang, Ziqi Wang, Junyi Wen, Bifei Mao, Jialin Liu, Xin Yao
Given the growing concerns about bias in machine learning, dozens of metrics have been proposed to measure the fairness of machine learning. Several platforms have also been developed to compute and illustrate fairness metrics on platform-provided data. However, most platforms do not provide a user-friendly interface for users to upload and evaluate their own data or machine learning models. Moreover
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Share Your Preprint Research with the World! IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05
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Conference Calendar [Conference Calendar] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05 Leandro Lei Minku, Liyan Song
The 2nd International Conference on Cyber-energy Systems and Intelligent Energies (ICCSIE 2024)
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Hierarchical Bipartite Graph Convolutional Network for Recommendation IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05 Yi-Wei Cheng, Zhiqiang Zhong, Jun Pang, Cheng-Te Li
Graph Neural Networks (GNNs) have emerged as a dominant paradigm in machine learning for graphs, and recently developed Recommendation System (RecSys) models have significantly benefited from them. However, recent research has highlighted a limitation in classical GNNs, revealing that their message-passing mechanism is inherently flat, making it unable to capture hierarchical semantics within the graph
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Genetic Programming and Reinforcement Learning on Learning Heuristics for Dynamic Scheduling: A Preliminary Comparison IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05 Meng Xu, Yi Mei, Fangfang Zhang, Mengjie Zhang
Scheduling heuristics are commonly used to solve dynamic scheduling problems in real-world applications. However, designing effective heuristics can be time-consuming and often leads to suboptimal performance. Genetic programming has been widely used to automatically learn scheduling heuristics. In recent years, reinforcement learning has also gained attention in this field. Understanding their strengths
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AI for Materials Design and Discovery Using Atomistic Scale Information [Industrial and Governmental Activities] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-04-05 Massimiliano Lupo Pasini
The design and discovery of materials with desired functional properties is pivotal to the scientific mission of the United States Department of Energy (US-DOE) [1], which includes within its portfolio several important applications for the national economy and security. These applications range from: renewable energy (e.g., solar cells, organic photovoltaics, and organic light-emitting diodes), energy
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Call for Participation: IEEE World Congress on Computational Intelligence IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08
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IEEE Computational Intelligence Society Publications IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08
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Share Your Preprint Research with the World! IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08
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2024 IEEE Conference on Artificial Intelligence IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08
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IEEE Connects You to a Universe of Information! IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08
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CIS-ing in an Uncertain World [President's Message] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Yaochu Jin
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
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IEEE CIS VP-Conferences Vision Statement [Society Briefs] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Leandro L. Minku
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
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IEEE CIS VP-Technical Activities Vision Statement [Society Briefs] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Christian Wagner
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,
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2024 IEEE CIS Awards [Society Briefs] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Chin-Teng Lin
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:
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Acknowledging Volunteers Associated With 2023 IEEE CIS Conference Management [Society Briefs] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Marley Velasco, Steven Corns
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
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Guest Editorial: AI-eXplained (Part II) [Guest Editorial] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Pau-Choo Chung, Alexander Dockhorn, Jen-Wei Huang
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
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CIS: Connect It Strong! [Editor's Remarks] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Chuan-Kang Ting
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
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AI in Industrial IoT Cybersecurity [Industrial and Governmental Activities] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Kai Goebel, Shantanu Rane
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
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Breaking Boundaries Initiative: Workshop on Industry-Academia Collaboration [Technical Activities] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Marcin Pietrasik, Anna Wilbik, Paul Grefen, Christian Wagner, Luis Magdalena
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.
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The IEEE XES Standard for Process Mining: Experiences, Adoption, and Revision [Society Briefs] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Moe T. Wynn, Wil van der Aalst, Eric Verbeek, Bruno Di Stefano
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
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CIS Publication Spotlight [Publication Spotlight] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Yongduan Song, Dongrui Wu, Carlos A. Coello Coello, Georgios N. Yannakakis, Huajin Tang, Yiu-ming Cheung
“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
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Forward Composition Propagation for Explainable Neural Reasoning IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Isel Grau, Gonzalo Nápoles, Marilyn Bello, Yamisleydi Salgueiro, Agnieszka Jastrzebska
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
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Dual Sparse Structured Subspaces and Graph Regularisation for Particle Swarm Optimisation-Based Multi-Label Feature Selection IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Kaan Demir, Bach Hoai Nguyen, Bing Xue, Mengjie Zhang
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
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SPAIC: A Spike-Based Artificial Intelligence Computing Framework IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Chaofei Hong, Mengwen Yuan, Mengxiao Zhang, Xiao Wang, Chengjun Zhang, Jiaxin Wang, Gang Pan, Huajin Tang
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
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When Evolutionary Computation Meets Privacy IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Bowen Zhao, Wei-Neng Chen, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Jun Zhang
Recently, evolutionary computation (EC) has experienced significant advancements due to the integration of machine learning, distributed computing, and big data technologies. These developments have led to new research avenues in EC, such as distributed EC and surrogate-assisted EC. While these advancements have greatly enhanced the performance and applicability of EC, they have also raised concerns
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Interactive Augmentations, Features, and Parameters for Contrastive Learning [AI-eXplained] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Yu-Ting Chen, Chien-Yu Chiou, Chun-Rong Huang
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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How to Build Self-Explaining Fuzzy Systems: From Interpretability to Explainability [AI-eXplained] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Ilia Stepin, Muhammad Suffian, Alejandro Catala, Jose M. Alonso-Moral
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
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An Interactive Approach to Build Fuzzy Color Spaces [AI-eXplained] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Míriam Mengíbar-Rodríguez, Jesús Chamorro-Martínez, James M. Keller
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.
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Playing With Monte-Carlo Tree Search [AI-eXplained] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Yunlong Zhao, Chengpeng Hu, Jialin Liu
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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Conference Calendar [Conference Calendar] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2024-01-08 Leandro L. Minku, Liyan Song
* Denotes a CIS-Sponsored Conference
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An Interaction Is Worth a Thousand Words [Editor's Remarks] IEEE Comput. Intell. Mag. (IF 9.0) Pub Date : 2023-10-17 Chuan-Kang Ting
One year ago, CIM published the inaugural AI-eXplained (AI-X) immersive article on IEEE Xplore. It is thrilled to witness that our call for the new form of content representation has been responded to with tremendous passion and a surge of submissions. With great efforts, CIM offers a platform for delivering Al/CI concepts, designs, and applications via a vivid and innovative means of knowledge distribution