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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Pang, Lie Meng Ishibuchi, Hisao Nan, Yang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635054

To handle constrained multi-objective optimization problems (CMOPs), many constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed. However, a recent study has shown that many of these CMOEAs do not perform well on real-world CMOPs. In contrast, NSGA-II, proposed over 20 years ago with a simple constrained dominance principle, has demonstrated better performance than recent CMOEAs on many real-world CMOPs. Motivated by NSGA-II’s promising results, this paper aims to further enhance its performance. We explore the idea of enhancing NSGA-II with a knee point for solving CMOPs. Specifically, a knee point is identified using the minimum distance from the estimated ideal point, and always included in the next population of NSGA-II. Experimental results show that this simple idea improves the performance of NSGA-II on real-world CMOPs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Stability in Online Coalition Formation

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Journal of Artificial Intelligence Research 2025年 82卷 2423-2452页

作者： Bullinger, Martin Romen, René Department of Computer Science University of Oxford 7 Parks Road OxfordOX1 3QD United Kingdom School of Computation Information and Technology Technical University of Munich Boltzmannstr 3 Munich85748 Germany

Coalition formation is concerned with the question of how to partition a set of agents into disjoint coalitions according to their preferences. Deviating from most of the previous work, we consider an online variant of the problem, where agents arrive in sequence. Whenever an agent arrives, they must be assigned to a coalition immediately and irrevocably. The scarce existing literature on online coalition formation has focused on maximizing social welfare, a demanding requirement, even in the offline setting. Instead, we seek to achieve stable coalition structures online and treat the most common stability concepts based on deviations by single agents and groups of agents. We present a comprehensive picture in additively separable hedonic games, leading to dichotomies, where positive results are obtained by deterministic algorithms and negative results even hold for randomized algorithms. ©2025 The Authors.

关键词： reinforcement learning

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Mcrood: Multi-Class Radar Out-Of-Distribution Detection 48

Mcrood: Multi-Class Radar Out-Of-Distribution Detection

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kahya, Sabri Mustafa Sami Yavuz, Muhammet Steinbach, Eckehard Technical University of Munich School of Computation Information and Technology Department of Computer Engineering Media Technology Germany Infineon Technologies Ag Germany

ISBN: (纸本)9781728163277

Out-of-distribution (OOD) detection has recently received special attention due to its critical role in safely deploying modern deep learning (DL) architectures. This work proposes a reconstruction-based multi-class OOD detector that operates on radar range doppler images (RDIs). The detector aims to classify any moving object other than a person sitting, standing, or walking as OOD. We also provide a simple yet effective pre-processing technique to detect minor human body movements like breathing. The simple idea is called respiration detector (RESPD) and eases the OOD detection, especially for human sitting and standing classes. On our dataset collected by 60GHz short-range FMCW Radar, we achieve AUROCs of 97.45%, 92.13%, and 96.58% for sitting, standing, and walking classes, respectively. We perform extensive experiments and show that our method outperforms state-of-the-art (SOTA) OOD detection methods. Also, our pipeline performs 24 times faster than the second-best method and is very suitable for real-time processing. © 2023 IEEE.

关键词： 60GHz FMCW radar deep neural networks Out-of-distribution detection

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5th Artificial Intelligence and Cloud Computing Conference, AICCC 2022

作者： Elouali, Aya Mora, Higinio Gimeno, Francisco J. Mora Department of Computer Science Technology and Computation University of Alicante Alicante Spain

ISBN: (纸本)9781450398749

Edge-cloud collaborative processing for IoT data is a relatively new approach that tries to solve processing and network issues in IoT systems. It consists of splitting the processing done by a Neural Network model into edge part and cloud part in order to solve network, privacy and load issues. However, it also has it shortcomings such as the big size of the edge part's output that has to be transmitted to the cloud. In this paper, we are proposing a data transmission reduction method for edge-cloud collaborative solutions that is based on data similarities in stationary objects. The performed experiments proved that we were able to reduce 62% of the data sent. © 2022 ACM.

关键词： Data transfer

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Distance-Based Subset Selection With a Knee Point for Multi-Objective Optimization 13

Distance-Based Subset Selection With a Knee Point for Multi-...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Pang, Lie Meng Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350373332

Recently, the use of an unbounded external archive in the design of evolutionary multi-objective optimization (EMO) algorithms has received increasing attention. An important component in the use of an unbounded external archive is its subset selection method used to select a pre-specified number of solutions from the archive. One of the most commonly used subset selection method is distance-based subset selection (DSS) since it is very easy to calculate and can easily scale to many-objective optimization. However, the DSS's selection mechanism usually lead to a small hypervolume value for the selected subset even when the optimal subset with respect to the minimum distance among the selected solutions is obtained. In this paper, we propose a modification to the DSS algorithm by selecting a knee point as the first solution. The knee point is often a solution of interest to decision makers in the absence of preference information. Experimental results show that the inclusion of a knee point generally has a positive effect on improving the hypervolume value of the selected subset. © 2024 IEEE.

关键词： Optimization algorithms

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Analysis of Algorithm Comparison Results on Real-World Multi-Objective Problems 13

Analysis of Algorithm Comparison Results on Real-World Multi...

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13th IEEE Congress on Evolutionary computation, CEC 2024

作者： Pang, Lie Meng Ishibuchi, Hisao Shang, Ke Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350308365

Recently, several real-world multi-objective optimization problem suites have been proposed to facilitate the evaluation of the performance of evolutionary multi-objective optimization (EMO) algorithms. In spite of the importance of using real-world problems to evaluate EMO algorithms, their characteristics are not well understood compared to artificial test problems. Thus, there is a need to examine and understand the challenges posed by these real-world problems. In this study, we attempt to explore the characteristics of the most recently proposed real-world application suite (i.e., RWA suite). Six EMO algorithms are evaluated on the RWA suite, including three classic algorithms and three recently-proposed algorithms. Based on the performance comparison results, we systematically analyze the RWA suite in terms of convergence and diversity difficulties. © 2024 IEEE.

关键词： Optimization algorithms

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Performance Evaluation of Evolutionary Multi-Objective Algorithms Using Real-World Problems with an Additional Total Constraint Violation Objective 13

Performance Evaluation of Evolutionary Multi-Objective Algor...

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13th IEEE Congress on Evolutionary computation, CEC 2024

作者： Nan, Yang Ishibuchi, Hisao Shu, Tianye Southern University of Science and Technology Guangdong Provinial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350308365

In the community of evolutionary multi-objective optimization (EMO), one important issue is the choice of test problems for performance evaluations of EMO algorithms. This is because performance evaluation results of EMO algorithms totally depend on the choice of test problems. This means that the research on new EMO algorithm design is also influenced by the choice of test problems. Recently, researchers have started to use real-world problems for performance evaluation of EMO algorithms. Among them, a real-world problem suite RE has attracted much attention and has been used in many studies. However, most RE problems have been created from real-world constrained problems by using the total constraint violation as an additional objective. That is, the original versions of most RE problems are not unconstrained multi-objective problems. Thus, even when a good solution set is obtained by an EMO algorithm for an RE problem, it can be a poor solution set for its original constrained problem. This is because many well-distributed solutions over the entire Pareto front of the transformed unconstrained problem are usually infeasible solutions of the original constrained problem with some positive total constraint violation values. In this paper, we examine whether good solution sets obtained by EMO algorithms for RE problems are also good solution sets for their original constrained problems. Our experimental results show that good solutions sets for most RE problems include good feasible solution sets for their original constrained problems. However, for a few RE problems, good solution sets obtained by some high performance EMO algorithms do not include good feasible solution sets. Our results show that high-performance EMO algorithms on most RE problems generate good feasible solution sets for their original constrained versions. This observation supports the usefulness of those RE problems as test problems for performance evaluation of EMO algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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Challenges and Advancement in Federated Recommendation System: A Comprehensive Review 8th

Challenges and Advancement in Federated Recommendation Syste...

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8th International Conference on computers, Management and Mathematical Sciences, ICCM 2022

作者： Otari, Manisha S. Kumar, B. Suresh Patil, Mithun B. Research Scholar at School of Computation Computer Science and Engineering Sanjay Ghodawt University Maharashtra Kolhapur India School of Computation Computer Science & Engg. Sanjay Ghodawt University Maharashtra Kolhapur India Department of Computer Science & Engg N K Orchid College of Engg &Technology Maharashtra Solapur India

ISBN: (纸本)9783031511660

In the age of information overload, recommendation systems have become essential for individuals and society. Deep learning has been successful in developing high-quality recommendation systems, but limited training data can hinder their effectiveness. Collecting more data may compromise user privacy and security. To address this issue and improve recommendation quality, federated machine learning, and recommender systems have been fused into a new research area called federated recommender systems. While researchers have made progress in this area, gaps still exist. This review aims to (1) identify the common challenges faced by federated recommendation systems;(2) explore different types of federated recommendation systems based on architecture and machine learning models used;(3) conduct a comparative analysis of federated recommendation systems;(4) examine the communication efficiency, tools, and frameworks used for federated recommendation systems;and (5) suggest future research directions for practitioners in federated learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Recommender systems

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Argumentation Frameworks Induced by Assumption-based Argumentation: Relating Size and Complexity 20

Argumentation Frameworks Induced by Assumption-based Argumen...

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20th International Conference on Principles of Knowledge Representation and Reasoning, KR 2023

作者： Lehtonen, Tuomo Rapberger, Anna Ulbricht, Markus Wallner, Johannes P. University of Helsinki Department of Computer Science Finland TU Wien Institute of Logic and Computation Austria Leipzig University Department of Computer Science Germany Graz University of Technology Institute of Software Technology Austria

ISBN: (纸本)9781956792027

A key ingredient of computational argumentation in AI is the generation of arguments in favor of or against claims under scrutiny. In this paper we look at the complexity of argument construction and reasoning in the prominent structured formalism of assumption-based argumentation (ABA). We point out that reasoning in ABA by means of constructing an abstract argumentation framework (AF) gives rise to two main sources of complexity: (i) constructing the AF and (ii) reasoning within the constructed graph. Since both steps are intractable in general, it is no surprise that the best performing state-of-the-art ABA reasoners skip the instantiation procedure entirely and perform tasks directly on the input knowledge base. Driven by this observation, we identify and study atomic and symmetric ABA, two ABA fragments that preserve the expressive power of general ABA, and that can be utilized to have milder complexity in the first or second step. We show that using atomic ABA allows for an instantiation procedure for general ABA leading to polynomially-bounded AFs and that symmetric ABA can be used to create AFs that have mild complexity to reason on. By an experimental evaluation, we show that using the former approach with modern AF solvers can be competitive with state-of-the-art ABA solvers, improving on previous AF instantiation approaches that are hindered by intractable argument construction. © 2023 Proceedings of the International Conference on Knowledge Representation and Reasoning. All rights reserved

关键词： Knowledge representation

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The Structure of Data Privacy Compliance 3

The Structure of Data Privacy Compliance

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3rd International Workshop on Current Information Security and Compliance Issues in Information Systems Research, CIISR 2023

作者： Klymenko, Alexandra Meisenbacher, Stephen Matthes, Florian Technical University of Munich School of Computation Information and Technology Department of Computer Science Boltzmannstr. 3 Garching85748 Germany

Achieving Data Privacy Compliance involves a dynamic process requiring the expertise of many roles, particularly legal and technical experts, and it ultimately revolves around the goal of data protection, particularly in technical systems. While this goal may be clear, the inner workings and overall structure of the compliance process remain under-researched. In particular, the roles involved in the process of data privacy compliance and the nature of the interactions between them have not yet been investigated or formalized in a structured manner. In this work, we present such a structure, based on a series of interviews conducted with privacy professionals with varying responsibilities in compliance programs. © 2023 Copyright for this paper by its authors.

关键词： Data privacy

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