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Constrained Multi-objective Optimization Based on Dynamic Priority and Cooperative Offspring Generation

IEEE Transactions on Evolutionary Computation 2025年

作者： He, Zhihui Wang, Feng Li, Bingdong Zhou, aimin Wuhan University School of Computer Science Hubei 430072 China East China Normal University Shanghai Frontiers Science Center of Molecule Intelligent Syntheses Shanghai Institute of AI for Education School of Computer Science and Technology Shanghai200062 China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science China

As the number and complexity of constraints in constrained multi-objective optimization problems (CMOPs) increase, the performance of existing constrained multi-objective evolutionary algorithms (CMOEAs) declines significantly. A novel idea is to sequentially address each constraint based on priority, effectively reducing the complexity of CMOPs. However, in these algorithms, the constraint-handling priority is determined statically in the initial stage. This may lead to inappropriate determination of constraint-handling priority since accurately estimating the constraint landscape in the initial stage is quite challenging. Moreover, these algorithms tackle constraints separately, neglecting the potential for inter-constraint cooperation and thus compromising their efficiency in constraint handling. Thus, we propose a constrained multi-objective evolutionary algorithm based on dynamic priority and cooperative offspring generation called DPCMOEA. Firstly, the constraint-handling priority is determined dynamically by the estimated inconsistency degree (EID) between the Pareto fronts of the candidate constraints and the current population. Secondly, computational resources are automatically allocated to each constraint according to EID based constraint relationship analysis. Finally, a new offspring generation strategy based on constraint cooperation is designed to enhance the quality of new solutions. Experimental results on six CMOP test suites demonstrate that DPCMOEA outperforms six state-of-the-art algorithms. © 1997-2012 IEEE.

关键词： Optimization algorithms

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UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designs 25

UICopilot: Automating UI Synthesis via Hierarchical Code Gen...

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34th ACM Web Conference, WWW 2025

作者： Gui, Yi Wan, Yao Li, Zhen Zhang, Zhongyi Chen, Dongping Zhang, Hongyu Su, Yi Chen, Bohua Zhou, Xing Jiang, Wenbin Zhang, Xiangliang Huazhong University of Science and Technology Wuhan China Chongqing University Chongqing China Hubei University of Automotive Technology Shiyan China Rabbitpre AI Shenzhen China University of Notre Dame Notre Dame United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology China

ISBN: (纸本)9798400712746

Automating the synthesis of User Interfaces (UIs) plays a crucial role in enhancing productivity and accelerating the development lifecycle, reducing both development time and manual effort. Recently, the rapid development of Multimodal Large Language Models (MLLMs) has made it possible to generate front-end Hypertext Markup Language (HTML) code directly from webpage designs. However, real-world webpages encompass not only a diverse array of HTML tags but also complex stylesheets, resulting in significantly lengthy code. The lengthy code poses challenges for the performance and efficiency of MLLMs, especially in capturing the structural information of UI designs. To address these challenges, this paper proposes UICopilot, a novel approach to automating UI synthesis via hierarchical code generation from webpage designs. The core idea of UICopilot is to decompose the generation process into two stages: first, generating the coarse-grained HTML hierarchical structure, followed by the generation of fine-grained code. To validate the effectiveness of UICopilot, we conduct experiments on a real-world dataset, i.e., WebCode2M. Experimental results demonstrate that UICopilot significantly outperforms existing baselines in both automatic evaluation metrics and human evaluations. Specifically, statistical analysis reveals that the majority of human annotators prefer the webpages generated by UICopilot over those produced by GPT-4V. © 2025 Copyright held by the owner/author(s).

关键词： Web Design

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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

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IEEE Transactions on computer-aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

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The Whu Wake Word Lipreading System for the 2024 Chat-Scenario Chinese Lipreading Challenge

The Whu Wake Word Lipreading System for the 2024 Chat-Scenar...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Haoxu Wang Cancan Li Fei Su Juan Liu Hongbin Suo Ming Li School of Computer Science Wuhan University Wuhan China AI Center OPPO Beijing China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Data Science Research Center Duke Kunshan University Kunshan China

ISBN: (数字)9798350379815

ISBN: (纸本)9798350379822

The paper describes the Wake Word Lipreading system developed by the WHU team for the ChatCLR Challenge 2024. Although Lipreading and Wake Word Spotting have seen significant development, exploration of pretrained frontends for Wake Word Lipreading (WWL) remains insufficient. Our system is built upon a pretrained frontend and Transformer-liked backend architecture, incorporating Attentive Pooling and a Classifier. We investigate the effectiveness of different frontends, including Auto-AVSR and AV-Hubert, and evaluate the performance of Conformer and E-Branchformer backends. Additionally, we introduce Multi-layer Feature Aggregation to leverage features from multiple encoder block layers, demonstrating its effectiveness. Finally, we apply various fusion strategies, leading to score fusion that achieved a false reject rate of 8.21% and a false alarm rate of 8.50% along with a WWS score of 16.71% on the evaluation set, and obtain the first place in the task 1 of the ChatCLR Challenge.

关键词： Conferences Transformers Task analysis

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Latency and Secrecy Performance Analysis in Backscatter-Enabled Vehicular Cloudlet Networks

Latency and Secrecy Performance Analysis in Backscatter-Enab...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Nguyen, Van-Long Tien Bui, Thi My Phan, Van-Binh Ha, Dac-Binh School of Computer Science Duy Tan University Da Nang Viet Nam Quy Nhon AI Center FPT Software Science Avenue Binh Dinh Province Quy Nhon City Viet Nam Kien Giang Vocational College Faculty of Basic Sciences Viet Nam School of Engineering and Technology Duy Tan University Da Nang Viet Nam

ISBN: (纸本)9798331530839

In this work, we investigate the joint secrecy and latency performance of a multi-user backscatter communication (BC)-enabled vehicular network in the presence of a passive eavesdropper. Specifically, a number of vehicles intend to offload their tasks to a vehicle equipped with a mobile edge computing (MEC) server by utilizing dedicated BC communication and time division multiple access (TDMA) schemes. Meanwhile, a passive mobile eavesdropper attempts to intercept information from each user without any attacks. In order to investigate the performance of this system, we derive the closed-form expressions of the successful computation probability (SCP) and secrecy successful computation probability (SSCP) in two corresponding scenarios: with and without eavesdropper. In addition, the impacts of key parameters on the system's behavior to gain insight into its performance are also examined. Finally, the numerical results using Monte-Carlo simulations are provided to validate our analysis. © 2024 IEEE.

关键词： Time division multiple access

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Find truth in the hands of the few:acquiring specific knowledge with crowdsourcing

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Frontiers of computer science 2021年第4期15卷 5-16页

作者： Tao HAN Hailong SUN Yangqiu SONG Yili FANG Xudong LIU SKLSDE Lab School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyClearwater BayHong Kong 999077China School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful ***,when we aggregate the crowd knowledge based on the currently developed voting algorithms,it often results in common knowledge that may not be *** this paper,we consider the problem of collecting specific knowledge via *** the help of using external knowledge base such as WordNet,we incorporate the semantic relations between the alternative answers into a probabilisticmodel to determine which answer is more *** formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption,and solve it by the expectation-maximization(EM)*** increase algorithm compatibility,we also refine our method into semi-supervised *** results show that our approach is robust with hyper-parameters and achieves better improvement thanmajority voting and other algorithms when more specific answers are expected,especially for sparse data.

关键词： crowdsourcing knowledge acquisition EM algorithm label aggregation

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Pheromone Matrix Eigenvalue-based Convergence Assessment Indicator for Ant Colony Optimization 11

Pheromone Matrix Eigenvalue-based Convergence Assessment Ind...

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11th International Conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Li, Xu Shi, Lin Li, Jian-Yu Xu, Jing Zhan, Zhi-Hui South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Guangdong University of Finance and Economics Research Center of Intelligent Computing & Big Data Technology Guangdong Guangzhou510320 China Nankai University College of Artificial Intelligence Tianjin300350 China

ISBN: (纸本)9798350385670

As a crucial member of the evolutionary computation (EC), ant colony optimization (ACO) converges to the optimal result through a continuous process of iterative optimization. However, determining the convergence status of an ant colony remains a challenging task without well-established methods. To this end, this paper proposes a global evaluation indicator - average absolute eigenvalue of pheromone matrix (AAEPM) by constructing a pheromone matrix and analyzing its eigenvalue variations. This indicator aims to assess the convergence status of the ant colony during the evolutionary process. Experimental results show that the AAEPM is correlated with the convergence status of ACO and is not significantly influenced by algorithm parameters and problem scale. © 2024 IEEE.

关键词： Anthropogenic

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OneDConv: Generalized Convolution for Transform-Invariant Representation

OneDConv: Generalized Convolution for Transform-Invariant Re...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Weng, Haohan Chen, C. L. Philip Yi, Ke Liu, Haiqi Zhang, Tong The authors are with the Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition the School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China and is with the Pazhou Lab Guangzhou 510335 China and is with Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China.

ISBN: (纸本)9781665410205

Convolutional Neural Networks (CNNs) have ex-hibited great power in various vision tasks. However, the lack of transform-invariant property limits their further applications in complicated real-world scenarios. In this work, we pro-posed a novel generalized one-dimension convolutional operator (OneDConv), which dynamically transforms the convolution kernels based on the input features in a computationally and parametrically efficient manner. The proposed operator can extract the transform-invariant features naturally. It improves the robustness and generalization of convolution without sac-rificing the performance of common images. The proposed OneDConv operator can substitute the vanilla convolution. Thus, it can readily be incorporated into popular convolutional architectures, supporting end-to-end training. Empirical evaluations on popular benchmarks reveal OneDConv's superior performance over the standard convolution and competitive models in handling canonical and distorted images. © 2024 IEEE.

关键词： Convolution

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAai Conference on Artificial Intelligence, AAai 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

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Dual-Domain Attention Based Adaptive Graph Convolutional Network for EEG Emotion Recognition

Dual-Domain Attention Based Adaptive Graph Convolutional Net...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Xu, Tie Zhang, Tong Chen, Bianna Chen, C.L. Philip All authors are with the Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition the School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China and are with the Pazhou Lab Guangzhou 510335 China and are with Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China.

ISBN: (纸本)9781665410205

The asymmetry of emotional responses is observed in electroencephalogram (EEG) of different frequency bands across various spatial brain regions in neuroscience research. Many prior works have primarily emphasized the dependencies among channels in the spatial domain, neglecting the dynamic interaction of EEG in both spatial and frequency domains, which may limit the performance of EEG emotion recognition. To address these issues, we propose the dual-domain attention based adaptive graph convolutional network (DDA-AGCN) for EEG emotion recognition. Specifically, we propose the lightweight dual-domain attention mechanism (DDA) based on random vector similarity measurement and the squeezeexcitation technique to capture important characteristics in the channel and frequency domain respectively. Furthermore, the adaptive graph convolutional network (AGCN) is utilized to adaptively filter and refine low signal-to-noise ratio EEG data, while also learning the dynamic connectivity patterns among important EEG channels and extracting higher-level abstract features for emotion recognition tasks. To validate the effectiveness of the proposed method, experimental comparisons were conducted on SEED, SEED-IV, and MPED. The experimental results show that our method achieves highly competitive classification performance compared to existing methods. Moreover, under fair comparison, the DDA demonstrates better performance and computational efficiency than self-attention. © 2024 IEEE.

关键词： Electroencephalography

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