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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4574-4584页

作者： Fan, Zongwen Gou, Jin Weng, Shaoyuan College of Computer Science and Technology Huaqiao University Xiamen361021 China School of Software Engineering Xiamen Institute of Software Technology Xiamen361024 China

Concrete is a vital component in modern construction, prized for its strength, durability, and versatility. Accurately determining the quantities of concrete components is crucial in civil engineering applications to optimize resources (e.g., manpower and financial resources). In this article, we propose an unbiased fuzzy-weighted relative error support vector machine (UFW-RE-SVM) for reverse prediction of concrete components. First, we add an unbiased term to the target function of UFW-RE-SVM for obtaining an unbiased model. Second, we design a fuzzy-weighted operation to indicate sample importance by incorporating the fuzzy membership values into the UFW-RE-SVM. The nth root operation is introduced to address the exponential explosion issue in the fuzzy-weighted operation. Finally, considering the UFW-RE-SVM is sensitive to its hyperparameters for multioutput prediction, the whale optimization algorithm (WOA) is utilized for hyperparameter optimization for its effectiveness in optimization tasks. We design the fitness function based on the results from multiple components to balance the performance of multioutput predictions. Experimental results show that the performance of our proposed model outperforms existing works in predicting concrete components in terms of mean absolute relative error, standard deviation, and root mean square error. Further, the statistical test shows the WOA and two other metaheuristics can significantly improve the prediction performance. This indicates that the unbiased term, fuzzy-weighted operation, and WOA are effective for improving the proposed model for reverse prediction concrete components. With these promising results, the proposed model could provide decision-makers with a valuable tool for determining concrete component quantities based on desired concrete qualities. © 2020 IEEE.

关键词： Forecasting

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Topological Evaluation of Certain computer Networks by Contraharmonic-Quadratic Indices

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computers, Materials & Continua 2023年第2期74卷 3795-3810页

作者： Ahmed M.Alghamdi Khalid Hamid Muhammad Waseem Iqbal M.Usman Ashraf Abdullah Alshahrani Adel Alshamrani Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and Engineering at University of JeddahJeddah21493Saudi Arabia

In various fields,different networks are used,most of the time not of a single kind;but rather a mix of at least two *** kinds of networks are called bridge networks which are utilized in interconnection networks of PC,portable networks,spine of internet,networks engaged with advanced mechanics,power generation interconnection,bio-informatics and substance intensify *** number that can be entirely calculated by a graph is called graph *** mathematical graph invariants have been portrayed and utilized for connection investigation during the latest twenty ***,no trustworthy evaluation has been embraced to pick,how much these invariants are associated with a network graph or subatomic *** this paper,it will discuss three unmistakable varieties of bridge networks with an incredible capacity of assumption in the field of computer science,chemistry,physics,drug industry,informatics and arithmetic in setting with physical and manufactured developments and networks,since Contraharmonic-quadratic invariants(CQIs)are recently presented and have different figure qualities for different varieties of bridge graphs or *** study settled the geography of bridge graphs/networks of three novel sorts with two kinds of CQI and Quadratic-Contraharmonic Indices(QCIs).The deduced results can be used for the modeling of the above-mentioned networks.

关键词： Bridge networks invariants Quadratic-Contraharmonic Indices maple network graph molecular graph

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Exploring AI Innovations in Automated software Source Code Generation: Progress, Hurdles, and Future Paths

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第8期48卷 125-136页

作者： Odeh, Ayman Odeh, Nada Software Engineering and Computer Science Department College of Engineering Al Ain University Al Jimi Al Ain United Arab Emirates

In today's dynamic world of software development, the demand for efficient and rapid creation of high-quality code has never been more pronounced. Automated software source code generation (ASSCG) emerges as a compelling solution to meet this demand, offering significant advantages in terms of speed, accuracy, and scalability. This paper aims to explore the critical role of automated software source code generation and its profound significance in modern software development practices. By navigating through the intersection of ASSCG, AI innovations, and the challenges therein, this paper endeavors to provide a comprehensive understanding of this transformative field and pave the way for informed decision-making and advancements in software development practices. This paper delves into the critical role of ASSCG and its transformative impact on modern software development. In this work, we endeavor to delve into the multifaceted landscape of automated code generation, assessing its significance, the transformative potential of AI innovations, and the challenges and objectives inherent in this evolving domain. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Deep learning

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Dealing with Noisy Data in Federated Learning: An Incentive Mechanism with Flexible Pricing 25

Dealing with Noisy Data in Federated Learning: An Incentive ...

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34th ACM Web Conference, WWW 2025

作者： Wang, Hengzhi Chen, Haoran Ma, Minghe Cui, Laizhong Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798400712746

Federated Learning (FL) has emerged as a promising training framework that enables a server to effectively train a global model by coordinating multiple devices, i.e., clients, without sharing their raw data. Keeping data locally can ensure data privacy, but also makes the server difficult to assess data quality, leading to the noisy data issue. Specifically, for any given training task, only a portion of each client’s data is relevant and beneficial, while the rest may be redundant or noisy. Training with excessive noisy data can degrade performance. Motivated by this, we investigate the limitations of existing studies and develop an incentive mechanism with flexible pricing tailored for noisy data settings. The insight lies in mitigating the impact of noisy data by selecting appropriate clients and incentivizing them to clean their data spontaneously. Further, both rigorous theoretical analysis and extensive simulations compared with state-of-the-art methods have been well-conducted to validate the effectiveness of the proposed mechanism. © 2025 Copyright held by the owner/author(s).

关键词： Federated learning

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Flood Inundation Range Prediction Method Based on SRR-Informer 13th

Flood Inundation Range Prediction Method Based on SRR-Inform...

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13th International Conference on AI and Multimodal Services, AIMS 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Liu, Han Chen, Zhihao Sun, Qi College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031776809

Flood forecasting methods based on deep learning rely on a large number of observational data, and are facing serious challenges in areas with scarce data. Aiming at the problems of flood inundated range prediction in areas with scarce data, this paper proposes a flood inundated range prediction method based on spatial reduction reconstruction (SRR) and improved deep attention neural network (Informer) to reduce the data requirements of the two core parts of water level simulation and spatial modeling. It alleviates the problem of insufficient observational data for flood inundation range prediction in areas with scarce data. This method obtains the dependency relationship of long time series data through Informer model, and inserts the built-in input selection layer to reduce the number of parameters in the model, reducing the data requirement of water level simulation. At the same time, the SRR algorithm is used to select the representative locations that are easy to be inundated in the basin, which reduces the number of locations required for spatial modeling of flood inundation range and the corresponding data requirements. The experimental results show that this method can improve the accuracy of flood inundation range prediction and speed up the efficiency of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Spectral Reconstruction via Dual Cross-Scanning and Cross-Attention Mechanisms 14

Spectral Reconstruction via Dual Cross-Scanning and Cross-At...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Lin, Mingying Mo, Ziqian Zhang, Hongtao Fu, Xiyou Xu, Meng College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9798331513139

Hyperspectral images (HSIs) provide rich spectral information, but acquiring high-resolution data is costly and challenging, making spectral super-resolution essential. Inspired by the near-linear efficiency of state space models (SSM) in long-sequence tasks, we propose a spectral reconstruction method based on dual cross-scanning and cross-attention mechanisms (SR-CSCA). This novel network integrates Transformer and Mamba architectures, efficiently extracting multi-resolution contextual information and optimizing computational strategies to enhance spectral reconstruction performance. SR-CSCA method captures spectral and spatial details and dependencies by employing an optimized joint spatial-spectral dimensional scanning strategy. Furthermore, the cross-attention mechanism enhances feature fusion and information extraction. The efficacy of our approach is demonstrated through comparisons with multiple benchmark methods for spectral reconstruction. Experimental results indicate that SR-CSCA achieves state-of-the-art performance in spectral reconstruction while maintaining a low computational cost. © 2024 IEEE.

关键词： Hyperspectral imaging

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Human Action Recognition in Multi-Level Convolutional Temporal Attention Network

Human Action Recognition in Multi-Level Convolutional Tempor...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Chen, ZhongQi Li, Chang Qian, WeiWen College of Computer Science and Software Engineering Hohai University NanJing China

ISBN: (纸本)9798350368741

Human Action Recognition (HAR) has widespread applications in areas such as human-computer interaction, elderly care, and home healthcare. However, current sensor-based HAR faces challenges of low fine-grained recognition performance and difficulty in distinguishing similar actions. To solve this problem, this paper proposes a model based on Multilevel Convolutional Time Series Attention Network (MCTSANet). By Multi-ResCNN to pay attention to different levels of features and using Time Series Attention (TSA) to pay attention to the more important data in the channel, so as to improve the ability of confusable action *** on three public datasets show that the proposed method outperforms state-of-the-art sensor-based HAR approaches. © 2025 IEEE.

关键词： Attention Mechanism Deep Learning Human Action Recognition Wearable Sensors

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Application and Optimization of Multi-agent Reinforcement Learning in Collaborative Decision-Making 8th

Application and Optimization of Multi-agent Reinforcement Le...

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Sun, Qi Chen, Zhihao Liu, Han College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031779534

With the rapid development of intelligent systems, Multi-Agent Systems (MAS) have shown unique advantages in solving complex decision-making problems. Particularly in the field of Multi-Agent Reinforcement Learning (MARL), Multiple agents can decompose complex tasks, process information and make decisions in parallel, share experiences, accelerate the learning process, and significantly improve decision quality and efficiency. This paper explores the theoretical underpinnings of MARL and its application to collaborative decision-making and analyzes practical cases in areas such as transportation system management, automated manufacturing, and smart grids. Additionally, it addresses challenges in strategy coordination, handling dynamic environments, and improving learning efficiency. This paper proposes several optimization strategies and introduces reservoir group optimization experiments. By comparing with single-agent algorithms, it verifies that multi-agent systems can coordinate multiple reservoirs, enhance convergence speed, and achieve higher power generation efficiency, demonstrating better practical application prospects. Furthermore, the future trends of MARL, including technological advancements, potential applications, and challenges, are discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reinforcement learning

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EchoGCN: An Echo Graph Convolutional Network for Skeleton-Based Action Recognition 27th

EchoGCN: An Echo Graph Convolutional Network for Skeleton-Ba...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Qian, Weiwen Huang, Qian Li, Chang Chen, Zhongqi Mao, Yingchi College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031783531

Graph Convolutional Networks (GCNs) have attracted considerable attention in the realm of human action recognition. However, conventional GCNs-based methods typically struggle to construct adjacency matrices that capture diverse semantics, thus leading to performance limitations. To tackle this issue, we propose the Echo Graph as a set of adjacency matrices, which includes both hierarchical and global graphs. Specifically, our hierarchical graph exploits the hierarchical information based on the selected central broadcast nodes, aggregating joints dispersed across considerable physical distances into a unified semantic space. The global graph we construct transcends the limitations imposed by physically defined topological structures, delving into comprehensive information exchange among nodes. Additionally, we propose node activation and hierarchical activation model. These activations aim to prominently highlight crucial nodes and edges for specific samples. Finally, we incorporate Margin ReLU distillation to improve computational efficiency and design a four-stream integration using only the joint and bone data streams from two central broadcast nodes. Based on the aforementioned components, we propose EchoGCN, capable of extracting representative skeleton features. Experimental results on three datasets (NTU-RGB+D 60, NTU-RGB+D 120, and NW-UCLA) demonstrate that our model achieves state-of-the-art performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Musculoskeletal system

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SSTHyper: Sparse Spectral Transformer for Hyperspectral Image Reconstruction 17th

SSTHyper: Sparse Spectral Transformer for Hyperspectral Ima...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Xu, Meng Lin, Mingying Ren, Qi Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9789819609109

Transformer-based methods have improved the quality of hyperspectral images (HSIs) reconstructed from RGB by effectively capturing their remote relationships. The self-attention mechanisms in existing Transformer models have not fully considered the spatial sparsity and spectral continuity characteristics of HSIs and fail to effectively filter out significant features, resulting in lower-quality reconstruction results. This paper proposes a sparse spectral transformer model for HSI reconstruction (SSTHyper) to address this limitation, adaptively preserving crucial features. The network consists of an encoder-decoder structure capable of learning shallow and deep spatial-spectral priors, primarily composed of sparse spectral self-attention groups. Introducing a sparse spectral self-attention mechanism allows adaptive masking of non-significant details, enhancing reconstruction accuracy. Meanwhile, a lightweight cross-level fusion network is proposed to reduce model parameters and computational costs to enhance spatial-spectral feature extraction. Experimental results on two benchmark datasets demonstrate the outstanding performance of the proposed method. The code will be released at SSTHyper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image reconstruction

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