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Corrigendum to ‘Device-to-device communication in 5G heterogeneous network based on game-theoretic approaches: A comprehensive survey’ [J. Netw. Comput., Volume 238, June 2025, 104152] (Journal of Network and computer Applications (2025) 238, (S1084804525000499), (10.1016/***.2025.104152))

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Journal of Network and computer Applications 2025年 239卷

作者： Ahmad, Rana Zeeshan Rizwan, Muhammad Yousuf, Muhammad Jehanzaib Khan, Mohammad Bilal Almadhor, Ahmad Gadekallu, Thippa Reddy Abbas, Sidra Department of Information Technology University of Sialkot Sialkot Pakistan College of Science and Engineering University of Derby United Kingdom Software Research Institute Athlone Institute of Technology Ireland School of Electronic Engineering Xidian University Xidian China Department of Computer Engineering and Networks College of Computer and Information Science Jouf University Sakaka Saudi Arabia The College of Mathematics and Computer Science Zhejiang A&F University Hangzhou311300 China Division of Research and Development Lovely Professional University Phagwara India Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Science COMSATS University Islamabad Sahiwal Pakistan

The authors regret the mistake in affiliation of Author Sidra Abbas. The correct affiliation is as follows: Sidra AbbasDepartment of computer Science, COMSATS University Islamabad, Sahiwal, Pakistansidraabbas@***:sidr...

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Effective Entry-Wise Flow for Molecule Generation

Effective Entry-Wise Flow for Molecule Generation

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International Conference on Data engineering

作者： Qifan Zhang Junjie Yao Yuquan Yang Yizhou Shi Wei Gao Xiaoling Wang School of Computer Science and Technology East China Normal University Software Engineering Institute East China Normal University Shanghai Key Laboratory of Trustworthy Computing Department of Chemistry University of Massachusetts Boston University of Michigan Ann Arbor.

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Molecule generation is a critical process in the fields of drug discovery and materials science. Recently, generative models based on normalizing flows have demonstrated significant potential in this domain. These models are particularly suited for handling the symmetrical and complex chemical structures often encountered in molecular datasets. Despite their promising nature, normalizing flow-based models for molecule generation face considerable challenges. The complexity of molecule representation, the rigorous demands of optimization, and the scarcity of training labels in molecular datasets contribute to these difficulties. Additionally, adequately and comprehensively learning the distribution of molecular datasets remains a formidable task. In this paper, we delve into the intricate entry-wise modules in vanilla flows, introducing an effective variation of flow-based models. Our proposed approach innovatively encapsulates affine coupling transformations within normalizing flows. Furthermore, we deconstruct existing invertible flow models, integrating them with newly developed entry-wise transformations. Our experimental studies demonstrate that these proposed entry-wise modules, when incorporated into standard flow-based models, surpass other generative models in performance on various representative datasets and generation tasks. Notably, in the context of low-resourced molecular graph generation, our model achieves remarkable performance compared to its counterparts.

关键词： Training Couplings Materials science and technology Interpolation Data engineering Drug discovery Task analysis

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Secure parallel Outsourcing Scheme for Large-scale Matrix Multiplication on Distributed Cloud Servers

Secure parallel Outsourcing Scheme for Large-scale Matrix Mu...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yinlong Wang Yunting Tao Fanyu Kong Zhaoquan Gu Jia Yu Hanlin Zhang School of Software Shandong University Jinan Shandong China College of Information Engineering Binzhou Polytechnic Binzhou Shandong China Shandong Sansec Information Technology Co. Ltd Jinan China School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen China College of Computer Science and Technology Qingdao University Qingdao Shandong China

Large-scale matrix multiplication is a computational bottleneck in various applications including artificial intelligence and machine learning. Given the time complexity of O(n 3 ) for matrix multiplication, large matrix computation is exceedingly time-consuming for the client-side user. By outsourcing this task to cloud servers with substantial computational resources, we can significantly reduce the client-side computational time. This paper presents a parallel matrix multiplication outsourcing scheme based on Cannon’s algorithm. By distributing the matrix across multiple cloud servers for parallel computation, we can get a significant efficiency speedup. Our scheme employs multiple cloud servers to perform parallel matrix computation, reducing the computational load by 89-97% when utilizing 4-16 servers as opposed to using a single server. We provide a comprehensive analysis of the scheme’s correctness, security, and verifiability, substantiating the benefits of our approach through the experimental data.

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DDA: A Dynamic Difficulty-aware Data Augmenter for Image Super-resolution

DDA: A Dynamic Difficulty-aware Data Augmenter for Image Sup...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xinyi Zhang Tao Dai Bin Chen Shu-Tao Xia Tsinghua Shenzhen International Graduate School Tsinghua University College of Computer Science and Software Engineering Shenzhen University Harbin Institute of Technology Shenzhen Peng Cheng Laboratory Research Center of Artificial Intelligence

Deep neural networks (DNNs) have been recently widely used in image super-resolution (SR) and have achieved remarkable performance. However, most existing methods focus on elaborate network design, while rarely considering the training strategy, which affects the model performance and training efficiency. In practice, most SR methods still train the networks with the commonly-used data augmentation (e.g., random crop and sampling), which is shown to converge slowly for deep SR networks. To address this issue, in this paper, we propose a dynamic difficulty-aware data augmenter, named DDA, by considering the restoration difficulty and distribution of input patches. Our DDA mainly consists of difficulty-aware divider, dynamic sampler, and adaptive re-weighter. Specifically, our DDA first uses the difficulty-aware divider to divide the input image into small over-lapping patches, followed by classification into $N$ different classes based on the restoration difficulty. Next, dynamic sampler samples the training patches from each class with probability based on training loss. Furthermore, to remedy the imbalance of training patches between different classes, adaptive re-weighter updates the weight of each training patch according to the accumulated training loss. Extensive experiments demonstrate the effectiveness of our DDA on different SR methods by improving training efficiency and model performance across a wide range of scenarios.

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Model-Driven Intelligent Management: Constructing a New Framework for Efficient engineering Safety Monitoring

Model-Driven Intelligent Management: Constructing a New Fram...

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computer technology and Information Science (ISCTIS), International Symposium on

作者： Hong Li Fei Wang Shaoxun Deng Research and Development Department Guizhou Bit Software Co. Ltd. Guiyang China Power China Guiyang Engineering Co. Ltd. Institute of Engineering Safety Science Guiyang China School of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In the context of ensuring operational safety for reservoir dams and other vital infrastructures, engineering safety monitoring systems play an essential role, functioning as a core component in assessing and guaranteeing structural health. Traditional monitoring systems, however, have been constrained by their reliance on data architectures tailored to specific manufacturers' devices, limiting system scalability and maintainability. This paper introduces an innovative strategy aimed at eliminating differences among instruments, achieving an integrated process from data acquisition, processing, analysis to report *** study adopts JSON format to standardize instrument models, incorporating abstract design principles that define not only fundamental parameters and data fields but also specific computational logic to accommodate diverse monitoring needs. particularly for complex instruments like multipoint displacement meters, the system introduces the concept of virtual instrument types, realizing advanced functionality through combinations of basic instrument models. Furthermore, by employing predefined templates for data visualization, graphics, and analysis, the system can flexibly adapt to various monitoring *** approach has been successfully implemented in the engineering safety monitoring projects of China Power Construction Group Guiyang institute, covering over 82 dams, foundation pits, and other structures, processing more than 20 million monitoring records, significantly enhancing monitoring efficiency and data analysis quality. While the current system operates effectively with relational databases, future research aims to explore its potential within a Bson and NoSQL environment, further boosting scalability and flexibility.

关键词： Meters Dams Instruments Computational modeling Scalability Relational databases Reservoirs Safety Logic Monitoring

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Invertible Residual Rescaling Models

arXiv

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arXiv 2024年

作者： Li, Jinmin Dai, Tao Zha, Yaohua Luo, Yilu Lu, Longfei Chen, Bin Wang, Zhi Xia, Shu-Tao Zhang, Jingyun Tsinghua Shenzhen International Graduate School Tsinghua University China College of Computer Science and Software Engineering Shenzhen University China Research Center of Artificial Intelligence Peng Cheng Laboratory China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China WeChat Pay Lab33 Tencent China

Invertible Rescaling Networks (IRNs) and their variants have witnessed remarkable achievements in various image processing tasks like image rescaling. However, we observe that IRNs with deeper networks are difficult to train, thus hindering the representational ability of IRNs. To address this issue, we propose Invertible Residual Rescaling Models (IRRM) for image rescaling by learning a bijection between a high-resolution image and its low-resolution counterpart with a specific distribution. Specifically, we propose IRRM to build a deep network, which contains several Residual Downscaling Modules (RDMs) with long skip connections. Each RDM consists of several Invertible Residual Blocks (IRBs) with short connections. In this way, RDM allows rich low-frequency information to be bypassed by skip connections and forces models to focus on extracting high-frequency information from the image. Extensive experiments show that our IRRM performs significantly better than other state-of-the-art methods with much fewer parameters and complexity. Particularly, our IRRM has respectively PSNR gains of at least 0.3 dB over HCFlow and IRN in the ×4 rescaling while only using 60% parameters and 50% FLOPs. The code will be available at https://***/THUKingmin/IRRM. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

TechRxiv

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TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning

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Blockchain-based New Energy Grid Electric Energy Measurement Transaction System

Blockchain-based New Energy Grid Electric Energy Measurement...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Wei Chen Chengfu Sun School of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

With the maturity of hlockchain technology, it has heen employed in many fields. This article aims to apply hlockchain technology to the real-time electric power transaction of alternative energy sources. A new energy grid electricity measurement transaction system based on hlockchain is designed. First, the architecture design of the system is introduced. Second, the smart contracts are deployed in the private hlockchain to realize the basic power measurement and trading. The real-time active and reactive powers are measured by smart meters which connect to the smart contracts with the MQTT protocol. Data is uploaded to the hlockchain and the trading can be completed automatically. Finally, the comparison with the traditional power trading system shows the advantages of the hlockchain based electricity transaction system.

关键词： Visualization Smart contracts Energy measurement Blockchain Real-time systems Smart meters Servers

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Optimizing for the Shortest Path in Denoising Diffusion Model

arXiv

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arXiv 2025年

作者： Chen, Ping Zhang, Xingpeng Liu, Zhaoxiang Hu, Huan Liu, Xiang Wang, Kai Wang, Min Qian, Yanlin Lian, Shiguo Data Science & Artificial Intelligence Research Institute China Unicom China Unicom Data Intelligence China Unicom China DJI Technology Co. Ltd. China School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China

In this research, we propose a novel denoising diffusion model based on shortest-path modeling that optimizes residual propagation to enhance both denoising efficiency and quality. Drawing on Denoising Diffusion Implicit Models (DDIM) and insights from graph theory, our model, termed the Shortest Path Diffusion Model (ShortDF), treats the denoising process as a shortest-path problem aimed at minimizing reconstruction error. By optimizing the initial residuals, we improve the efficiency of the reverse diffusion process and the quality of the generated samples. Extensive experiments on multiple standard benchmarks demonstrate that ShortDF significantly reduces diffusion time (or steps) while enhancing the visual fidelity of generated samples compared to prior arts. This work, we suppose, paves the way for interactive diffusion-based applications and establishes a foundation for rapid data generation. Code is available at https://***/UnicomAI/ShortDF. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph theory

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