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Image Representations of Numerical Simulations for Training Neural Networks

computer Modeling in engineering & sciences 2023年第2期134卷 821-833页

作者： Yiming Zhang Zhiran Gao Xueya Wang Qi Liu School of Civil and Transportation Engineering Hebei University of TechnologyTianjin300401China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China

A large amount of data can partly assure good fitting quality for the trained neural *** the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality *** the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation ***,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and *** this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by *** temporal and spatial information is kept and the data are greatly *** addition,the results are readable for not only computers but also human *** examples are given,indicating the effectiveness of the proposed strategy.

关键词： Numerical simulations neural network pre-/post-processing data compression

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A noval approach for dual probabilistic linguistic multi-criteria decision making based on prioritized Maclaurin symmetric mean operators and VIKOR

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Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 447-462页

作者： Guo, Jing Zhu, Xianjun Li, Wenfeng Li, Hui School of Software Engineering Jinling Institute of Technology Nanjing China School of Engineering Management Nanjing University Nanjing China Reproductive Medicine Center Jinling Hospital Nanjing China Research Center for New Technology in Intelligent Equipment Nanjing University Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

The evolving landscape of decision-making, especially in complex scenarios, poses a challenge in accurately capturing decision-makers’ cognitive information. This challenge becomes even more intricate in group decision-making situations due to the diverse cognitive perspectives of the participants. In response to these complexities, this study delves into the realm of dual probabilistic linguistic 2-tuple sets, an extension of dual-hesitant fuzzy sets known for their effectiveness in handling perplexing data. Dual probabilistic linguistic 2-tuple sets introduces a nuanced approach, considering multiple linguistic memberships, non-membership degrees, and their associated probabilistic and deviation information. We have devised diverse mechanisms aimed at augmenting the comprehension and utilization of dual probabilistic linguistic 2-tuple sets. These mechanisms encompass transformation functions, scoring methodologies and accuracy metrics. In this study, we introduce two aggregation operators for dual probabilistic linguistic 2-tuple sets based on prioritized averaging and Maclaurin symmetric mean principles. These operators serve as the cornerstone for a pioneering hybrid approach meticulously crafted for multiple criteria decision making within the framework of dual probabilistic linguistic 2-tuple sets. To demonstrate the practicality and efficacy of our approach, we present a real-world case study. The subsequent analytical comparison and discussion underscore the effectiveness of our methodology. The outcomes of this study not only showcase the excellent performance of our proposed method but also offer valuable practical implications for decision-makers navigating complex and diverse decision environments. Our work stands as a significant stride toward enhancing decision-making processes amid contemporary challenges. © 2025 – IOS Press. All rights reserved.

关键词： Fuzzy rules

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Data Security Storage Mechanism Based on Blockchain Network

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computers, Materials & Continua 2023年第3期74卷 4933-4950页

作者： Jin Wang WeiOu Wenhai Wang RSimon Sherratt Yongjun Ren Xiaofeng Yu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Control Science and Engineering Zhejiang UniversityHangzhou310058China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China School of Business Nanjing UniversityNanjing210093China

With the rapid development of information technology,the development of blockchain technology has also been deeply *** performing block verification in the blockchain network,if all transactions are verified on the chain,this will cause the accumulation of data on the chain,resulting in data storage *** the same time,the security of data is also challenged,which will put enormous pressure on the block,resulting in extremely low communication efficiency of the *** traditional blockchain system uses theMerkle Tree method to store *** verifying the integrity and correctness of the data,the amount of proof is large,and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency,which will cause end-to-end communication delays and seriously affect the blockchain system’s stability,efficiency,and *** order to solve this problem,this paper proposes to replace the Merkle tree with polynomial commitments,which take advantage of the properties of polynomials to reduce the proof size and communication *** realizing the ingenious use of aggregated proof and smart contracts,the verification efficiency of blocks is improved,and the pressure of node communication is reduced.

关键词： Blockchain cryptographic commitment smart contract data storage

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The Distributed Intelligent Collaboration to AAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年第12期12卷 21473-21487页

作者： Yi, Meng Lee, Vincent C. S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS & AI ClaytonVIC3800 Australia Xi’an University of Technology School of Computer Science and Engineering Xi’an710048 China Southeast University School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Southeast University Suzhou215123 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying autonomous aerial vehicles (AAVs) as aerial base stations enhances the coverage and performance of communication networks in vehicular edge computing scenarios. However, due to the limited communication range and energy capacity of AAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of AAVs and efficient task offloading remains a significant challenge. To address this problem, the AAV-assisted two-stage intelligent collaboration (UTIC) method is proposed in this article to tackle the joint position optimization and task scheduling issue. First, a AAV-assisted two-stage task scheduling system model is designed to optimize the allocation process. Second, an Enhanced Particle Swarm Optimization algorithm is designed to determine the optimal positions of AAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of AAVs. Third, deep deterministic policy gradient method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in AAV deployment and outperform three other classical reinforcement learning algorithms in terms of reducing system cost. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Research on Lightweight Sensing technology Based on Single-Antenna Multicarrier

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IEEE Internet of Things Journal 2025年第12期12卷 20678-20694页

作者： Jiang, Yu Zhu, Di Wang, Jiadong Hu, Aiqun Southeast University School of Cyber Science and Engineering Nanjing210096 China Purple Mountain Laboratories Nanjing210096 China Key Laboratory of Computer Network Technology of Jiangsu Province Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Southeast University School of Cyber Science and Engineering School of Information Science and Engineering State Key Laboratory of Mobile Communication Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China

Channel state information (CSI) serves as a critical indicator of wireless signal conditions and is widely regarded by researchers for its sensitivity in detecting changes within the channel. However, traditional sensing technologies often require substantial data and intricate learning algorithms, highlighting an urgent need for advancements in lightweight sensing technologies. These technologies should leverage simpler terminal devices, reduced data volumes, and more straightforward classification algorithms to achieve sensing capability that are comparable to those offered by more complex and established methods. This article concentrates on the lightweight application of wireless sensing and encompasses the following key contributions: 1) the development of a lightweight sensing model utilizing a single-antenna multicarrier system, which introduces a CSI ratio model that adapts multiantenna techniques for single-antenna settings and 2) the enhancement of feature stability through the introduction of a complex-plane fitting method using artificial vector, alongside a dual receiver-based method for cross-scene feature generation aimed at producing stable and high-quality auxiliary features. Experimental results show that the feature extraction capability of the single-antenna multicarrier CSI ratio model is close to that of traditional multiantenna scheme. On the gait dataset, when the enhanced CSI ratio is used as a feature, the accuracy is nearly 100%, surpassing the 93% accuracy of the original amplitude feature. On the gesture dataset, the combination of the enhanced CSI ratio and position and environment independent features achieves an accuracy of 96%, which is superior to using the original CSI amplitude feature alone. An analysis of resource consumption shows that the lightweight SVM model incurs very low computational overhead during decision-making, validating the potential of this scheme in terms of efficiency and practical application. © 2014 IEEE.

关键词： Communication channels (information theory)

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SGAN-RL: Fusion classification method for network intrusion detection based on data augmentation

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Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 569-583页

作者： Li, Lining Liu, Shuai Ren, Rong Wu, Lianhai Wang, Qian Zhang, Bing College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information Science and Engineering Yanshan University P.R. Qinhuangdao China The Key Laboratory of Software Engineering Yanshan University Qinhuangdao China

Although traditional research methods for intrusion detection can effectively prevent and mitigate issues such as data leaks to avoid severe consequences, existing intrusion detection technologies encounter limitations, including low classification accuracy and high false positive rates, particularly when dealing with high-dimensional, complex network anomaly traffic data. Additionally, the common problem of class imbalance in intrusion detection datasets exacerbates these challenges. This paper introduces a novel approach to network intrusion detection classification, termed SGAN-RL, which integrates ResNet and LSTM models based on the semi-supervised generative adversarial network (SGAN) framework. This method utilizes SGAN to generate synthetic data resembling real intrusion detection data and continually trains the discriminator to produce high-quality data, thereby enhancing data distribution balance and quality. Moreover, a fusion intrusion detection classification model is presented, leveraging the strengths of ResNet and LSTM architectures to capture spatial and temporal features, respectively. This synergistic fusion enhances the model’s capacity for comprehensive data representation, resulting in improved performance in intrusion detection data classification tasks. Experiments conducted on intrusion detection datasets NSL-KDD, UNSW-NB15, and CIC-IDS2017 demonstrate that the proposed model enhances accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, respectively, showcasing its robust performance and reliability in network intrusion detection classification. © 2025 – IOS Press. All rights reserved.

关键词： Network intrusion

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3MVGAT: Multi-Modal and Multi-View Knowledge Graph Attention Network for Recommendation Algorithm 4

3MVGAT: Multi-Modal and Multi-View Knowledge Graph Attention...

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4th International Conference on computers and Artificial Intelligence technology, CAIT 2023

作者： Wang, Shenghong Wang, Yongli Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350326710

The importance of recommendation systems in our lives is increasingly growing, helping people quickly access valuable information from massive datasets. Previous recommendation systems have achieved significant success by incorporating techniques such as multimodal data and deep learning. However, current multimodal data recommendation algorithms primarily rely on official content, often overlooking the multimodal data provided by users. The inclusion of user-generated multimodal data from their perspective can further enhance the accuracy of recommendations for *** paper introduces a novel recommender algorithm, the Multi-Modal and Multi-View Knowledge Graph Attention Network (3MVGAT), which utilizes multi-modal and multi-view data. In the item encoder, we unify and fuse images and text from different perspectives, to learn item representations. Furthermore, we apply an attention mechanism to this encoder to select essential modalities for item representation learning. In the user encoder, we learn user representations based on their historical interactions with items and apply an attention mechanism to capture user preferences. Extensive experiments on real-world datasets demonstrate that our approach effectively enhances the performance of recommender algorithms. © 2023 IEEE.

关键词： Recommender systems

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Representation Learning Based Predicate Invention on Knowledge Graphs 39

Representation Learning Based Predicate Invention on Knowled...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhu, Man Huang, Pengfei Gu, Lei Xu, Xiaolong Han, Jingyu School of Computer Science Nanjing University of Posts and Telecommunications China College of Integrated Circuits Nanjing University of Aeronautics and Astronautics China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)157735897X

The recognition of whether or not a predicate should be invented is an important problem in the domain of predicate invention. Despite its significance, existing research has yet to fully harness the rich data available in knowledge graphs. In this paper, we introduce a novel problem formulation, ReLPI (Representation Learning for Predicate Invention in Knowledge Graphs), which constitutes a significant advancement in this domain. To address the core issues of ReLPI, we devise a scoring function that informs the learning process. By optimizing embeddings towards this scoring function, we endow them with semantic meaning, crucial for capturing the nuances of predicate presence patterns. Furthermore, we present SEmPI (Semantic Embeddings for Predicate Invention), a framework that leverages predicate (relation) embeddings as a trainable medium. SEmPI uncovers latent patterns governing predicate occurrences in knowledge graphs, enabling the invention of novel predicates grounded in these discovered patterns. This approach represents a significant step forward in leveraging data-driven methods for predicate invention in knowledge graphs. We evaluate the proposed approach on FB15k and DRKG datasets, and the results demonstrate the effectiveness of SEmPI in discovering new predicates. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Domain Knowledge

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Stochastic Perturbation Averaging Boosts Transferability of Adversarial Examples 10

Stochastic Perturbation Averaging Boosts Transferability of ...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Yang, Rongbo Li, Qianmu Meng, Shunmei Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350345032

In image, video and even real physics domains, adversarial examples can mislead deep models to produce wrong predictions. Transfer-based attacks against black-box models are more in line with realistic scenarios, but adversarial examples made on surrogate model have a low success rate when transferred to the target model due to overfitting the source model. We study the Stochastic Weight Averaging strategy in the domain generalization process and propose a Stochastic Perturbation Averaging method (SPA). Specifically, we add stochastic perturbations to the examples during the gradient descent attack, and we design a Central Amplification method (CAM) to enhance this random variation, then SPA stabilizes the iteration direction by computing the gradient average of the perturbed examples to find a relatively flat local minimum of the loss function. SPA is an efficient and general strategy which can significantly improve the transferability of the gradient-based attack methods. For instance, the average attack success rate of the adversarial examples produced based on four single models against seven pre-trained models reached 90.10%, which is the best result so far. Code is available at https://*** yangrongbo/SPA. © 2023 IEEE.

关键词： Gradient methods

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A ROI Based Self-supervised Strategy for Retinal Image Analysis

A ROI Based Self-supervised Strategy for Retinal Image Analy...

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2022 International Conference on Optical and Photonic engineering, icOPEN 2022

作者： Dong, Shiyun Chen, Qiang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781510661905

With the maturity of deep learning methods and the collection of a large number of retinal images, many deep learning models have been used for retinopathy analysis, but most of them are supervised methods. That is, the training of the models relies heavily on a large number of annotated retinal images, which is sometimes difficult to satisfy in medical fields including ophthalmology. This paper explores the application of self-supervised methods that do not require a large amount of labeled data in the analysis of retinal images. Firstly, we propose a novel self-supervised task of column dislocation and compare the performance of various self-supervised tasks on the retinopathy classification, verifying our proposed task's effectiveness. Then, by considering the characteristics of retinal images, namely the region of interest (ROI) in spectral domain optical coherence tomography (SD-OCT) images is less than half of the whole images, we also propose a ROI based self-supervised strategy to improve the feature representation ability. The experiments on two public datasets for retinal image classification and segmentation demonstrate that the proposed strategy is effective compared with several existed self-supervised learning methods. © 2022 SPIE.

关键词： Ophthalmology

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