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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xiao, Guobao Liu, Xinyu Lin, Zebin Ming, Rui School of Computer Science and Technology Tongji University China College of Computer and Data Science Fuzhou University China School of Computer and Data Science Minjiang University China

ISBN: (纸本)157735897X

This paper introduces a lightweight Semantic-guided Mutually Reinforcing network (SMR-Net) for the tasks of cross-modal image fusion and salient object detection (SOD). The core concept of SMR-Net is to leverage semantics for directing the mutual reinforcing between image fusion and SOD. Specifically, a Progressive Cross-modal Interaction (PCI) image fusion subnetwork is designed to exploit local interactions via convolution operations and extend to global interactions utilizing spatial and channel attention mechanisms. Subsequently, a cross-modal Bit-Plane Slicing-based SOD subnetwork (BPS) is developed by incorporating the fused image as a third modality. This component employs bit-plane slicing and the deformable convolution technique to effectively extract irregular semantic information embedded in fusion features. The refined semantic information then guides the feature extraction process of the source modalities in a reweighted fashion. By cascading these two subnetworks, BPS leverages final semantic results to direct PCI towards focusing more on semantic information. Ultimately, through this semantic-guided mutual enhancement process, SMR-Net excels in both producing high-quality fused images and achieving effective salient object detection. Our extensive experiments on image fusion and SOD tasks convincingly demonstrate the superiority of our network over existing state-of-the-art alternatives without introducing noticeable computational costs. Compared to nearest competitors, our method demonstrates a stronger generalization ability with 26% fewer parameters. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Object detection

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Fine-Grained Cross-Modal Fusion Based Refinement for Text-to-Image Synthesis

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Chinese Journal of Electronics 2023年第6期32卷 1329-1340页

作者： SUN Haoran WANG Yang LIU Haipeng QIAN Biao Department of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of EducationHefei University of Technology

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel finegrained text-image fusion based generative adversarial networks(FF-GAN), which consists of two modules: Finegrained text-image fusion block(FF-Block) and global semantic refinement(GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.

关键词： Visualization Fuses Convolution Semantics Linguistics Benchmark testing Generative adversarial networks

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Observer-based input-to-state stabilization for coupled reaction–diffusion neural networks with disturbances via spatiotemporal sampled measurements and sliding mode control

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International Journal of Dynamics and Control 2025年第6期13卷 1-14页

作者： Chen, Juan Xue, Yuwei Zhang, Chenlong Jiao, Yingying Aliyun School of Big Data Changzhou University Gehu road Jiangsu Changzhou213164 China School of Computer Science and Artificial Intelligence Changzhou University Gehu road Jiangsu Changzhou213164 China

In this paper, for time-delayed reaction–diffusion neural networks (TDRDNNs) with matched and unmatched disturbances, we propose a backstepping-based mixed boundary control strategy integrating higher-order sliding mode control (HOSMC) and an observer via spatiotemporal sampled data (STSD). First, in the sensor network, the STSD strategy is employed to obtain the information of neurons, which facilitates our design of observer errors. Matched and unmatched disturbances consistently affect the observer’s fit to the original TDRDNNs, at which point we devise HOSMC algorithm to counteract the effects of disturbances. Subsequently, it is shown that the estimation error is input-to-state stable (ISS), which means that the observer effectively predicts the state of the neurons in spacetime even though there are ineradicable perturbations in the original TDRDNNs. Secondly, we construct an explicit expression for the boundary controller using the backstepping in conjunction with the STSD and HOSMC algorithm after mapping the observer to the target systems. In this process, our designed control scheme effectively attenuates the disturbances of the TDRDNNs. Further, using this controller and some inequality tools, the ISS of the closed-loop TDRDNNs is demonstrated. Finally, the effectiveness of the control strategy is verified by numerical simulation, and it can be seen from the comparison experiments that TDRDNNs produce less jitter under our controller. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Sliding mode control

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Security Performance Analysis for MIMO Systems: From Artificial Noise's Power and Dimension Perspective

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9985-9989页

作者： Xu, Panpan Li, Zhenling Li, Chunguo Tan, Weijie Guizhou University School of Mathematics and Statistics Guiyang550025 China Southeast University School of Information Science and Engineering Nanjing210096 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China

This paper analyzes the influence of power and dimension of artificial noise (AN) on security performance of multiple-input multiple-output (MIMO) system with multiple randomly located eavesdroppers. We derive the closed-form expressions of the secrecy outage probability (SOP) and the optimal secrecy capacity by utilizing the irrelevance among transmission channels, and the orthogonality between AN and the basis vectors of the legitimate channel matrix's null space. We use the Poisson point process to determine the number and location of eavesdroppers, then analyze the security performance of the communication system under the 3D channel condition of probability-based line-of-sight (LoS) or non-line-of-sight (NLoS) channel. The simulation results show the following conclusions. When the AN dimension reaches 1/4 highest dimension (i.e., one quarter of the number of transmitting antennae less than one) and the power splitter factor (PSF) falls in the open interval (0.6, 0.9), we can achieve a trade-off between secrecy capacity and design complexity. © 1967-2012 IEEE.

关键词： Vector spaces

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Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL 31

Solid-SQL: Enhanced Schema-linking based In-context Learning...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Geling Tan, Yunzhi Zhong, Ruichao Xie, Yuanzhen Zhao, Lingchen Wang, Qian Hu, Bo Li, Zang School of Cyber Science and Engineering Wuhan University China Big Data and AI Platform Department Tencent China

ISBN: (纸本)9798891761964

Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems. Nevertheless, many state-of-the-art (SOTA) approaches have overlooked the critical aspect of system robustness. Our experiments reveal that while LLM-driven methods excel on standard datasets, their accuracy is notably compromised when faced with adversarial perturbations. To address this challenge, we propose a robust text-to-SQL solution, called Solid-SQL, designed to integrate with various LLMs. We focus on the pre-processing stage, training a robust schema-linking model enhanced by LLM-based data augmentation. Additionally, we design a two-round, structural similarity-based example retrieval strategy for in-context learning. Our method achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% on the general Spider and Bird benchmarks, respectively. Furthermore, experimental results show that Solid-SQL delivers an average improvement of 11.6% compared to baselines on the perturbed Spider-Syn, Spider-Realistic, and Dr. Spider benchmarks. © 2025 Association for Computational Linguistics.

关键词： Adversarial machine learning

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Credit card fraud detection: an evaluation of SMOTE resampling and machine learning model performance

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International Journal of Business Intelligence and data Mining 2023年第1期23卷 1-13页

作者： Alshameri, Faleh Xia, Ran Department of Information Technology Data Science and Cybersecurity School of Business and Technology Marymount University United States

Credit card fraud has been a noted security issue that requires financial organisations to continuously improve their fraud detection system. In most cases, a credit transaction dataset is expected to have a significantly larger number of normal transactions than fraud transactions. Therefore, the accuracy of a fraud detection system depends on building a model that can adequately handle such an imbalanced dataset. The purpose of this paper is to explore one of the techniques of dataset rebalancing, the synthetic minority oversampling technique (SMOTE). To evaluate the effects of this technique on model training, we selected four basic classification algorithms, complement naïve Bayes (CNB), K-nearest neighbour (KNN), random forest and support vector machine (SVM). We then compared the performances of the four models trained on the rebalanced and original dataset using the area under precision-recall curve (AUPRC) plots. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Support vector machines

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Multi-UAV Cooperative Task Offloading in Blockchain-Enabled MEC for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2271-2284页

作者： Jing, Jinfeng Yang, Yaozong Zhou, Xiaokang Huang, Jiwei Qi, Lianyong Chen, Ying Beijing Information Science and Technology University School of Computer Science Beijing100101 China Kansai University Faculty of Business and Data Science Japan China University of Petroleum Beijing Key Laboratory of Petroleum Data Mining Beijing102249 China College of Computer Science and Technology China

The development of the Internet of Things (IoT) has made the traditional Consumer Electronics (CE) evolve into a next-generation CE with many intelligent application which generates a large number of computing-intensive tasks and is sensitive to latency. The combination of blockchain technology and multi-UAV-assisted edge computing is considered a promising technology that can process tasks near CE devices instead of sending data to remote data centers, and it has advantages in flexible deployment, differentiated services, and security assurance. This paper considers a Blockchain-enabled MEC scenario where CE devices can offload tasks to multiple-UAV-edge layers for decentralized cooperative computing, and utilize the reputation mechanism of blockchain to identify abnormal nodes in edge offloading. To solve the problem of computation offloading and resource allocation in this scenario, we jointly optimize the CPU cycle frequencies and offloading resources of UAVs and CE devices, and the decision of UAVs. Our goal is to minimize system energy consumption while ensuring the quality of service. The stochastic optimization technique is employed to transform the original stochastic optimization problem into a deterministic optimization problem, decomposing it into multiple sub-problems which can be solved in parallel. Additionally, we design a dynamic priority scoring strategy to facilitate cooperative processing among multiple UAVs and propose a Blockchain-Based Multi-UAV Cooperative Task Offloading (BMCTO) algorithm. Through theoretical analysis and simulation experiments, we demonstrate that the BMCTO algorithm can effectively reduce system energy consumption while maintaining system performance. © 1975-2011 IEEE.

关键词： Computation offloading

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On the Security of Smart Home Systems:A Survey

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Journal of Computer science & technology 2023年第2期38卷 228-247页

作者：袁斌万俊吴宇晗邹德清金海 School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Distributed System Security Huazhong University of Science and Technology Wuhan 430074China Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan 430074China National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan 430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts to investigate and enhance the security of smart home *** a more secure smart home ecosystem,we present a detailed literature review on the security of smart home ***,we categorize smart home systems’security issues into the platform,device,and communication *** exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security ***,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

关键词： IoT(Internet of Things) smart home IoT security smart home security

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Application of New Feature Techniques for Multimedia Analysis in Artificial Neural Networks by Using Image Processing

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第11期48卷 113-124页

作者： Liu, Lianqiu Yang, Yongping Chen, Hong Shun School Of Artificial Intelligence and Big Data Chongqing College of Finance and Economics Chongqing402160 China Department of Quality Monitoring and Development Planning Hunan University of Humanities Science and Technology Hunan Loudi417000 China

To evaluate and extract information from multimedia material including photos, videos, and audio, a fusion of image processing and computer vision methods known as kernel principal component analysis (KPCA) is used. The objective is to create a system that can automatically identify relevant aspects of multimedia data and make them available for analysis and decision-making. By merging various processing methods, the fusion of image processing and multimedia analysis may improve analysis efficiency. The CT scans dataset from Kaggle is used to train artificial neural networks (ANN) for use in multimedia analysis. For multimedia analysis by combining image processing, we proposed kernel principal component analysis with artificial neural networks (KPCA-ANN) in this paper. The ability to process, analyze, and understand multimedia data by merging image processing into multimedia analysis has tremendous promise. It may improve decision-making, deepen our comprehension of complicated processes, and provide more fruitful means of information exchange. The experimental findings demonstrate that the proposed strategy has provided an absolute mean error of 7 and a structural similarity index of 86. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Merging

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Intelligent proportioning model of converter scrap based on optimization algorithm

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Progress in Natural science:Materials International 2024年第1期34卷 37-44页

作者： Chaojie Zhang Jinjun Cheng Liqiang Zhang Yingxue Wang Wei Zhang Jianwei Yuan Haichuan Wang School of Metallurgical Engineering Anhui University of Technology National Engineering Research Center for Public Safety Risk Perception and Control by Big Data(RPP) China Academic of Electronics and Information Technology Wuhu Xinxing Pipes Co. Ltd.

Scrap is one of the main raw materials in converter steelmaking. Due to the wide variety of scrap and the significant differences in its prices, the proportioning of scrap presents a challenge in converter steelmaking. This paper explores the optimal proportioning method of scrap for converters using an optimization mathematical model. Initially, the melting characteristics of scrap after being added to the converter's molten iron were investigated using finite element numerical simulations. The study found that when the weight of the scrap is fixed, both the shape and type of the scrap significantly influence the melting time. Subsequently, employing mathematical optimization methods and considering comprehensive process parameters of the 210t converter smelting, including the composition and unit price of the scrap, as well as the requirements of the converter steelmaking process for scrap, an optimization model for the proportioning of scrap in converters was *** model was solved using computer programming languages. Through this optimization model, it is possible to obtain the lowest cost per ton of molten steel in the converter smelting process by determining the optimal proportions of various types of scrap.

关键词： scrap proportioning Optimization model Converter steelmaking mathematical model numerical simulation

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