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PromptFusion:Harmonized Semantic Prompt Learning for Infrared and Visible Image Fusion

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 502-515页

作者： Jinyuan Liu Xingyuan Li Zirui Wang Zhiying Jiang Wei Zhong Wei Fan Bin Xu IEEE the School of Software Technology Dalian University of Technology the School of Mechanical Engineering Beijing Institute of Technology

The goal of infrared and visible image fusion(IVIF)is to integrate the unique advantages of both modalities to achieve a more comprehensive understanding of a scene. However, existing methods struggle to effectively handle modal disparities,resulting in visual degradation of the details and prominent targets of the fused images. To address these challenges, we introduce Prompt Fusion, a prompt-based approach that harmoniously combines multi-modality images under the guidance of semantic prompts. Firstly, to better characterize the features of different modalities, a contourlet autoencoder is designed to separate and extract the high-/low-frequency components of different modalities, thereby improving the extraction of fine details and textures. We also introduce a prompt learning mechanism using positive and negative prompts, leveraging Vision-Language Models to improve the fusion model's understanding and identification of targets in multi-modality images, leading to improved performance in downstream tasks. Furthermore, we employ bi-level asymptotic convergence optimization. This approach simplifies the intricate non-singleton non-convex bi-level problem into a series of convergent and differentiable single optimization problems that can be effectively resolved through gradient *** approach advances the state-of-the-art, delivering superior fusion quality and boosting the performance of related downstream tasks. Project page: https://***/hey-it-s-me/PromptFusion.

关键词： Bi-level optimization image fusion infrared and visible image prompt learning

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Tax Risk Early Warning System for SMEs Using Auto Encoder-Backpropagation Neural Network with Genetic Algorithm Optimization

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第6期49卷 13-26页

作者： Chen, Zhen School of Digital Economy and Management Software Engineering Institute of Guangzhou Guangzhou510990 China

In the current economic circumstances, the tax risk management needs of small and medium-sized enterprises are becoming increasingly urgent. Effectively warning and controlling tax risks has become a critical issue of common concern for enterprise managers and tax departments. In view of this, the study first used the analytic hierarchy process to determine the indicator system and the corresponding weights for each indicator. Secondly, a fusion model for detection and warning was constructed by combining auto encoders with backpropagation neural networks. In the experiment, the study used a self-built data set containing approximately 280,000 samples to compare the performance of models such as support vector machines and gradient boosting decision trees. The performance test results showed that the optimal model parameters after genetic algorithm optimization were 8 hidden layer neurons, 11 hidden layers, dropout rate of 00.48, and learning rate of 0.024. When the amount of iterations was 1000, the loss function value of the model was 0.05, the F1 score was 0.96, the average absolute error was 0.05, and the accuracy was 0.94. In the simulation test, the model had the highest success rate of warning for the manufacturing industry, at 90.12%, and the average success rate of warning for different industries was 86.18%. The experiment findings indicated that the model exhibited high accuracy and reliability in tax risk warning. Therefore, the research not only provides a new technological means for tax risk management of small and medium-sized enterprises, but also provides a certain reference for further research and application in related fields. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Risk management

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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Science China(Information Sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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An Unbiased Fuzzy Weighted Relative Error Support Vector Machine for Reverse Prediction of Concrete Components

IEEE Transactions on Artificial Intelligence

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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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Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第1期52卷 1-10页

作者： Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China

Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lung cancer

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A Modified Dual Mode Repetitive Control for a CVCF PWM Inverter

A Modified Dual Mode Repetitive Control for a CVCF PWM Inver...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Dapeng, Li School of Software Engineering Jingling Institute of Technology Nanjing China

ISBN: (纸本)9789819620418

Repetitive control (RC) is a commonly employed approach for accurately tracking periodic signals and mitigating periodic disturbances. However, the inherent delay in repetitive controllers can adversely impact both transient and steady-state system performances. To address this challenge and enhance the overall performance of repetitive control systems, a digital dual mode repetitive control (DMRC) strategy has been introduced. DMRC has high tracking precision and swift transient response, representing a significant and noteworthy improvement. Based on DMRC, a modified dual mode repetitive control (MDRC) is introduced in the paper. The superiority of this method is analyzed. The experimental results show that MDRC can improve the steady state control accuracy of the system when the control system remains stable. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Low pass filters

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A Reduced Infinite-Order Repetitive Controller for PWM Inverters

A Reduced Infinite-Order Repetitive Controller for PWM Inver...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Li, Dapeng School of Software Engineering Jingling Institute of Technology Nanjing China

ISBN: (纸本)9789819620456

High order repetitive control (HORC) has been reported to improve the robustness of the control system that incorporate the non-periodic disturbance. In fact, the higher the order is, the more memory cells are needed, and the more total delay time of the control system is needed. Notably, in practical applications, the reported order of HORC rarely exceeds three, and an application with infinite-order HORC has never been reported. To address this issue, a reduced infinite-order repetitive controller (RIORC), using geometric principles, is introduced in this paper. The RIORC is equivalent to a second order repetitive controller. Compared to the traditional infinite-order repetitive control, the memory cells are largely reduced, and the total delay time of the control system is less needed. The effectiveness of the proposed RIORC scheme is verified through its application in a PWM inverter. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Delay control systems

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FedCLCC:A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing

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Defence Technology(防务技术) 2025年第1期43卷 80-93页

作者： Kangning Yin Xinhui Ji Yan Wang Zhiguo Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Institute of Public Security Kash Institute of Electronics and Information IndustryKashi844000China

Federated learning(FL)is a distributed machine learning paradigm for edge cloud *** can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge ***,the diversity of clients in edge cloud computing presents significant challenges for *** federated learning(pFL)received considerable attention in recent *** example of pFL involves exploiting the global and local information in the local *** pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized *** achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional *** core of FedCLCC is the use of contrastive learning and conditional *** learning determines the feature representation similarity to adjust the local *** computing separates the global and local information and feeds it to their corresponding heads for global and local *** comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.

关键词： Federated learning Statistical heterogeneity Personalized model Conditional computing Contrastive learning

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A topical VAEGAN-IHMM approach for automatic story segmentation

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Mathematical Biosciences and engineering 2024年第7期21卷 6608-6630页

作者： Yu, Jia Peng, Huiling Wang, Guoqiang Shi, Nianfeng School of Computer and Information Engineering Luoyang Institute of Science and Technology China Software Research Institute Technological University of Shannon Ireland

Feature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and generative adversarial network (GAN), which not only captures intricate data representations through VAE's probabilistic encoding and decoding mechanism but also enhances feature diversity and quality via GAN's adversarial training. To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the learned feature, a segmentor splits the document into shorter ones with different topics. Hidden Markov model (HMM) is a popular approach for story segmentation, in which stories are viewed as instances of topics (hidden states). The number of states has to be set manually but it is often unknown in real scenarios. To solve this problem, we proposed an infinite HMM (IHMM) approach which utilized an HDP prior on transition matrices over countably infinite state spaces to automatically infer the state's number from the data. Given a running text, a Blocked Gibbis sampler labeled the states with topic classes. The position where the topic changes was a story boundary. Experimental results on the TDT2 corpus demonstrated that the proposed topical VAEGAN-IHMM approach was significantly better than the traditional HMM method in story segmentation tasks and achieved state-of-the-art performance. © 2024 the Author(s).

关键词： Hidden Markov models

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