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Multi-mechanism neural machine translation framework for automatic program repair

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 7859-7873页

作者： Cao, Heling Han, Dong Chu, Yonghe Tian, Fangchao Wang, Yun Liu, Yu Jia, Junliang Ge, Haoyang College of Information Science and Engineering Henan University of Technology Zhengzhou China Henan International Joint Laboratory of Grain Information Processing Henan University of Technology Zhengzhou China Henan Kingsource Information Technology Zhengzhou China

Automatic program repair (APR) is crucial to improve software quality. Recently, neural machine translation (NMT) based modeling for bug fixes has demonstrated great potential. However, these approaches still have two major challenges. One is that their search space is limited due to the out-of-vocabulary (OOV) problem. The other is that the NMT-based APR models tend to ignore past translation information, which often leads to over-translation and under-translation. To address the above challenges, we propose MNRepair, a new NMT-based APR approach that combines multiple mechanisms to fix bugs in source code. Specifically, we devise an encoder-decoder NMT framework with the attention mechanism. Our framework combines the copy mechanism to overcome the OOV problem that occurs with source code. To deal with the over-translation and under-translation, we utilize a coverage mechanism to record past translation information. MNRepair is able to capture a wide range of repair operators and fix 26 bugs in Defects4J. Our evaluation shows the effectiveness of multiple mechanisms in the repair process. © 2024 – IOS Press.

关键词： Neural machine translation

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grain Depot Wearable Device Detection Model Based on YOLOv5

Grain Depot Wearable Device Detection Model Based on YOLOv5

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Intelligent Management and Software Engineering (IMSE), International Conference on

作者： Jianjun Wu Yimeng Lian Qing Wen Tong Then Yuhua Zhu Zhihui Li College of Information Science and Engineering Henan University of Technology Zhengzhou China Key Laboratory of Grain Information Processing and Control Henan University of Technology Zhengzhou China

Standardized wearing of helmets and ventilators is the focus of grain depot safety management, which can effectively prevent safety accidents such as falling objects hurting people and fumigation poisoning. This paper is based on the deep learning algorithm to detect the wearing specifications of grain depot operations. Firstly, collect images by simulating the operation scene of the grain depot, recording video frames on the spot, etc. Use Labellmag software to mark the personnel wearing and not wearing the device, and create the original data set; Then, a grain depot wear standard monitoring model based on YOLOv5 is constructed. Add CBAM Attention to improve the network’s attention to local and global features and the ability to extract small target location information. In the post-processing stage, DIOU-NMS is optimized as the screening scheme of the target boxes to reduce the rate of missed detection rate of adjacent targets. Through the contrast experiment, the results show that the improved model mAP(mean average precision) is increased from 92.6% to 93.9%, and finally the detection model meeting the detection accuracy and real-time requirements is obtained.

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DWMFusion: A lightweight medical image fusion network based on discrete wavelet convolution and selective state space models

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Expert Systems with Applications 2025年 286卷

作者： Fan, Chao Yun, Mengyang Chen, Li Zhao, Huijun Peng, Bingcheng School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China Ministry of Education Key Laboratory of Grain Information Processing and Control Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City450001 China

Multimodal medical image fusion improves the accuracy of clinical diagnosis by integrating medical imaging information from different sensors into unified and complementary multi-scale fused information images. However, existing fusion methods still face challenges in capturing modality-specific features, balancing local and global contextual features, and preserving complementary information from different representations. To overcome these shortcomings, this study proposes a lightweight feature complementary fusion network(DWMFusion) to achieve effective fusion of functional and structural medical images under lightweight premise. Specifically, a cross-channel feature extraction unit based on discrete wavelet transform (DWT) and convolutional coupling structure is designed to enhance the modal intrinsic frequency domain features. Meanwhile, a visual state space block (VSSB) and a contextual modeling structure with multiple layers of Mamba are introduced to balance local details with global semantics and reduce computational redundancy. In the image fusion stage, a multilevel cross-fusion unit (CFFM) that introduces position coding is designed to realize deep cross-modal spatio-temporal feature interaction. A joint loss function incorporating wavelet decomposition properties is proposed to effectively adaptively constrain the frequency domain consistency and maintain the structural information. Validation on the Harvard Medical School database shows that DWMFusion is capable of generating visually satisfactory fusion images. And compared with other methods, it improves the SD metrics by an average of 17.59%, the SF metrics by an average of 17.64%, and the AG metrics by an average of 19.96% on the three datasets. These results confirm the robustness and adaptability of the model. © 2025 Elsevier Ltd

关键词： Wavelet decomposition

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Wi-Wheat+:Contact-free wheat moisture sensing with commodity WiFi based on entropy

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Digital Communications and Networks 2023年第3期9卷 698-709页

作者： Weidong Yang Erbo Shen Xuyu Wang Shiwen Mao Yuehong Gong Pengming Hu College of Information Science and Engineering Henan University of TechnologyZhengzhou450001China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001China College of International Education Kaifeng UniversityKaifengHenan475004China Knight Foundation School of Computing&Information Sciences Florida International UniversityMiamiFL33199USA Department of Electrical and Computer Engineering Auburn UniversityAuburnAL36849-5201USA

In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time *** proposed system is based on Commodity WiFi and is easy to *** WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel *** feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.

关键词： Channel state information(CSI) WiFi Multi-scale entropy Multi-class support vector machine(SVM) Radio frequency(RF)sensing

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Ultrasound Imaging Based on Mean and Standard Deviation

Ultrasound Imaging Based on Mean and Standard Deviation

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Cuijuan Lou Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education Zhengzhou China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Dept. of Artificial Intelligence Henan University of Technology Zhengzhou China

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Resolution and contrast are two important indicators for evaluating the quality of ultrasound imaging. In order to improve both indicators of ultrasound imaging, the ultrasound imaging method based on mean and standard deviation are proposed. Through simulation results of multiple scattering points at different depths, the proposed method achieves a lateral resolution of 50.50% compared to delay and sum (DAS) and 36.17% compared to square-neighborhood robust signal mean-to-standard-deviation factor imaging (SN-RSMSF). Through the simulated anechoic and hyperechoic regions results, the contrast improvement of the proposed method can reach up to 0.36dB, compared to DAS and SN-RSMSF.

关键词： In vivo Ultrasonic imaging Image resolution Scientific computing Simulation Imaging Scattering

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Cross-Modality Comprehensive Feature Aggregation Network for Road Scene Segmentation

Cross-Modality Comprehensive Feature Aggregation Network for...

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International Symposium on Computer Science and Intelligent controls (ISCSIC)

作者： Yale Yang Zihao Zhang Huifang Hou Wenwen Zhang Chunsheng Zhuang College of Information Science and Engineering Henan University of Technology Zhengzhou China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control and school of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China Henan Academy of Sciences Zhengzhou China

ISBN: (数字)9798350380286

ISBN: (纸本)9798350380293

RGB-thermal semantic segmentation is a potential solution for reliable semantic scene understanding under adverse weather and complex lighting conditions. However, past studies have primarily focused on the design of fusion paradigms. Unfortunately, these methods have not fully considered the modality differences caused by different imaging mechanisms, thereby reducing the performance of pixel-level semantic analysis. In response to this issue, this paper proposes an innovative Cross-Modality Comprehensive Feature Aggregation Network (CFANet). This method first designs a Cross-Modality Feature Rectification Module (CFRM) to align modality differences; then, it constructs a Global-Local Fusion Module (GLF) to sharpen object edges and refine the segmentation map. Results on two urban scene understanding datasets demonstrate that CFANet can effectively aggregate multi-layer deep features and outperform existing advanced deep learning methods in scene understanding performance.

关键词： Deep learning Semantic segmentation Roads Design methodology Semantics Lighting Imaging Reliability Meteorology Intelligent control

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Isolated FeN_(3)sites anchored hierarchical porous carbon nanoboxes for hydrazine-assisted rechargeable Zn-CO_(2)batteries with ultralow charge voltage

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Carbon Energy 2025年第1期7卷 121-132页

作者： Sanshuang Gao Hongyi Li Zhansheng Lu Songjie Meng Xue Zhao Xinzhong Wang Xijun Liu Guangzhi Hu Shenzhen Institute of Information Technology Shenzhen 518172China State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures Guangxi Key Laboratory of Processing for Nonferrous Metals and Featured MaterialsSchool of ResourcesEnvironment and MaterialsGuangxi UniversityNanning 530004China School of Ecology and Environmental Science Institute for Ecological Research and Pollution Control of Plateau LakesYunnan UniversityKunming650504China State Key Laboratory of Chemistry and Utilization of Carbon Based Energy Resources College of ChemistryXinjiang UniversityUrumqiChina School of Physics Henan Normal UniversityXinxiangChina Faculty of Chemistry and Chemical Engineering Yunnan Normal UniversityKunmingChina

Zn-CO_(2)batteries(ZCBs)are promising for CO_(2)conversion and electric energy ***,the ZCBs couple the electrochemical CO_(2)reduction(ECO_(2)R)with the oxygen evolution reaction and competitive hydrogen evolution reaction,which normally causes ultrahigh charge voltage and CO_(2)conversion efficiency attenuation,thereby resulting in~90%total power ***,isolated FeN_(3)sites encapsulated in hierarchical porous carbon nanoboxes(Fe-HPCN,derived from the thermal activation process of ferrocene and polydopamine-coated cubic ZIF-8)were proposed for hydrazine-assisted rechargeable ZCBs based on ECO_(2)R(discharging process:CO_(2)+2H+→CO+H_(2)O)and hydrazine oxidation reaction(HzOR,charging process:N_(2)H_(4)+4OH−→N_(2)+4H_(2)O+4e^(−)).The isolated FeN_(3)endows the HzOR with a lower overpotential and boosts the ECO_(2)R with a 96%CO Faraday efficiency(FECO).Benefitting from the bifunctional ECO_(2)R and HzOR catalytic activities,the homemade hydrazine-assisted rechargeable ZCBs assembled with the Fe-HPCN air cathode exhibited an ultralow charge voltage(decreasing by~1.84 V),excellent CO selectivity(FECO close to 100%),and high 89%energy *** situ infrared spectroscopy confirmed that Fe-HPCN can generate rate-determining*N_(2)and*CO intermediates during HzOR and ECO_(2)*** paper proposes FeN_(3)centers for bifunctional ECO_(2)R/HzOR performance and further presents the pioneering achievements of ECO_(2)R and HzOR for hydrazine-assisted rechargeable ZCBs.

关键词： electrochemical CO_(2)reduction Fe single atoms hierarchical porous carbon nanoboxes hydrazine-assisted Zn-CO_(2)batteries hydrazine oxidation reaction

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Mildew Detection for Stored Wheat Using Gas Chromatography–Ion Mobility Spectrometry and Broad Learning Network

SSRN

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SSRN 2023年

作者： Lian, Fei-Yu Fu, Mai-Xia Qin, Yao Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou China School of Information Science and Engineering Henan University of Technology Zhengzhou China

Most of the existing methods for wheat mildew detection are biochemical methods, which have the problems of complicated procedure and slow speed. In this paper, a novel wheat mildew detection and classification model was proposed by combining gas chromatography-ion mobility spectrometry (GC-IMS) with broad learning network (BLN) model. Firstly, GC-IMS fingerprint spectrum of wheat samples with different degrees of mildew were collected by GC-IMS spectrometer, and then a classification models was constructed based on broad learning. To improve the classification accuracy of the model, the incremental learning model and the spatial attention mechanism (SAM) were introduced into the model. The model uses the feature information and structure information of the nodes to calculate the attention weight, and extracts the more important feature information in the fingerprint of the samples. At the same time, regularization mechanism was introduced to enhance the generalization performance of the model. Experimental results show that the training time of the proposed model is greatly reduced compared to existing deep learning models. Under the small sample set condition, the mean average accuracy (mAP) of wheat mildew types reaches 90.32%, and the identification precision of early wheat mildew reach 95.34%, and the overall performance index is better than that of the deep learning model. The experiment also proved that GC-IMS combined with broad learning model is an efficient and accurate method for wheat mildew detection. © 2023, The Authors. All rights reserved.

关键词： Gas chromatography

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Software Multiple-Fault Localisation Based on Iterative Self-Organizing Clustering

SSRN

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SSRN 2024年

作者： Cao, Heling Wang, Zhaolong Tian, Fangchao Deng, Miaolei Chu, Yonghe Jia, Junliang Wang, Fei Ge, Haoyang Ministry of Education Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Henan kingsource Information Technology 450000 China Henan International Joint Laboratory of Grain Information Processing Zhengzhou450001 China

The traditional software defect localisation method based on spectrum performs well in single defect scenarios, but it lacks effectiveness in multiple defect scenarios. Cluster analysis is a method to improve the accuracy of multi-defect locations. This method clusters the failed test cases to generate multiple clusters centered around defects. By utilizing the clustering results, defect reports are generated to identify the locations of multiple defects. We proposed iterative self-organizing clustering for multiple-fault localisation (ISOMFL) to improve the efficacy of multi-defect localisation. We have achieved the effectiveness of this method for multi-defect localisation through some improvements. Firstly, we construct a vector representation for failed test cases and improve the Footrule distance measurement to accurately calculate the distance between test case vectors. This allows for a better assessment of the dissimilarity between failed test cases. Secondly, to address the issue of random assignment of cluster centers, we introduce a dissimilarity matrix to identify suitable initial cluster centers. Next, through the iterative self-organizing clustering algorithm, we dynamically adjust the number of clusters by employing cluster merging and splitting operations. This leads to the creation of multiple clusters centered around defects. Lastly, we combine the clustering results of failed test cases with all successful test cases to form a novel coverage information spectrum. By utilizing the doubt formula, we calculate and generate defect reports based on these clusters. These optimization measures significantly enhance the accuracy and efficiency of multi-defect localisation. Experimental studies on six defect projects in the SIR and Defects4J databases demonstrate that ISOMFL outperforms other software multi-defect localisation methods such as MSeer, FSMFL, and J2 in terms of localisation performance. © 2024, The Authors. All rights reserved.

关键词： Cluster analysis

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Classification and Quantitative Modeling Analysis of Groundwater Hardness based on Ultraviolet Absorption Spectrum 3

Classification and Quantitative Modeling Analysis of Groundw...

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2020 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Wu, Guofang Lian, Feiyu Fu, Maixia Zhang, Yuan School of Information Science and Engineering Henan University of Technology China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou45001 China Key Laboratory of Grain Photoelectric Detection and Control Henan Province Henan University of Technology Zhengzhou45001 China

In accordance with the national standard of groundwater quality, samples with various ion concentrations are prepared based on water hardness standard substances, and a water hardness classification and quantitative analysis model based on ultraviolet absorption spectrum are established. Firstly, thepartial least squares discriminant analysis (PLSDA) model is established by using a group of samples with different ion concentration that form the training set, and the binary tree is introduced for multiple classification of water hardness. Secondly, partial least squares support vector machine (PLS-SVM) is used to establish the quantitative model of each class of samples. Atlast, Monte carlo cross-examination is performed on the model using a test set composed of samples with different ion concentrations. Experimental results show that, for the identification of underwater hardness, the average error rate is 1.13%, which is less than other common classification methods, and MSE is 0.247, and R2 is 0.987, which both are improved than other common methods for the prediction of ion concentration of underwater. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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