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IAENG International Journal of computer science 2025年第3期52卷 771-780页

作者： Ma, Wenlong Liu, Weisheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the analysis of drone aerial images, object detection tasks are particularly challenging, especially in the presence of complex terrain structures, extreme differences in target sizes, suboptimal shooting angles, and varying lighting conditions, all of which exacerbate the difficulty of recognition. In recent years, the DETR model based on the Transformer architecture has eliminated traditional post-processing steps such as NMS(Non-Maximum Suppression), thereby simplifying the object detection process and improving detection accuracy, which has garnered widespread attention in the academic community. However, DETR has limitations such as slow training convergence, difficulty in query optimization, and high computational costs, which hinder its application in practical fields. To address these issues, this paper proposes a new object detection model called OptiDETR. This model first employs a more efficient hybrid encoder to replace the traditional Transformer encoder. The new encoder significantly enhances feature processing capabilities through internal and cross-scale feature interaction and fusion logic. Secondly, an IoU (Intersection over Union) aware query selection mechanism is introduced. This mechanism adds IoU constraints during the training phase to provide higher-quality initial object queries for the decoder, significantly improving the decoding performance. Additionally, the OptiDETR model integrates SW-Block into the DETR decoder, leveraging the advantages of Swin Transformer in global context modeling and feature representation to further enhance the performance and efficiency of object detection. To tackle the problem of small object detection, this study innovatively employs the SAHI algorithm for data augmentation. Through a series of experiments, It achieved a significant performance improvement of more than two percentage points in the mAP (mean Average Precision) metric compared to current mainstream object detection models. Furthermore, ther

关键词： Decoding

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A Nonlinear Explicit Model of A Non-Circular Subsea Tunnel-Liner System with An FGM Inverted Arch Under Mechanical Loading and Fire Fields

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China Ocean engineering 2024年第5期38卷 855-865页

作者： HE Jie CHANG Guo-yong LIU Yang LI Zhao-chao School of Civil Engineering Hunan University of TechnologyZhuzhou 412007China School of Civil Engineering Changsha University of Science and TechnologyChangsha 410114China

This paper proposes an explicit scheme to analyze the failure of a subsea polyhedral tunnel-liner system with an inverted arch under mechanical loading and fire *** thin-walled liner is made of Functionally Graded Materials(FGMs),which may improve the stability behavior of the tunnel-liner *** pressure is inevitable in the liner since underground water may penetrate the cracks of the tunnel,and reach the outer surface of the *** addition,an elevated temperature loading is taken into account,considering that fire may occur in the tunnel-liner *** the combination of mechanical loading and thermal loading,the liner deforms into a single-lobe shape,which is depicted by a trigonometric *** total potential energy is expressed quantitatively after the energy approach and thin-walled shell theory are *** minimum potential energy is obtained when the critical buckling *** critical buckling pressure is calculated,which considers the effect of the thermal *** present analytical prediction is subsequently compared precisely with other closed-form ***,the effects of several parameters,such as the geometric shapes,temperature variations,and volume fraction indices,are discussed to further survey the buckling performance of the nonlinear buckling of an FGM polyhedral liner with an inverted *** may address a polyhedral liner with fewer polyhedral sides,and a lower volume fraction index is recommended to rehabilitate cracked tunnels in engineering applications.

关键词： tunnel-liner system with an inverted arch volume fraction index mechanical and thermal loadings rehabilitation buckling

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China's poverty assessment and analysis under the framework of the UN SDGs based on multisource remote sensing data

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地球空间信息科学学报（英文版） 2024年第1期27卷 111-131页

作者： Mengjie Wang Yanjun Wang Fei Teng Shaochun Li Yunhao Lin Hengfan Cai Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying Mapping and Remote SensingHunan University of Science and TechnologyXiangtanChina National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtanChina School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtanChina Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying Mapping and Remote SensingHunan University of Science and TechnologyXiangtanChina National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and TechnologyXiangtanChina

Poverty has always been a global concern that has restricted human *** first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the *** establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG *** paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development *** temporal and spatial distribution characteristics of China's poverty areas and their SDG 1 evaluation values in 2012,2014,2016,and 2018 have been *** on the SDGs global indicator framework,this paper first constructed SDG 1 China's district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images,land cover data,and digital elevation model ***,we establish SDG 1 China's localized partial least squares estimation model and SDG 1 China's localized machine learning estimation ***,we analyze and verify the spatiotemporal distribution characteristics of China's poverty areas and counties and their SDG 1 evaluation *** results show that SDG 1 China's district and county localization indicator system proposed in this study and SDG 1 China's localized partial least squares estimation model can better reflect the poverty level of China's districts and *** estimated model R2 is 0.65,which can identify 72.77%of China's national poverty *** 2012 to 2018,the spatial distribution pattern of SDG evaluation values in China's districts and counties is that the SDG evaluation values gradually increase from western China to eastern *** addition,the average SDG 1 evaluation value of China's districts and counties increased by 23%from 2012 to *** paper is oriented to the United Nations SDGs frame-work,explores the SDG 1 localized evalu

关键词： Multisource remote sensing data Sustainable Development Goals(SDGs) poverty indicator system partial least squares machine learning

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Principle of subtraction ghost imaging in scattering medium

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Chinese Physics B 2023年第6期32卷 250-254页

作者：付芹白艳锋谭威黄贤伟南苏琴傅喜泉 College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China School of Computer and Information Engineering Hunan University of Technology and BusinessChangsha 410205China

Scattering medium in light path will cause distortion of the light field,resulting in poor signal-to-noise ratio(SNR)of ghost *** disturbance is usually eliminated by the method of *** deduce the intensity fluctuation correlation function of the ghost imaging with the disturbance of the scattering medium,which proves that the ghost image consists of two correlated results:the image of scattering medium and the target *** effect of the scattering medium can be eliminated by subtracting the correlated result between the light field after the scattering medium and the reference light from ghost image,which verifies the theoretical *** research may provide a new idea of ghost imaging in harsh environment.

关键词： ghost imaging image reconstruction techniques scattering medium

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Feature-Grounded Single-Stage Text-to-Image Generation

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Tsinghua science and technology 2024年第2期29卷 469-480页

作者： Yuan Zhou Peng Wang Lei Xiang Haofeng Zhang School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Recently,Generative Adversarial Networks(GANs)have become the mainstream text-to-image(T2I)***,a standard normal distribution noise of inputs cannot provide sufficient information to synthesize an image that approaches the ground-truth image ***,the multistage generation strategy results in complex T2I ***,this study proposes a novel feature-grounded single-stage T2I model,which considers the“real”distribution learned from training images as one input and introduces a worst-case-optimized similarity measure into the loss function to enhance the model's generation *** results on two benchmark datasets demonstrate the competitive performance of the proposed model in terms of the Frechet inception distance and inception score compared to those of some classical and state-of-the-art models,showing the improved similarities among the generated image,text,and ground truth.

关键词： text-to-image(T2I) feature-grounded single-stage generation Generative Adversarial Network(GAN)

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Minimal Context-Switching Data Race Detection with Dataflow Tracking

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Journal of computer science & technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

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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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Code context-based reviewer recommendation

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Frontiers of computer science 2025年第1期19卷 97-108页

作者： Dawei YUAN Xiao PENG Zijie CHEN Tao ZHANG Ruijia LEI School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China Faculty of Science University of AmsterdamAmsterdam WB 1018Netherlands

Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scrutinize changes made to source code. However, in large-scale open-source projects, selecting the most suitable reviewers for a specific change can be a challenging task. To address this, we introduce the Code Context Based Reviewer Recommendation (CCB-RR), a model that leverages information from changesets to recommend the most suitable reviewers. The model takes into consideration the paths of modified files and the context derived from the changesets, including their titles and descriptions. Additionally, CCB-RR employs KeyBERT to extract the most relevant keywords and compare the semantic similarity across changesets. The model integrates the paths of modified files, keyword information, and the context of code changes to form a comprehensive picture of the changeset. We conducted extensive experiments on four open-source projects, demonstrating the effectiveness of CCB-RR. The model achieved a Top-1 accuracy of 60%, 55%, 51%, and 45% on the Android, OpenStack, QT, and LibreOffice projects respectively. For Mean Reciprocal Rank (MRR), CCB achieved 71%, 62%, 52%, and 68% on the same projects respectively, thereby highlighting its potential for practical application in code reviewer recommendation.

关键词： code reviewer recommendation code context pull request

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A Generative Model-Based Network Framework for Ecological Data Reconstruction

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computers, Materials & Continua 2025年第1期82卷 929-948页

作者： Shuqiao Liu Zhao Zhang Hongyan Zhou Xuebo Chen School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

关键词： Convolutional Neural Network(CNN) VAE GAN TOPSIS data reconstruction

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Highly anti-corrosive NiFe LDHs–NiFe alloy hybrid enables long-term stable alkaline seawater electrolysis

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Rare Metals 2024年第9期43卷 4321-4332页

作者： Jia-Hong Li Hao Chen Si-Hang You Gan-Xin Yang Peng Liu Meng-Qi Gao Shu-Guang Chen Fei-Fei Zhang School of Materials Science and Engineering Changsha University of Science and TechnologyHunan 410114China Department of Materials Science and Engineering Faculty of EngineeringNational University of SingaporeSingapore 117576Singapore School of Materials Science and Engineering Jilin UniversityJilin 130022China

Owing to the significant potential of alkalin seawater electrolysis for converting surplus power into eco friendly hydrogen fuel,we developed bifunctional elec trodes that integrate low-crystalline NiFe LDHs and amorphous NiFe alloy on a Ni foam(NF)substrate to enhance this *** by the battery-like charac teristics of NiFe LDHs,an anti-corrosive and active oute layer of NiFe^(vac)OOH continuously forms over time in th hybrid on the anode for the oxygen evolution reaction(OER),effectively mitigating powder shedding caused by corrosion induced by multiple anions in *** while,the strong bond between the hybrid and the NF substrate maintains intact hybrid coatings to ensure a rel atively high overall conductivity of the electrodes,signif icantly reducing the negative effects of structura degradation during the OER and hydrogen evolution reaction(HER),as well as the accumulation of contami nants on the electrode *** long-term tests,thes bifunctionalhybridelectrodesmaintained stable performance,even at a high current density o500 mA·cm^(-2).The cell voltage increased by only 88 m V over 1000 h to 1.970 V during saline electrolysis and by103 mV over 500 h to 2.062 V during seawater electroly ***,this study provides valuable insights into efficient and stable seawater electrolysis using NiFe LDHs–NiFe alloy hybrids.

关键词： NiFe LDHs Bifunctional Corrosion Stability Seawater electrolysis

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