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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Yang, Jian Guo, Hongcheng Yin, Yuwei Bai, Jiaqi Wang, Bing Liu, Jiaheng Liang, Xinnian Chai, Linzheng Yang, Liqun Li, Zhoujun State Key Lab of Software Development Environment Beihang University China Department of Computer Science University of British Columbia Canada

ISBN: (纸本)9782493814104

Multilingual translation supports multiple translation directions by projecting all languages in a shared space, but the translation quality is undermined by the difference between languages in the text-only modality, especially when the number of languages is large. To bridge this gap, we introduce visual context as the universal language-independent representation to facilitate multilingual translation. In this paper, we propose a framework to leverage the multimodal prompt to guide the Multimodal Multilingual neural Machine Translation (m3P), which aligns the representations of different languages with the same meaning and generates the conditional vision-language memory for translation. We construct a multilingual multimodal instruction dataset (InstrMulti102) to support 102 languages Our method aims to minimize the representation distance of different languages by regarding the image as a central language. Experimental results show that m3P outperforms previous text-only baselines and multilingual multimodal methods by a large margin. Furthermore, the probing experiments validate the effectiveness of our method in enhancing translation under the low-resource and massively multilingual scenario. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Visual languages

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Quantitative Parameters Analysis for Prenatally Echocardiographic Diagnosis of Atrioventricular Septal Defects

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Congenital Heart Disease 2023年第3期18卷 387-397页

作者： Xiaoxue Zhou Tingyang Yang Ye Zhang Yanping Ruan Jiancheng Han Xiaowei Liu Ying Zhao Xiaoyan Gu Tingting Liu Hairui Wang Yihua He Maternal-Fetal Consultation Center of Congenital Heart Disease Department of EchocardiographyBeijing Anzhen HospitalCapital Medical UniversityBeijingChina State Key Laboratory of Software Development Environment Beihang UniversityBeijingChina

Background:Atrioventricular septal defects(AVSDs)are screened and diagnosed usually rely on the imaging characteristics of fetal echocardiography(FE).However,diagnosis on images is heavily depended on sonographers’experience and the quantitative data are rarely ***:This study aimed to realize the prenatal diagnosis of AVSDs by analyzing the quantitative data on ***:One hundred and thirteen cardiac quantitative data was analyzed in 370 normal and 49 AVSDs fetuses *** top six with the highest diagnostic accuracy rate were acquired according to the area under the curve(AUC),and the diagnostic value of six variables was ***:Six parameters obtained on the four-chamber view(4CHV),including the atrial to ventricular length ratio in end-diastole(AVLR-ED),AVLR-ED combined with the atrial to ventricular length ratio in end-systole(AVLR-ES),quantile score(Q score)of AVLR-ED,Q score of AVLR-ES,Q score of ventricle length in end-diastole(VL-ED),and AVLR-ES,were the top six with the highest diagnostic value,and the AUC was 0.99(95%CI 0.99–1.00),0.99(95%CI 0.99–1.00),0.99(95%CI 0.98–1.00),0.95(95%CI 0.91–0.99),0.93(95%CI 0.87–0.99),and 0.91(95%CI 0.83–1.00),*** within the 20%false positive rate,the diagnostic sensitivity was greater than 100%,100%,100%,90%,90%,and 88%,***:Six variables could be used for prenatal diagnosis of *** them,AVLR-ED and Q score of AVLR-ED,obtained on the 4CHV,were more convenient to acquire and had higher diagnostic accuracy.

关键词： AVSDs fetus echocardiography Q score quantitative

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Changes in global land surface frozen ground and freeze-thaw processes during 1950-2020 based on ERA5-Land data

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Advances in Climate Change Research 2024年第2期15卷 265-274页

作者： Yong YANG Ren-Sheng CHEN Yong-Jian DING Hong-Yuan LI Zhang-Wen LIU Qilian Alpine Ecology and Hydrology Research Station Key Laboratory of Ecological Safety and Sustainable Development in Arid LandsNorthwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 73000China State Key Laboratory of Cryospheric Sciences Northwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 73000China Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection College of Resources and EnvironmentLinyi UniversityLinyi 276000China

Frozen ground(FG)plays an important role in global and regional climates and environments through changes in land freeze-thaw processes,which have been conducted mainly in different ***,the changes in land surface freeze-thaw processes under climate change on a global scale are still *** on ERA5-Land hourly land skin temperature data,this study evaluated changes in the global FG area,global land surface first freeze date(FFD),last freeze date(LFD)and frost-free period(FFP)from 1950 to *** results show that the current FG areas(1991-2020 mean)in the Northern Hemisphere(NH),Southern Hemisphere(SH),and globe are 68.50×10^(6),9.03×10^(6),and 77.53×10^(6)km^(2),which account for 72.4%,26.8%,and 60.4%of the exposed land(excluding glaciers,ice sheets,and water bodies)in the NH,SH and the globe,respectively;further,relative to 1951-1980,the FG area decreased by 1.9%,8.8%,and 2.8%,*** FG at lower latitudes degrades to intermittently FG,and intermittently FG degrades to non-frozen ground,which caused the global FG boundary to retreat to higher latitudes from 1950 to *** annual FG areas in the NH,SH,and globe all show significant decreasing trends(p<0.05)from 1950 to 2020 at-0.32×10^(6),-0.22×10^(6),and-0.54×10^(6)km^(2)per decade,*** FFP prolongation in the NH is mainly influenced by LFD advance,while in the SH it is mainly controlled by FFD *** prolongation trend of FFP in the NH(1.34 d per decade)is larger than that in the SH(1.15 d per decade).

关键词： Frozen ground Freeze-thaw processes Frost-free period First freeze date Global warming

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Simulation of unmanned survey path planning in debris flow gully based on GRE-Bat algorithm

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Journal of Mountain Science 2024年第12期21卷 4062-4082页

作者： LIU Dunlong FENG Duanguo SANG Xuejia ZHANG Shaojie YANG Hongjuan College of Software Engineering Chengdu University of Information TechnologyChengdu 610225China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China

Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data *** planning of UAV advancing along river valleys in wild environments is one of the first and most difficult problems faced by unmanned surveys of debris flow *** study proposes a new hybrid bat optimization algorithm,GRE-Bat(Good point set,Reverse learning,Elite Pool-Bat algorithm),for unmanned exploration path planning of debris flow sources in outdoor *** the GRE-Bat algorithm,the good point set strategy is adopted to evenly distribute the population,ensure sufficient coverage of the search space,and improve the stability of the convergence accuracy of the ***,a reverse learning strategy is introduced to increase the diversity of the population and improve the local stagnation problem of the *** addition,an Elite pool strategy is added to balance the replacement and learning behaviors of particles within the population based on elimination and local perturbation *** demonstrate the effectiveness of the GRE-Bat algorithm,we conducted multiple simulation experiments using benchmark test functions and digital terrain *** to commonly used path planning algorithms such as the Bat Algorithm(BA)and the Improved Sparrow Search Algorithm(ISSA),the GRE-Bat algorithm can converge to the optimal value in different types of test functions and obtains a near-optimal solution after an average of 60 *** GRE-Bat algorithm can obtain higher quality flight routes in the designated environment of unmanned investigation in the debris flow gully basin,demonstrating its potential for practical application.

关键词： Bat algorithm Unmanned surveys Debris flow gully Path planning Unmanned aerial vehicle Reverse learning

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Neighborhood Subgraph-based Illicit Transaction Detection in Cryptocurrency Networks 12

Neighborhood Subgraph-based Illicit Transaction Detection in...

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12th IEEE International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2024

作者： Jin, Shenghao Yang, Qiwen Chen, Shengyu Zhang, Hui Beihang University State Key Laboratory of Complex & Critical Software Environment School of Computer Science & Engineering Beijing China

ISBN: (纸本)9798331510633

With the widespread adoption of cryptocurrencies, associated illicit activities such as money laundering, fraud, extortion, and Ponzi schemes have garnered significant attention. Traditional studies have extensively employed graph neural network technologies to identify illicit transactions, typically treating the entire transaction network as input to classify its nodes. Although these methods perform well on small-scale networks, they face performance limitations when processing large-scale blockchain transaction data. To address this issue, we propose a neighborhood subgraph-based method for detecting illicit transactions, integrating GCN and LSTM. In this approach, the GCN captures and integrates information from adjacent nodes for each transaction, facilitating a deep understanding of network structures. The LSTM is tasked with capturing the sequence and variations in fund flows, effectively tracking the direction and temporal characteristics of the money flow. Experimental results indicate that when using 3-hop neighborhood subgraphs, our model's performance surpasses other GCN-based methods. Compared to previous models that require considering the entire transaction network, our approach only needs to utilize information from an average of 80 nodes, significantly improving efficiency. © 2024 IEEE.

关键词： Graph neural networks

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Continual Adaptation for Unsupervised Time Series Anomaly Detection 9

Continual Adaptation for Unsupervised Time Series Anomaly De...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Yu, Ting Ding, Bo Wang, Xu College of Computer National University of Defense Technology State Key Laboratory of Complex & Critical Software Environment Changsha China

ISBN: (纸本)9798350376548

The application of time series anomaly detection has been widespread across various industrial scenarios. Although current algorithms for time series anomaly detection have shown promising results, they primarily focus on the characteristics of the data distribution within the current training set. This often leads to a lack of adaptability to changes in data distribution while retaining the ability to accommodate new data and preserve existing knowledge. Given the dynamic nature of real-world industrial production systems, the issue of continuous updating of time series anomaly detection algorithms warrants significant attention. However, existing time series anomaly detection algorithms predominantly rely on unsupervised methods, thereby resulting in sparse availability of relevant update strategies. Recognizing this challenge, this paper proposes an unsupervised anomaly detection algorithm update mechanism that incorporates continual learning methods. This approach aims to address the issue in time series anomaly detection algorithms stemming from the absence of label guidance, which leads to a lack of continuous updating capability in models. The method employs a strategy of retaining key information about the past data's feature distribution to ensure memory of past information, thus fulfilling the need for updating and adapting to new data features with minimal storage overhead. Compared to other potential improvement algorithms for continual updating, our approach achieves comparable performance with significantly reduced overhead. In particular, it addresses the issue in the current domain of unsupervised time series anomaly detection, where the lack of labeled data results in a deficiency of continuous updating methods. © 2024 IEEE.

关键词： Time series

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Landslide susceptibility prediction method based on HSOM and IABPA-CNN in Wenchuan earthquake disaster area

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Journal of Mountain Science 2024年第12期21卷 4001-4018页

作者： LIU Dunlong XIANG Qian SANG Xuejia ZHANG Shaojie YANG Hongjuan College of Software Engineering Chengdu University of Information TechnologyChengdu 610225China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China

Landslide susceptibility prediction assesses the likelihood of landslides occurring in specific areas,providing crucial scientific support for mitigating the threat to people’s lives and property posed by landslide *** address the challenges in the existing landslide susceptibility prediction methods,such as insufficient representativeness of selected nonlandslide points,limited ability to capture nonlinear relationships,and a tendency to fall into local optima,the traditional Self-Organizing Maps(SOM)is improved in this paper by using a hierarchical approach to form Hierarchical Self-Organizing Maps(HSOM),and a model integrating the Information Value(IV),Adaptive Bat Precise Algorithm(ABPA),and Convolutional Neural Network(CNN)is proposed,termed *** factors such as topography,basic geology and hydrometeorology were selected with the 2008 Wenchuan earthquake-hit disaster area as the study *** data were preprocessed,Pearson correlation coefficient,tolerance(TOL),and variance inflation factor(VIF)were employed to assess the correlation among all the ***,the information value for each evaluation factor's classification was calculated,thereby establishing a high-quality sample dataset,which was input into the IABPA-CNN model and IVCNN model(contrast model),*** Receiver Operating Characteristic(ROC)curve was used to compare and analyze the accuracy and *** Area Under Curve(AUC)values for the two models is 0.89 and 0.85,respectively,indicating that the IABPA-CNN model has higher predictive *** with the IV-CNN model,the proportion of landslides predicted by the IABPA-CNN model in the high susceptibility and very high susceptibility area increased to 28.18%and 30.76%,*** the area proportions of very low susceptibility and low susceptibility area increased,the proportion of landslides quantity ***,Monte Carlo method was employed to analyze the uncertaint

关键词： Landslide Susceptibility Information value Convolutional neural network Bat algorithm Monte Carlo

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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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UKF‐MOT:An unscented Kalman filter‐based 3D multi‐object tracker

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1031-1041页

作者： Meng Liu Jianwei Niu Yu Liu Collective Intelligence&Collaboration Laboratory China North Artificial Intelligence and Innovation Research InstituteBeijingChina China North Vehicle Research Institute BeijingChina State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijingChina School of Computer Science and Engineering Beihang UniversityBeijingChina

Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for *** the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous *** conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.

关键词： autonomous vehicle transportation

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Potassium isotopic signatures of modern offshore detrital sediments from different climatic regimes and the implications

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Science China Earth Sciences 2024年第2期67卷 405-419页

作者： Jun MU Tianyu CHEN Qian YU Shichao AN Jianfang CHEN Xuefa SHI Weiqiang LI State Key Laboratory for Mineral Deposits Research School of Earth Sciences and EngineeringNanjing UniversityNanjing210023China Ministry of Education Key Laboratory for Coast and Island Development School of Geography and Ocean ScienceNanjing UniversityNanjing210023China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural ResourcesHangzhou310012China Key Laboratory of Marine Geology and Metallogeny First Institute of OceanographyMinistry of Natural ResourcesQingdao266061China

Potassium isotopes are a novel tracer for continental *** K isotope studies on chemical weathering generally targeted weathering profiles under a particular climate region,yet the effects of chemical weathering on K isotopes under different climatic backgrounds remain ***,little is known about the K isotope signatures of modern unconsolidated detrital ***,we report K isotopic data of surficial seafloor sediments from continental shelves along the east coast of China(ECC),as well as those around the tropical Hainan island in the northern South China *** ECC sediments have a relatively narrow distribution ofδ^(41)K(with reference to NIST3141a)values,which range from(-0.40±0.01)‰to(-0.57±0.04)‰,with an average of(-0.51±0.09)‰.By contrast,δ^(41)K values of Hainan offshore sediments display a larger variation,ranging from(-0.28±0.07)‰to(-0.67±0.02)‰.Theδ^(41)K values of Hainan offshore sediments exhibit negative correlations with the chemical index of alteration(CIA),Al/K,Ti/K,and total iron(FeT),which underlines the control of chemical weathering on K isotopic signatures of detritus inputs into *** also measured Mg isotope compositions for the same samples;interestingly,the variability inδ^(26)Mg of the samples is small(~0.24‰)for all ECC and Hainan offshore sediments,andδ^(26)Mg values do not show clear correlations with indexes of chemical *** study demonstrates the link between K isotopic variability of detrital sediments and climatic conditions including rainfall intensity,which indicates that K isotopes of the detrital component of marine sediments could be applied to study Earth’s climate in deep ***δ^(41)K values of the offshore detrital sediments are significantly less variable than those of pelagic marine sediments,highlighting the importance of distinguishing the effects of diagenesis and neoformation of clay minerals from continental weathering in attempts to study deep-time climate-weathering

关键词： Potassium isotopes Magnesium isotopes Chemical weathering Detrital sediments Climatic regimes Mean annual precipitation

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