检索结果-南通市图书馆

GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

science China(Information sciences) 2025年第1期68卷 145-158页

作者： Wei JU Yiyang GU Zhengyang MAO Ziyue QIAO Yifang QIN Xiao LUO Hui XIONG Ming ZHANG school of computer science National Key Laboratory for Multimedia Information ProcessingPeking University Artificial Intelligence Thrust The Hong Kong University of Science and Technology Department of computer science University of California

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.

关键词： graph representation learning graph neural networks graph contrastive learning graph augmentations graph pooling

Detection and Recognition of Spray Code Numbers on Can Surfaces Based on OCR

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

computers, Materials & Continua 2025年第1期82卷 1109-1128页

作者： Hailong Wang Junchao Shi school of computer science Zhongyuan University of TechnologyZhengzhou450007China

A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number *** the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection *** terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background *** addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production *** experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line *** Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.

关键词： Can coding recognition differentiable binarization network scene visual text recognition model pruning and quantification transport model

The linear self-attracting diffusion driven by the weighted-fractional Brownian motion II: The parameter estimation

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

science China Mathematics 2025年

作者： Litan Yan Rui Guo Wenyi Pei Department of statistics College of Science Donghua University school of statistics and Mathematics Zhejiang Gongshang University

Let Ba,bbe a weighted-fractional Brownian motion with Hurst indexes a and b such that a>-1and 0≤b<1∧（1+a）.In this paper,we consider the linear self-attracting diffusion ■ interacting diffusion (see Cranston and Le Jan （1995）).Under the continuous observation,we study asymptotic ■ converges in distribution to the sum of the chi-square random variable with 1 degree of freedom and the random variable Z1a,b.

关键词：

Multi-receptive field interaction network for shape from polarization

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

science China(Information sciences) 2025年第1期68卷 394-395页

作者： Yini PENG Rui LIU Zhiyuan ZHANG Zhongyuan WANG Jiayi MA Xin TIAN Electronic Information school Wuhan University computer science school Wuhan University Wuhan Institute of Quantum Technology

Shape from polarization (SfP) method can use the polarization information in reflected light to estimate the surface normal of the target,which can further reconstruct the shape of the *** a simple image capture process,it can use low-cost equipment to meet a high precision imaging requirement,which can be used in remote scenes and other applications.

关键词：

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

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

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

LoRa Sense:Sensing and Optimization of LoRa Link Behavior Using Path-Loss Models in Open-Cast Mines

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

computer Modeling in Engineering & sciences 2025年第1期142卷 425-466页

作者： Bhanu Pratap Reddy Bhavanam Prashanth Ragam school of computer science and Engineering VIT-AP UniversityAmaravati52237India

The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic *** study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining *** IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable *** advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and *** field tests are conducted in various opencast mines to investigate coverage potential and signal *** is tested in the Tadicherla open-cast coal mine in ***,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal *** field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of *** the O2O scenario,the Ericsson model

关键词： Internet of things long range wireless area network ZigBee mining environments path-loss models coefficient of determination mean square error

PS-CoT-Adapter: adapting plan-and-solve chain-of-thought for scienceQA

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

science China(Information sciences) 2025年第1期68卷 392-393页

作者： Qun LI Haixin SUN Fu XIAO Yiming WANG Xinping GAO Bir BHANU school of computer science Nanjing University of Posts and Telecommunications Purple Mountain Laboratories Department of Electrical and computer Engineering University of California at Riverside

Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1-3]has investigated chain-of-thought (CoT) reasoning in complex multimodal scenarios,such as science question answering (scienceQA) tasks [4],by fine-tuning multimodal models through human-annotated CoT ***,collected CoT rationales often miss the necessary rea-soning steps and specific expertise.

关键词：

Normalized Solutions of Nonlinear Choquard Equations with Nonconstant Potential

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

应用数学 2025年第1期38卷 14-29页

作者： LI Nan XU Liping school of Mathematics and statistics Henan University of Science and TechnologyLuoyang 471000China

In this paper,we mainly focus on a type of nonlinear Choquard equations with nonconstant *** appropriate hypotheses on potential function and nonlinear terms,we prove that the above Choquard equation with prescribed 2... 详细信息

关键词： Nonlinear Choquard equation Potential function Variational method Normalized solution

On construction of strong orthogonal arrays and column-orthogonal strong orthogonal arrays of strength two plus

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

science China Mathematics 2025年

作者： Bochuan Jiang Yaping Wang Fasheng Sun school of Mathematics and statistics Beijing Jiaotong University Key Laboratory of Advanced Theory and Application in statistics and Data science Ministry of EducationSchool of Statistics East China Normal University Key Laboratory for Applied statistics of Ministry of Education and school of Mathematics and statistics Northeast Normal University

computer experiments require space-filling designs with good low-dimensional projection *** orthogonal arrays are a type of space-filling design that provides better stratifications in low dimensions than ordinary orthogonal arrays. In this paper, we address the problem of constructing strong orthogonal arrays and column-orthogonal strong orthogonal arrays of strength two plus. Existing methods typically rely on regular designs or specific nonregular designs as base orthogonal arrays, limiting the sizes of the final ***, we propose two general methods that are easy to implement and applicable to a wide range of base orthogonal arrays. These methods produce space-filling designs that can accommodate a large number of factors,provide significant flexibility in terms of run sizes, and possess appealing low-dimensional projection ***, these designs are ideal for computer experiments.

关键词：