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An Anchor-Free Refining Feature Pyramid Network for Dense and Multioriented Wheat Spikes Detection under UAV

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jiao, Lin Liu, Haiyun Liang, Zheng Chen, Peng Wang, Rujing Liu, Kang Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Internet Hefei230601 China Hefei Institutes of Physical Science Chinese Academy of Sciences Institute of Intelligent Machines Hefei230031 China University of Science and Technology of China Science Island Branch Graduate School Anhui Hefei230031 China Anhui University School of Internet Hefei230031 China School of Internet Institutes of Physical Science and Information Technology Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui Hefei230601 China Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Science Hefei230031 China Hong Kong Polytechnic University Department of Aeronautical and Aviation Engineering Hong Kong

The number of wheat spikes is a crucial index for evaluating the yield, and the precise detection of wheat spikes in an image plays an important role. Among various methods, deep learning-based approaches show impressive results in the task of wheat spike detection. However, the precise detection and recognition of wheat spike encounters large challenges due to complicated backgrounds, arbitrary orientations, and dense distribution in wheat spike images. To alleviate these issues, we have developed an anchor-free refining feature pyramid network (AFRFPN) that gets rid of horizontal bounding boxes (HBBs) from the network. First, the refining feature pyramid network (RFPN) has been introduced into extract richer features of wheat spike with highly variant appearances and multiple scales. Then, learning from the idea of coarse-to-fine, the two-stage anchor-free oriented detection (AFOD) module has been designed. The AFOD module first generates a set of coarse detection (CoDet) results in the way of anchor-free and then further fines them to achieve high-quality predicting bounding boxes (BBs). The number of wheat spike images is insufficient, resulting in poor performance of wheat spike detection modules. To mitigate the lack of the data in the task of oriented wheat spike detection, based on the global wheat head detection (GWHD) dataset, we released a new large-scale wheat spike dataset by relabeling the samples, termed it as rotated GWHD (R-GWHD) dataset. Massive experiments show that the proposed method can achieve 90.6% mAP and 96.7% recall, outperforming other state-of-the-art methods. Additionally, the experiments related to the counting of wheat spikes have been conducted, showing that the developed module can achieve the MAE of 4.95 and RMSE of 7.68, which demonstrates the excellent performance of the proposed method. © 2024 IEEE.

关键词： Deep learning

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Estimation and spatiotemporal variations of black carbon aerosol over the North China Plain in 2018

Estimation and spatiotemporal variations of black carbon aer...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Cai, Kun Qi, Yongkai Li, Shenshen Zhang, Shuo College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510668898

With the rapid economic development in the North China Plain, the significant emissions of black carbon (BC) aerosol have exerted notable impacts on climate, environment, and health, making it a hot topic in environmental studies in recent years. To compensate for the insufficient ground monitoring data, the PM2.5 satellite remote sensing inversion data and chemical model simulation data were utilized, applying the proportionality factor method to estimate the satellite concentration of BC. The estimation results were validated using BC monitoring data from seven stations in 2018, and based on this, the spatiotemporal variations of BC satellite concentration was obtained. The results showed that the estimated BC concentration using the proportionality factor method had good accuracy, with a correlation coefficient (R2) of 0.72. The spatial distribution of BC over the North China Plain in 2018 exhibited a decreasing trend from the inner region to the outer region, with high concentration areas mainly located in provinces with high straw burning and steel production. The seasonal variation of BC in the North China Plain in 2018 was pronounced, showing higher concentrations in autumn and winter and lower concentrations in spring and summer. © 2023 SPIE.

关键词： Satellites

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Implications of Preference and Reference Effects on A Green Supply Chain Considering Subsidy Withdrawal 12

Implications of Preference and Reference Effects on A Green ...

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12th International Symposium on Project Management, ISPM 2024

作者： Chen, Shan Fang, Meiqi Wang, Linlin Su, Jiafu Chongqing Industrial Big Data Innovation Center Co. Ltd. Chongqing China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing China School of Management Science and Engineering Chongqing Technology and Business University Chongqing China Western Branch of China Academy of Information and Communications Technology Chongqing China International College Krirk University Bangkok Thailand

ISBN: (纸本)9798331302344

In order to examine the impact of the withdrawal of government green subsidies on the green market, this paper formulates a two-period model to examine a green supply chain composed of a government and two manufacturers. We construct a basic model of government green subsidies in the first period, and formulate four scenarios in the second period considering the market expansion effect, green preference effect, price reference effect, and quality reference effect. We examine the impacts of the above effects on equilibrium decision-making and profits of the green supply chain through comparing and numerical analysis. We find that: 1) Only the market size enlarges to a certain degree, the profit of green manufacturer would increase after subsidy withdrawal. 2) Both green and traditional manufacturers can benefit from the consumers’ green preference, but it will hinder some traditional manufacturers from investing in green production. 3) The consumer price reference effect will damage green manufacturers' profits and reduce the greenness of products, while the traditional manufacturer could benefit from it under certain conditions. 4) Appropriate consumer green quality preferences are beneficial to the green market and can increase the profits of green manufacturers, but they will also provide free-riding opportunities for traditional manufacturers. © 2024 ISPM. All Rights Reserved.

关键词： Green manufacturing

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Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis 29

Sentiment Interpretable Logic Tensor Network for Aspect-Term...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Zhang, Bowen Huang, Xu Huang, Zhichao Huang, Hu Zhang, Baoquan Fu, Xianghua Jing, Liwen College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China JD Intelligent Cities Research Beijing China School of Cyber Science and Technology University of Science and Technology of China Hefei China Shenzhen China

Aspect-term sentiment analysis (ATSA) is an important task that aims to infer the sentiment towards the given aspect-terms. It is often required in the industry that ATSA should be performed with interpretability, computational efficiency and high accuracy. However, such an ATSA method has not yet been developed. This study aims to develop an ATSA method that fulfills all these requirements. To achieve the goal, we propose a novel Sentiment Interpretable Logic Tensor Network (SILTN). SILTN is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language (FOL). To realize SILTN with high inferring accuracy, we propose a novel learning strategy called the two-stage syntax knowledge distillation (TSynKD). Using widely used datasets, we experimentally demonstrate that the proposed TSynKD is effective for improving the accuracy of SILTN, and the SILTN has both high interpretability and computational efficiency. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Sentiment analysis

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ScMonica: Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis

ScMonica: Single-cell Mosaic Omics Nonlinear Integration and...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Xiaoli Zhang, Rui Aslam, Saba Li, Huijun Chen, Yuxi Zhang, Zequn Huang, Ruey-Song Wu, Hongyan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Centre for Cognitive and Brain Sciences University of Macau Taipa China Faculty of Science and Technology University of Macau Taipa China Shenzhen college of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Faculty of Health Sciences University of Macau Taipa China

ISBN: (纸本)9798350386226

Single-cell mosaic integration has revolutionized our understanding of cellular heterogeneity, offering unprecedented resolution into cellular states and contexts. While large language models (LLMs) have achieved success in the analysis of single-cell omics data, their application specifically focused on the integration of mosaic data remains limited. Current computational approaches often fall short in addressing these challenges. They frequently depend on assumptions of data completeness and uniform quality, failing to manage the variability and noise effectively introduced by missing data. Moreover, these models might not efficiently process large, diverse datasets or account for biological heterogeneity and long-ranging dependencies across various data types. To address these shortcomings, we introduce the Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis (scMonica) framework, which employs a LSTM-transformer hybrid architecture. This innovative model combines the strengths of Long Short-Term Memory (LSTM) networks, which excel at capturing long-range dependencies within sequential gene expression patterns, with transformers, renowned for their attention mechanisms that handle the complex, non-linear interactions characteristic of multi-layered datasets. By leveraging these complementary strengths, our approach enhances the integration process significantly, allowing for nuanced management of the intrinsic heterogeneity and sparsity of mosaic datasets. Comprehensive evaluations demonstrate the robustness and effectiveness of our approach, offering unparalleled versatility and accuracy in multi-omics data analysis. These advancements underscore scMonica's potential to drive significant insights in single-cell developmental biology, oncology, and beyond. We discuss the underlying technologies, analyze their applications, and contemplate future directions that promise to extend the boundaries of both research and clinical domains. © 2024 IEEE.

关键词： Gene expression

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Testing Graph database Systems via Equivalent Query Rewriting

Testing Graph Database Systems via Equivalent Query Rewritin...

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International Conference on Software engineering (ICSE)

作者： Qiuyang Mang Aoyang Fang Boxi Yu Hanfei Chen Pinjia He School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China School of Science and Engineering The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China China Shenzhen Research Institute of Big Data China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Graph database Management Systems (GDBMS), which utilize graph models for data storage and execute queries via graph tra-versals, have seen ubiquitous usage in real-world scenarios such as recommendation systems, knowledge graphs, and social networks. Much like Relational database Management Systems (RDBMS), GDBMS are not immune to bugs. These bugs typically manifest as logic errors that yield incorrect results (e.g., omitting a node that should be included), performance bugs (e.g., long execution time caused by redundant graph scanning), and exception issues (e.g., unexpected or missing exceptions). This paper adapts Equivalent Query Rewriting (EQR) to GDBMS testing. EQR rewrites a GDBMS query into equivalent ones that trigger distinct query plans, and checks whether they exhibit discrepancies in system behaviors. To facilitate the realization of EQR, we propose a general concept called Abstract Syntax Graph (ASG). Its core idea is to embed the semantics of a base query into the paths of a graph, which can be utilized to generate new queries with customized properties (e.g., equivalence). Given a base query, an ASG is constructed and then an equivalent query can be gener-ated by finding paths collectively carrying the complete semantics of the base query. To this end, we further design Random Walk Covering (RWC), a simple yet effective path covering algorithm. As a practical implementation of these ideas, we develop a tool GRev, which has successfully detected 22 previously unknown bugs across 5 popular GDBMS, with 15 of them being confirmed. In particular, 14 of the detected bugs are related to improper implementation of graph data retrieval in GDBMS, which is challenging to identify for existing techniques.

关键词： Social networking (online) Computer bugs Semantics Relational databases Syntactics database systems Logic

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Application research on Lightweight Vehicle Detection Based on YOLO 49

Application Research on Lightweight Vehicle Detection Based ...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Peng, Jun He, Yuanmin Jin, Shangzhu Dai, Haojun Peng, Fei Wu, Xiao Wu, Yu College of Mathematics Physics and Data Science Chongqing University of Science and Technology Chongqing401331 China Chongqing Sino-German Future Factory Research Institute Chongqing401331 China College of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing401331 China Chongqing University of Science and Technology Informatization Office Chongqing401331 China

ISBN: (纸本)9798350331820

A lightweight object detection algorithm based on YOLOv5 is proposed to address the problem of deploying detection models for traffic targets. This method was proposed to reduce the number of channels in the backbone and introduced GSConv to improve performance. At the same time, GSConv was improved to cut the parameters. The C3 module was replaced in the Neck with C2f to obtain more comprehensive gradient flow information. Finally, the model parameters are only 1.41M. Experiment on BDD100K public traffic dataset shows that the lightweight model reduces the number of parameters while losing a small amount of Recalls, and its performance is better than mainstream lightweight networks. © 2023 IEEE.

关键词： Object detection

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FELSDiff: Image Super-Resolution Based on Diffusion Model Fusing Feature Extraction with Latent Space

FELSDiff: Image Super-Resolution Based on Diffusion Model Fu...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Cancan Wu Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In addressing issues like an excessive number of network parameters and the loss of fine texture details in image super-resolution reconstruction approaches based on diffusion models, this passage introduces a novel image super-resolution model (FELSDiff). This model relies on the fusion of diffusion models, feature extraction, and latent space. FELSDiff utilizes a Pre-trained CNN for initial super-resolution to extract the primary low-band features and part high-band features of the image. The preliminary super-resolved image is then concatenated as a condition to this model. We introduce a cross-attention mechanism guided by the high-band features of the preliminary super-resolved image to enhance the network's feature extraction capability and obtain more effective features. In the U-Net structure, we incorporate a latent space strategy, enabling the model's training and inference processes to be conducted in the latent space, thereby reducing the number of network parameters. Experimental results on multiple standard datasets indicate that this method outperforms other advanced methods overall.

关键词： Training Runtime Computational modeling Superresolution Memory management Feature extraction Diffusion models Convolutional neural networks Image reconstruction Standards

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CONVERGENCE ANALYSIS OF THE TRANSFORMED GRADIENT PROJECTION ALGORITHMS ON COMPACT MATRIX MANIFOLDS

arXiv

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arXiv 2024年

作者： Ding, Wentao Li, Jianze Zhang, Shuzhong School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen China Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States

In this paper, to address the optimization problem on a compact matrix manifold, we introduce a novel algorithmic framework called the Transformed Gradient Projection (TGP) algorithm, using the projection onto this compact matrix manifold. Compared with the existing algorithms, the key innovation in our approach lies in the utilization of a new class of search directions and various stepsizes, including the Armijo, nonmonotone Armijo, and fixed stepsizes, to guide the selection of the next iterate. Our framework offers flexibility by encompassing the classical gradient projection algorithms as special cases, and intersecting the retraction-based line-search algorithms. Notably, our focus is on the Stiefel or Grassmann manifold, revealing that many existing algorithms in the literature can be seen as specific instances within our proposed framework, and this algorithmic framework also induces several new special cases. Then, we conduct a thorough exploration of the convergence properties of these algorithms, considering various search directions and stepsizes. To achieve this, we extensively analyze the geometric properties of the projection onto compact matrix manifolds, allowing us to extend classical inequalities related to retractions from the literature. Building upon these insights, we establish the weak convergence, convergence rate, and global convergence of TGP algorithms under three distinct stepsizes. In cases where the compact matrix manifold is the Stiefel or Grassmann manifold, our convergence results either encompass or surpass those found in the literature. Finally, through a series of numerical experiments, we observe that the TGP algorithms, owing to their increased flexibility in choosing search directions, outperform classical gradient projection and retraction-based line-search algorithms in several *** Codes 15A23, 49M37, 65K05, 90C26, 90C30 Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

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