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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Chen, Kang Wen, Ming Jia, Haoxiang Wu, Rongxin Jin, Hai Wuhan430074 China Jin YinHu Laboratory Wuhan430074 China School of Informatics Xiamen University Xiamen361005 China School of Computer Science and Technology HUST Wuhan430074 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China

ISBN: (纸本)9798350330663

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL-F I are effective that contribute significantly to its overall performance. © 2024 IEEE.

关键词： Errors

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Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with data Privacy Protection

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Computer Modeling in engineering & Sciences 2023年第10期137卷 717-732页

作者： You Lu Linqian Cui YunzheWang Jiacheng Sun Lanhui Liu School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

Most studies have conducted experiments on predicting energy consumption by integrating data formodel ***, the process of centralizing data can cause problems of data ***,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning ***, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te ***, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.

关键词： Energy consumption forecasting federated learning data privacy protection Q-learning

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Recognition of field roads based on improved U-Net++Network

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International Journal of Agricultural and Biological engineering 2023年第2期16卷 171-178页

作者： Lili Yang Yuanbo Li Mengshuai Chang Yuanyuan Xu Bingbing Hu Xinxin Wang Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China Kunlun Beidou Intelligence Technologies Co.Ltd. Beijing 102200China

Unmanned driving of agricultural machinery has garnered significant attention in recent years,especially with the development of precision farming and sensor *** achieve high performance and low cost,perception tasks are of great *** this study,a low-cost and high-safety method was proposed for field road recognition in unmanned agricultural *** approach of this study utilized point clouds,with low-resolution Lidar point clouds as inputs,generating high-resolution point clouds and Bird's Eye View(BEV)images that were encoded with several basic *** a BEV representation,road detection was reduced to a single-scale problem that could be addressed with an improved U-Net++neural *** enhancements were proposed for U-Net++:1)replacing the convolutional kernel in the original U-Net++ with an Asymmetric Convolution Block(ACBlock);2)adding a multi-branch Asymmetric Dilated Convolutional Block(MADC)in the highest semantic information layer;3)adding an Attention Gate(AG)model to the long-skip-connection in the decoding *** results of experiments of this study showed that our algorithm achieved a Mean Intersection Over Union of 96.54% on the 16-channel point clouds,which was 7.35 percentage points higher than U-Net++.Furthermore,the average processing time of the model was about 70 ms,meeting the time requirements of unmanned driving in agricultural *** proposed method of this study can be applied to enhance the perception ability of unmanned agricultural machinery thereby increasing the safety of field road driving.

关键词： image segmentation unmanned agricultural machinery field roads point cloud super-resolution point cloud bird’s eye view

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Plankton detection based on improved YOLOv5s

Plankton detection based on improved YOLOv5s

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2023 International Conference on Optics and Machine Vision, ICOMV 2023

作者： Li, Xiaohui Gu, Shenming Wei, Dan School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China

ISBN: (纸本)9781510664777

With the rapid development of marine resource exploitation, planktonic microorganisms have gradually become one of the research directions in the field of machine vision. In order to optimize the detection of small targets in the images of planktonic microorganisms, this paper proposes an improved YOLOv5s model to enhance the detection of planktonic microorganisms. The SE attention mechanism allows the network to pay more attention to the target feature area and suppress useless feature information. The PANet feature module is improved into a weighted bidirectional pyramidal BiFPN feature fusion network to achieve high-efficiency bidirectional cross-scale connectivity and weighted feature map fusion. The results show that the combination of the SE attention mechanism and BiFPN feature fusion improves the mAP value by 6.96%, increases the precision by 11.45%, and reduces the loss rate by 1.62%. Our proposed method effectively solves the problems of false detection, missed detection, and low detection accuracy of the existing models for small target detection. © 2023 SPIE. All rights reserved.

关键词： Computer vision

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Fault Estimation for Nonlinear Systems with Disturbance Based on Reinforcement Iterative Learning Strategy 13

Fault Estimation for Nonlinear Systems with Disturbance Base...

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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Chen, Zhengquan Huang, Ruirui Zhang, Zhiheng Hou, Yandong Henan University School of Computer and Information Engineering Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Artificial Intelligence Henan University Zhengzhou450000 China

ISBN: (纸本)9798350361674

This paper addresses the problem of low fault estimation accuracy in Lipschitz nonlinear systems with external disturbances, presenting an innovative strategy based on a Reinforcement Iterative Learning (RIL) framework. Initially, we conducted precise mathematical modeling, comprehensively considering the impact of Lipschitz non-linearity and external disturbances. Subsequently, an iterative learning fault observer was designed, and the optimization problem for its parameters was reformulated into a reinforcement learning task, clearly defining the states, actions, and reward functions. Employing the Advantage Actor-Critic (A2C) reinforcement learning method, we optimized the gain parameters of the iterative learning fault estimator to approximate non-linearity. Simultaneously, with the increase in iteration count, the neural network weights were continually optimized, achieving accurate fault estimation. Experimental results demonstrate a significant improvement in fault estimation using the proposed method. © 2024 IEEE.

关键词： Adversarial machine learning

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A method for correcting characteristic X-ray net peak count from drifted shadow peak

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Nuclear Science and Techniques 2023年第11期34卷 155-167页

作者： Lin Tang Xing‑Ke Ma Kai‑Bo Shi Yeng‑Chai Soh Hong‑Tao Shen College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse *** trained model was validated using simulated *** relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM *** a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping *** slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping *** model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,*** measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

关键词： Peak correction Triangular shaping Deep learning Long short-term memory Convolutional neural network X-ray fluorescence spectroscopy Silicon drift detector

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Optimization Scheme of Active Reconfigurable Intelligent Surface Assisted Internet of Vehicles System

Optimization Scheme of Active Reconfigurable Intelligent Sur...

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2024 International Workshop on Automation, Control, and Communication engineering, IWACCE 2024

作者： Xu, Zhuoran Ni, Yiyang Sun, Wenxue Liu, Yaxuan Mo, Zhaoying Zhao, Haitao Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal University Nanjing210013 China

ISBN: (数字)9781510685420

ISBN: (纸本)9781510685413

In recent years, the Integrated Satellite Aerial Terrestrial (I-SAT) network has emerged as an innovative and comprehensive communication system and has gained wide attention. However, in the face of a complex external environment, it will still be affected by other factors. This paper considers the integration of Unmanned Aerial Vehicle (UAV) with Reconfigurable Intelligent Surfaces (RIS) and introduces a novel sub-connected active RIS architecture under the energy consumption constraints of the system. Firstly, a joint RIS phase shift, amplification, and UAV trajectory optimization algorithm is proposed to achieve a high achievable sum rate. Secondly, a Deep Deterministic Policy Gradient (DDPG) algorithm is utilized to solve the optimization problem and converge in a continuous action space. Finally, simulation results confirm that the proposed approach significantly enhances the system performance compared to the other schemes. © 2024 SPIE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection 24

Machine Learning is All You Need: A Simple Token-Based Appro...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used bigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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Identification of Phtato Leaf Disease Based on The TS-Resnet Deep Learing Method 21

Identification of Phtato Leaf Disease Based on The TS-Resnet...

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21st International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2023

作者： Duan, Xiao-Yue Gu, Shen-Ming School of information Engineering Zhejiang Ocean University Zhoushan316022 China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9798350303810

At present, the incidence of potato disease is large, the disease is complex, and it is difficult to control, which seriously affects the potato yield and quality. Therefore, in order to realize the potato disease image recognition technology under small sample data, by using attention mechanism, transfer learning and data enhancement technology, the potato leaf disease image is accurately analyzed. This paper proposes a potato leaf disease recognition method based on TS-ResNet(Transform learning and SE) deep learning method. Based on the original ResNet101 model, its full connectivity layer is modified to add transfer learning and attention mechanism. In order to increase the number of potato disease part of the data set, the original graph can be flipped, horizontal flip and other operations to avoid the phenomenon of overfitting in the original model. The results of this study can provide some support for the intelligent research of potato leaf disease identification. © 2023 IEEE.

关键词： Disease recognition Potato ResNet101 SE attention mecha-nism Transfer learning

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Development and validation of a nomogram model for predicting overall survival in patients with gastric carcinoma

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World Journal of Gastrointestinal Oncology 2025年第2期17卷 132-143页

作者： Guan-Zhong Liang Xiao-Sheng Li Zu-Hai Hu Qian-Jie Xu Fang Wu Xiang-Lin Wu Hai-Ke Lei Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer HospitalChongqing 400030China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center Chongqing University Cancer HospitalChongqing 400030China Department of Health Statistics School of Public HealthChongqing Medical UniversityChongqing 400016China Research Center for Medicine and Social Development School of Public HealthChongqing Medical UniversityChongqing 400016China The Research Center of Big Data Chongqing University Cancer HospitalChongqing 400030China

BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health *** forecasting of overall survival(OS)is of paramount importance for the clinical management of individuals afflicted with this *** To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric *** data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and *** absolute shrinkage and selection operator,univariate,and multivariate Cox regression analyses were employed to identify independent prognostic factors.A nomogram model was developed to predict gastric cancer patient *** model's predictability and discriminative ability were evaluated via receiver operating characteristic *** evaluate the clinical utility of the model,Kaplan-Meier and decision curve analyses were *** A total of ten independent prognostic factors were identified,including body mass index,tumor-node-metastasis(TNM)stage,radiation,chemotherapy,surgery,albumin,globulin,neutrophil count,lactate dehydrogenase,and platelet-to-lymphocyte *** area under the curve(AUC)values for the 1-,3-,and 5-year survival prediction in the training set were 0.843,0.850,and 0.821,*** AUC values were 0.864,0.820,and 0.786 for the 1-,3-,and 5-year survival prediction in the validation set,*** model exhibited strong discriminative ability,with both the time AUC and time C-index exceeding *** with TNM staging,the model demonstrated superior clinical ***,a nomogram was developed via a web-based *** This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients,which demonstrat

关键词： Gastric carcinoma Prediction Overall survival Nomogram Prospective

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