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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bingcai Wei Hui Liu Chuang Qian Yi Jia Wangyu Wu Zhishan Li School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool UK School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns.

关键词： Couplings Uncertainty Neural networks Estimation Signal processing Robustness Stereo vision Image restoration Speech processing Autonomous vehicles

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Research and Implementation of Database Operation Recognition Based on YOLO v5 Algorithm 4

Research and Implementation of Database Operation Recognitio...

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4th International Conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Zhang, Liang Fang, Yuan Sheng, Jianqiao Zhang, Yicao Shi, Jin State Grid Anhui Electric Power Corporation Information Telecommunication Branch Anhui China North China Electric Power School of Control and Computer Engineering Beijing China

ISBN: (纸本)9781665406925

With the rapid development of information technology, traditional enterprises such as State Grid pay more and more attention to database security. In order to further strengthen the audit of database operations, a universal audit method is needed. This paper proposes a method for real-time database operation recognition based on the YOLO v5 algorithm. Through this method, real-time image recognition is performed on the database operation and maintenance interface, the instruction image is converted into a character sequence, and the database operation audit is completed according to the database audit rules. This article provides a new method for database auditing. © 2021 IEEE.

关键词： Database systems

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Selected Sample Retraining Semi-supervised Learning Method for Aerial Scene Classification 1st

Selected Sample Retraining Semi-supervised Learning Method f...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Tian, Ye Li, Ju Zhang, Liguo Sun, Jianguo Yin, Guisheng College of Computer Science and Technology Harbin Engineering University Harbin150001 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

ISBN: (纸本)9783030930455

The performance of scene classification for remote sensing images based on deep neural networks is limited by the number of labeled data. To alleviate this problem, a variety of methods have been proposed to apply semi-supervised learning to exploit both labeled and unlabeled samples for training classifiers, but most of them still require a certain number of labeled samples considering the complex context relationship and huge spatial differences of remote sensing images. In this paper, we proposed a novel selected sample retraining semi-supervised learning method (S2 R) that is simple but works efficiently on scene classification remote sensing. First, we train several models independently, each model is trained for only a few epochs, and use them to label samples in the unlabeled data set. Then, the labeled unlabeled data set is divided into low-noise labeled data set and sub-unlabeled data set through the high probability sample selection method. Finally, the two segmented data sets are combined with the labeled data sets to train a scene classifier based on the semi-supervised learning method. To verify the effectiveness of the proposed method, it is further compared with several state-of-the-art semi-supervised classification approaches. The results demonstrate that our method consistently outperforms the previous methods on the condition of only a few labeled samples over the scene classification for remote sensing images. © 2021, Springer Nature Switzerland AG.

关键词： Remote sensing

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A Real-time Hand Gesture Recognition System for Human-computer and Human-Robot Interaction

A Real-time Hand Gesture Recognition System for Human-Comput...

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2022 International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2022

作者： Ponzi, Valerio Iacobelli, Emanuele Napoli, Christian Starczewski, Janusz Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology al. Armii Krajowej 36 Czestochowa42-200 Poland

The proposed hand gesture recognition (HGR) system is designed to enhance human-computer interaction (HCI) and human-robot interaction (HRI), which are crucial areas of research aimed at improving the way humans interact with computer or robot systems. With the growing need for intelligent computers and robots in a range of applications, including healthcare, manufacturing, and education, both HCI and HRI have gained significant importance. In this context, the HGR system plays a vital role by enabling natural and intuitive communication between humans and technology through hand gestures. The presented system uses a single camera and efficient image processing techniques that enable real-time gesture detection. Unlike other methods, our approach employs a basic video camera, which is widely available on most computers, eliminating the need for expensive and specialized hardware. © 2022 Copyright for this paper by its authors.

关键词： Human robot interaction

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Method for Measuring Leaf Area of Brassica Napus Using Image Analysis 21

Method for Measuring Leaf Area of Brassica Napus Using Image...

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5th International Conference on computer Science and Application engineering, CSAE 2021

作者： Liu, Zhenqi Liu, Changhua Chen, Jiaxi University of Sheffield Automatic Control and Systems Engineering South Yorkshire Sheffield United Kingdom School of Mathematics and Computer Science Wuhan Polytechnic University Hubei Wuhan China

ISBN: (纸本)9781450389853

Leaf is an important part of plants. A rapid and accurate detection of plant leaf area is an important guidance to reasonable fertilizer application and accurate sprinkler irrigation. In order to solve the problem of large error and incomplete target region in segmenting leaves when people using Otus's threshold method, a method based on watershed algorithm was proposed to segment the leaves of Brassica napus. Firstly, the images of relevant crop leaves are collected by the digital camera on site. Then these images are preprocessed by Sobel operator and morphological operation. Thirdly, labeled images are segmented by watershed algorithm to get target images. Finally the leaf area is calculated by counting the number of pixels in target area. The experimental results show that this method can solve the problem of incomplete segmentation of the Otus's threshold method and effectively reduce the measurement error to less than 2.2%.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Mathematical morphology

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Potential Transmission Choice for Internet of Things(IoT):Wireless and Batteryless Communications and Open Problems

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China Communications 2021年第2期18卷 241-249页

作者： Zhan Xu Guanjie Hu Minzheng Jia Lan Dong School of Information and Communication Engineering and with Key Laboratory of Modern Measurement&Control Technology Ministry of EducationBeijing Information Science&Technology UniversityChina Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina Department of Information Engineering Beijing Polytechnic CollegeBeijingChina

The 5th generation mobile communications aims at connecting everything and future Internet of Things(IoT)will get everything smartly *** realize it,there exist many *** key challenge is the battery problem for small devices,such as sensors or *** backscatter,also referred to as or battery-free backscatter,is a new potential technology to address this *** early and typical type of batteryless backscatter is ambient ***,batteryless backscatter utilizes environmental wireless signals to enable battery-free devices to communicate with each *** devices first harvest energy from ambient wireless signals and then backscatter these signals so as to transmit their own *** paper reviews the current studies about batteryless backscatter,including various backscatter schemes and theoretical works,and then introduces open problems for future research.

关键词： batteryless backscatter battery-free channel state information(CSI) channel estimation multiple antennas signal detection symbiotic communication

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Integrating SQuaRE data quality model with ISO 31000 risk management to measure and mitigate software bias 3

Integrating SQuaRE data quality model with ISO 31000 risk ma...

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3rd International Workshop on Experience with SQuaRE Series and Its Future Direction, IWESQ 2021

作者： Simonetta, Alessandro Vetrò, Antonio Paoletti, Maria Cristina Torchiano, Marco Department of Enterprise Engineering University of Rome Tor Vergata Rome Italy Dept. of Control and Computer Eng Politecnico di Torino Turin Italy Rome Italy

In the last decades the exponential growth of available information, together with the availability of systems able to learn the knowledge that is present in the data, has pushed towards the complete automation of many decision-making processes in public and private organizations. This circumstance is posing impelling ethical and legal issues since a large number of studies and journalistic investigations showed that software-based decisions, when based on historical data, perpetuate the same prejudices and bias existing in society, resulting in a systematic and inescapable negative impact for individuals from minorities and disadvantaged groups. The problem is so relevant that the terms data bias and algorithm ethics have become familiar not only to researchers, but also to industry leaders and policy makers. In this context, we believe that the ISO SQuaRE standard, if appropriately integrated with risk management concepts and procedures from ISO 31000, can play an important role in democratizing the innovation of software-generated decisions, by making the development of this type of software systems more socially sustainable and in line with the shared values of our societies. More in details, we identified two additional measure for a quality characteristic already present in the standard (completeness) and another that extends it (balance) with the aim of highlighting information gaps or presence of bias in the training data. Those measures serve as risk level indicators to be checked with common fairness measures that indicate the level of polarization of the software classifications/predictions. The adoption of additional features with respect to the standard broadens its scope of application, while maintaining consistency and conformity. The proposed methodology aims to find correlations between quality deficiencies and algorithm decisions, thus allowing to verify and mitigate their impact. © 2021 for this paper by its authors

关键词： Risk management

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Autonomous Vehicle Drift With a Soft Actor-critic Reinforcement Learning Agent 20

Autonomous Vehicle Drift With a Soft Actor-critic Reinforcem...

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20th IEEE Jubilee World Symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Hunor Toth, Szilard Janos Viharos, Zsolt Bardos, Adam Institute of Computer Science and Control Research Laboratory on Engineering Management Intelligence Intelligent Processes Research Group Budapest Hungary John von Neumann University Faculty of Economics Kecskemét Hungary Budapest University of Technology and Economics Department of Automotive Technologies Budapest Hungary

ISBN: (纸本)9781665497046

Self-driving vehicles have become a more and more important field in recent years. Supported by the techniques of Artificial Intelligence (AI), the current tendency of positive results in applications is making it a promising area to focus further research. Additionally, Reinforcement Learning (RL) is already proved to be an efficient approach for complex problems, e.g. robots, industrial systems and also games (like chess, Go), *** is a driving technique at handling limits where the driver intentionally oversteers, with loss of traction, while driving the vehicle through the entirety of a corner. It is a very challenging control task and often results in an accident when it occurs on public roads, consequently, the efficient control of this motion is especially important in the safety of autonomous *** paper reports novel research results whose main goal is to develop a self-driving agent for drift motion control based on vehicle simulation by Matlab/Simulink. Longitudinal and lateral velocity together with the yaw rate formed the state representation of the vehicle. The agent action space consists of two continuous actuator values: pedal ratio and roadwheel angle. The goal of the agent is twofold: first, it has to jump into a drifting state, second, it has to keep the vehicle in *** simulation results show that the proposed Soft Actor-Critic (SAC) RL agent is capable of learning to approach a pre-determined drift equilibrium from cornering and staying in this drift situation as well. For the training, the solution excluded using any kind of prior data, it only works with information gained from the simulation model, which is a remarkable difference from the actual state-of-the-art RL-based solutions. © 2022 IEEE.

关键词： Training Roads Simulation Reinforcement learning Mathematical models Data models Artificial intelligence

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A Feature Fusion Attention-based Deep Learning Algorithm for Mammographic Architectural Distortion Classification

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Rehman, Khalil Ur Jianqiang, Li Yasin, Anaa Bilal, Anas Basheer, Shakila Ullah, Inam Jabbar, Muhammad Kashif Tian, Yibin Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Beijing University of Technology College of Software Engineering Beijing100124 China Hainan Normal University College of Information Science and Technology Hainan Haikou571158 China Princess Nourah Bint Abdulrahman University Department of Information Systems College of Computer and Information Science Riyadh11671 Saudi Arabia Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan

Architectural Distortion (AD) is a common abnormality in digital mammograms, alongside masses and microcalcifications. Detecting AD in dense breast tissue is particularly challenging due to its heterogeneous asymmetries and subtle presentation. Factors such as location, size, shape, texture, and variability in patterns contribute to reduced sensitivity. To address these challenges, we propose a novel feature fusion-based Vision Transformer (ViT) attention network, combined with VGG-16, to improve accuracy and efficiency in AD detection. Our approach mitigates issues related to texture fixation, background boundaries, and deep neural network limitations, enhancing the robustness of AD classification in mammograms. Experimental results demonstrate that the proposed model achieves state-of-the-art performance, outperforming eight existing deep learning models. On the PINUM dataset, it attains 0.97 sensitivity, 0.92 F1-score, 0.93 precision, 0.94 specificity, and 0.96 accuracy. On the DDSM dataset, it records 0.93 sensitivity, 0.91 F1-score, 0.94 precision, 0.92 specificity, and 0.95 accuracy. These results highlight the potential of our method for computer-aided breast cancer diagnosis, particularly in low-resource settings where access to high-end imaging technology is limited. By enabling more accurate and timely AD detection, our approach could significantly improve breast cancer screening and early intervention worldwide. © 2013 IEEE.

关键词： Deep neural networks

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Explainable Artificial Intelligence Solution for Online Retail

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computers, Materials & Continua 2022年第6期71卷 4425-4442页

作者： Kumail Javaid Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Allah Ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan Department of Computer Science University of South AsiaLahore54000Pakistan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Division of Science and Technology University of EducationLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for *** the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the *** this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one *** main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the *** permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the *** performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC *** model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree ***,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying *** research also reveals the importance of page value as a key feature for capturing online purchasing *** may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

关键词： Explainable artificial intelligence online retail neural network random forest regression

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