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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Huang, Juan Tang, Jinghua Tang, Dan Cai, Hongliang Yu, Hongzhou School of Software Engineering Chengdu University of Information Technology Sichuan Province Engineering Technology Research Center of Support Software of lnformatization Application Sichuan Provincial Big Data Technical Service Center Chengdu China

ISBN: (纸本)9798331541729

To address the problem of low repair efficiency of single- and double-disk of localized horizontal array code, this paper retains the original advantages of horizontal array code, transforms it on the basis of RDP 2D array code, and proposes a 2D array code model based on nested structure - NRDP (Nested Rows Diagonal Parity Code). The overall layout of the NRDP code is the same as that of the RDP code. However, when deploying data columns, the NRDP code deploys the RDP code of one prime number level smaller as a whole in the data columns of the NRDP code, forming a nested relationship. The increased redundant data reduce the repair overhead and error recovery time. Through theoretical analysis, NRDP has good compiled code complexity, improves the problem of uneven reading and writing during disk repair, and reduces the total amount of data that needs to be read during single- and double-disk failures, improving the success rate of single- and double-disk repair. The experimental results show that compared with RDP, LRRDP, and DRDP, NRDP can save 7.03%-30.01% of the single-disk failure repair time and 14.79%-26.51% of the dual-disk repair time. © 2024 IEEE.

关键词： Codes (symbols)

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Evaluation Method of Motor Coordination Ability in Children Based on Machine Vision

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Tsinghua Science and technology 2025年第2期30卷 633-649页

作者： Yi Lei Dawei Shu Miao Yu Donglin Shi Jianqiang Li Yanjie Chen Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Department of Automation Tsinghua UniversityBeijing 100084China Pharmacovigilance Research Center for information technology and Data Science Cross-strait Tsinghua Research InstituteXiamen 361000China Institute of Systems Engineering Dalian University of TechnologyDalian 116024China Physical Education Department Hebei Sports UniversityShijiazhuang 050049China Department of Children’s Health Care Center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijing 100045China

Motor coordination is crucial for preschoolers’development and is a key factor in assessing childhood *** diagnostic methods often rely on subjective manual *** paper presents a machine vision-based approach aimed at improving the objectivity and adaptability of *** method proposed involves the extraction of key points from the human skeleton through the utilization of a lightweight pose estimation network,thereby transforming video assessments into evaluations of keypoint *** study uses different methods to handle static and dynamic actions,including regularization and Dynamic Time Warping(DTW)for spatial alignment and temporal discrepancies.A penalty-adjusted single-frame pose similarity method is used to evaluate *** lightweight pose estimation model reduces parameters by 85%,uses only 6.6%of the original computational load,and has an average detection missing rate of less than 1%.The average error for static actions is 0.071 with a correlation coefficient of 0.766,and for dynamic actions it is 0.145 with a correlation coefficient of *** results confirm the proposed method’s effectiveness,which includes customized visual components like motion waveform graphs to improve accuracy in pediatric healthcare diagnoses.

关键词： developmental coordination disorder action quality assessment pose estimation dynamic time warping decision support system

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Key-Value Store Coupled with an Operating System for Storing Large-Scale Values

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Computers, Materials & Continua 2022年第11期73卷 3333-3350页

作者： Jeonghwan Im Hyuk-Yoon Kwon Graduate School of Data Science Seoul National University of Science and TechnologySeoulKorea Department of Industrial Engineering Graduate School of Data ScienceResearch Center for Electrical and Information ScienceSeoul National University of Science and TechnologySeoulKorea

The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value *** types of studies have been conducted to improve the performance of the key-value store while maintaining its ***,the research efforts storing the large-scale values such as multimedia data files(e.g.,images or videos)in the key-value store were *** this study,we propose a new key-value store,WR-Store++aiming to store the large-scale values ***,it provides a new design of separating data and index by working with the built-in data structure of the Windows operating system and the file *** utilization of the built-in data structure of the Windows operating system achieves the efficiency of the key-value store and that of the file system extends the limited space of the storage *** also present chunk-based memory management and parallel processing of WR-Store++to further improve its performance in the GET *** the experiments,we show that WR-Store++can store at least 32.74 times larger datasets than the existing baseline key-value store,WR-Store,which has the limitation in storing large-scale data ***,in terms of processing efficiency,we show that WR-Store++outperforms not only WR-Store but also the other state-ofthe-art key-value stores,LevelDB,RocksDB,and BerkeleyDB,for individual key-value operations and mixed workloads.

关键词： Key-value stores large-scale values chunk-based memory management parallel processing

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ViGT: proposal-free video grounding with a learnable token in the transformer

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Science China(information Sciences) 2023年第10期66卷 196-212页

作者： Kun LI Dan GUO Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artificial Intelligence Hefei Comprehensive National Science Center

The video grounding(VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer(ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows.(1) The token is unrelated to the video or the query and avoids data bias toward the original video and query.(2) The token simultaneously performs global context aggregation from video and query ***, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention(i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets:ANet-Captions, TACoS, and YouCookⅡ. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

关键词： video grounding temporal sentence grounding boundary regression token learning proposal-free

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Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

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Digital Communications and Networks 2025年第2期11卷 482-492页

作者： Puning Zhang Lei Tan Zhigang Yang Fengyi Huang Lijun Sun Haiying Peng School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing Innovation Center of Industrial Big-Data Co.Lt

The lack of facial features caused by wearing masks degrades the performance of facial recognition systems. Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer and the device layer. Besides, previous research fails to consider the facial characteristics including occluded and unoccluded parts. To solve the above problems, we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature fusion. Specifically, the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face recognition. Then, a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain facial. Furthermore, a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load, computational power, bandwidth, and delay tolerance constraints of the edge. This method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition accuracy. The experimental results show that the proposed method achieves an average gain of about 21% in recognition latency, while the accuracy of the face recognition task is basically the same compared to the baseline method.

关键词： Occluded face recognition Cross-domain feature fusion Device-edge collaboration

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Research on symmetric encryption and decryption algorithm of shared data based on chaotic mapping and permutation 6

Research on symmetric encryption and decryption algorithm of...

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2023 IEEE 6th International Conference on information Systems and Computer Aided Education, ICISCAE 2023

作者： Zhang, Jing Wang, Xinyan Liu, Boyu Wang, Junyi Li, Xiaoqin He, Liang State Grid Henan Information and Telecommunication Company Data Center Henan Zhengzhou China Nari Information and Communication Technology Company Jiangsu Nanjing China

ISBN: (纸本)9798350313444

This paper mainly focuses on the symmetric encryption and decryption of shared data. In the traditional symmetric encryption technology, sharing an identical key is safe for both communication parties, which is not flexible enough for large-scale data exchange though. The symmetric encryption and decryption of shared data means that multiple users can use the same key to encrypt or decrypt data, so as to achieve data security protection and improve transmission efficiency. At present, the research of symmetric encryption and decryption of shared data has made some progress, but there are still some challenging problems. For example, problems like how to avoid key leakage and how to ensure the efficiency of decryption process need to be further addressed. In this study, chaotic mapping and substitution methods are used to solve these problems. First, we transform the original data into a new random sequence by way of chaotic mapping, and then convert it back to the original. This method can effectively prevent key leakage and improve the decryption speed without affecting the data transmission. Secondly, we use the idea of permutation to design a new decryption algorithm, which can complete the decryption task through simple computational operations without slowing down the decryption speed. © 2023 IEEE.

关键词： Apply Cryptography information security Research Scheme demonstration

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MSU-Net:automatic segmentation of lung infection boundaries and small lesions in CT images 4

MSU-Net:automatic segmentation of lung infection boundaries ...

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4th International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024

作者： Zhan, Cheng He, Xuelei Han, Chenxu Wang, Hanchao Yu, Jingjing School of Physics and Information Technology Shaanxi Normal University Shaanxi Province Xi'an710119 China Network and Data Center Northwest University Xi’an710127 China School of Information Technology Northwest University Xi’an710127 China

ISBN: (纸本)9781510682443

Lung Computed Tomography (CT) images play an important role in the diagnosis and treatment of patients with COVID-19. However, manually identifying lesions in CT images is very time-consuming and requires specialized medical knowledge. The accuracy and integrity of the existing models are still not ideal due to the reasons such as scattered distribution, small lesions and blurred boundaries. Therefore, we propose a segmentation model based on U-Net architecture by combining Mirror-symmetry Boundary Guided (MBG) module and then adding Spatial Attention Dilation (SAD) convolution Module, called MSU-Net. The SAD module uses spatial attention mechanism and dilated convolution and multi-scale feature extraction strategy to enhance the network’s ability to recognize small lesions. The MBG module further enhances the model’s ability to capture more complex context information and boundary details, making the model more robust and able to effectively deal with the challenges of fuzzy boundaries. The proposed method has shown superior performance in terms of the Dice coefficient, Hausdorff distance, and Sensitivity on the publicly available COVID-19 CT dataset. © 2024 SPIE.

关键词： COVID-19

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Research on symmetric encryption and decryption of multi-user shared data for cloud storage environment 6

Research on symmetric encryption and decryption of multi-use...

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2023 IEEE 6th International Conference on information Systems and Computer Aided Education, ICISCAE 2023

作者： Wang, Xinyan Zhang, Jing Meng, Zhaotai Yi, Bin Hu, An Gu, Ming State Grid Henan Information and Telecommunication Company Data Center Henan Zhengzhou China Nari Information and Communication Technology Company Jiangsu Nanjing China

ISBN: (纸本)9798350313444

This paper mainly studies the symmetric encryption and decryption technology for multi-user shared data in cloud storage environment. With the popularity of cloud storage technology, more and more users store multi-user shared data on third-party cloud platforms. In order to protect the privacy and integrity of multi-user shared data, encryption technology has become an important means to deal with cloud security challenges [1]. The traditional multi-user shared data encryption technology has some problems in the scenario of multi-user shared data. For example, it is difficult to restrict the access to multi-user shared data after decryption. Therefore, this paper proposes a symmetric encryption technology for multi-user shared data based on access control, which uses attribute encryption to achieve controllable access to multi-user shared data, and uses group cryptography and obfuscation technology to ensure the confidentiality and integrity of multi-user shared data. The experimental results show that this technology not only ensures the security of multi-user data sharing, but also has high efficiency and user friendliness. As a result, it can meet the encryption requirements in large-scale multi-user data sharing scenarios. The research of this technology has certain reference significance for the security improvement of cloud storage field. © 2023 IEEE.

关键词： Cloud security cloud storage Multi-user data sharing Multi-user data sharing security

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An insider user authentication method based on improved temporal convolutional network

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High-Confidence Computing 2023年第4期3卷 87-95页

作者： Xiaoling Tao Yuelin Yu Lianyou Fu Jianxiang Liu Yunhao Zhang Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China

With the rapid development of information technology,information system security and insider threat detection have become important topics for organizational *** the current network environment,user behavioral bio-data presents the characteristics of nonlinearity and temporal *** of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature *** do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data,and also do not adequately reflect the personalized usage characteristics of users,leading to bottlenecks in the performance of the authentication *** order to solve the above problems,this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism(ECA-TCN)to extract user mouse dynamics features and constructs an one-class Support Vector Machine(OCSVM)for each user for *** results show that compared with four existing deep learning algorithms,the method retains more adequate key information and improves the classification performance of the neural *** the final authentication,the Area Under the Curve(AUC)can reach 96%.

关键词： Insider users Mouse dynamics Feature extraction Temporal convolutional network Efficient channel attention mechanism Authentication

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