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A text dependent copy-paste plagiarism and text-rewriting plagiarism model

Journal of intelligent and Fuzzy Systems 2025年第6期48卷 849-858页

作者： Guo, Qingkai Huang, Guimin Wang, Yabing Zhou, Ya School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin China

Plagiarism is common in English writing exams. Researchers classify plagiarism into copy-paste and text-rewriting plagiarism, but existing models need help with problems such as the single way of checking and unsatisfactory results. Aiming at the copy-paste problem in English writing plagiarism, this paper proposes a digital fingerprint model based on the N-Gram window jumping mechanism. The model incorporates a sliding window and an improved matching tool to solve the problems of excessive fingerprint density and low checking efficiency in text extraction. Meanwhile, the model adds a Fisher-Yates shuffle algorithm with a salt parameter to crack the hash collision in text matching. The experiments show that the model can detect copy-paste plagiarism in English composition. For text rewriting plagiarism, this paper designs a TextCNN-BiGRU-based model, which combines TextCNN and BiGRU so that the extracted text semantic information considers the text’s local and contextual features. The experiments show that the model improves the accuracy by 1.9 percentage points and the F1 value by 1.2 percentage points on the MRPC dataset compared with other models. © 2025 – IOS Press. All rights reserved.

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Tomato Leaf Disease Identification and Growth Dataset Augmentation Based on UAE-GAN

Tomato Leaf Disease Identification and Growth Dataset Augmen...

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International Conference on intelligent control and information processing (ICICIP)

作者： Xin Li Fang Li Xiaonan Luo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

To delve into the characterization of growth disorders in different crops, it is important to support the model with a large amount of image data that includes a variety of disease types and disease levels to capture the typical and subtle differences of various diseases on plant leaves. However, the actual process of gathering data is challenging, sample coverage is challenging to accomplish, data capture is impeded, and the quality of the data is subpar. This work aims to address the issue of data shortages by employing technical methods. In particular, we creatively investigated the UAE-GAN approach, which naturally combines CycleGAN, U-Net, Variational Autoencoder VAE, and Autoencoder to increase the data. Among these, U-Net can precisely extract the small details of disease locations in crop photos and provide a strong basis for further processing thanks to its special codec architectural benefits. The Variational Autoencoder (VAE) significantly enhances the diversity of data by mapping the image to the latent space and sampling based on a certain probability distribution, so producing new image samples that are distinct from the original image yet inherently connected. Learning the coding and decoding of the original image is the foundation of autoencoders. If a mild disruption is introduced into the coding process, it can achieve data augmentation in another dimension and create a sequence of new images with just little modifications to the original image. The aforementioned models are closely linked with CycleGAN to efficiently map and convert in a variety of picture domains and to fully leverage CycleGAN's remarkable unsupervised image conversion capabilities. The perception ability, feature capture ability, and information conversion ability of the fusion model for crop image data are significantly improved, and the key elements of each link in the data enhancement process are comprehensively considered to ensure that the generated new image data can not o

关键词： Training image coding Autoencoders Data enhancement Crops Generators Data models Stability analysis Probability distribution Diseases

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Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation

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Results in Physics 2025年 73卷

作者： Hou, Tiantian Jiao, Shangbin Wang, Yi Song, Nianlong Li, Jianghua Cui, Wenchuan Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China

The theory of Stochastic Resonance (SR) has drawn significant attention due to its exceptional ability to detect faint signals. Despite this, research to date indicates that for SR systems, whether they are monostable, bistable, or multi-stable, modifications to the system parameters lead to concurrent alterations in the depth and breadth of the potential wells when analyzing engineering signals, which results in suboptimal detection outcomes. To address these issues, a two-dimensional Gaussian bistable coupled SR (GBCSR) system has been proposed that can individually adjust the potential well characteristics. This innovative system facilitates the separate adjustment of shape characteristics of potential, allowing for more precise manipulation of the system's dynamic response. The system's non-linear dynamic traits are explicated through an analysis of the steady-state probability density (SPD) function and the mean first passage time (MFPT), substantiating the effectiveness of the new model. In practical scenarios, a variety of bearing defect signals serve as the objects of detection. The structural parameters of the GBCSR system are co-optimized using the Brain Storm Optimization (BSO) algorithm. This optimization approach leverages the algorithm's ability to enhance population diversity and improve convergence accuracy, thereby optimizing the system's performance. The experimental outcome results show that the proposed system can accurately detect the frequency of bearing fault signals. When compared with traditional SR systems such as the traditional bistable stochastic SR (TBSR), the traditional Gaussian SR system (TGSR), and the cascade stochastic resonance system, the proposed coupled system demonstrates superior performance. This is achieved through the transfer of energy or information between subsystems, which enables more efficient utilization of noise energy. The system can trigger the resonance effect over a broader range of noise intensities and signi

关键词： Bearing fault detection Coupled stochastic resonance Independent parameter regulation MFPT SPD

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C2P-Net: Comprehensive Depth Map to Planar Depth Conversion for Room Layout Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Zhang, Weidong Zhou, Mengjie Cheng, Jiyu Liu, Ying Zhang, Wei Xi'an University of Posts & Telecommunications School of Communications and Information Engineering China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets. © 1979-2012 IEEE.

关键词： image-Text Matching Medical Report Generation Sample-graph Consistency Self-boosting framework

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MASS FLOW DETECTION TECHNOLOGY FOR SEED AND FERTILISER PARTICLES AND ITS APPLICATION IN UAV-BASED SPREADING

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INMATEH - Agricultural Engineering 2025年第1期75卷 584-599页

作者： Wang, Yanshuang Song, Cancan Gao, Yongqi Lan, Yubin Wang, Guobin Ming, Rui College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology Zibo China Research of Institute of Ecological Unmanned Farm Shandong University of Technology Zibo China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

The processes of sowing and fertiliser application represent a significant aspect of agricultural production. In order to achieve efficient and precise seeding and fertiliser application, mass flow detection of seed and fertiliser particles can facilitate real-time monitoring and precise decision-making for intelligent seeding and fertiliser application. However, the diversity of seed and fertiliser particle types and particle flow modes presents a challenge for existing detection methods, particularly in meeting the varying operational requirements, especially for particle mass flow detection in low-altitude and high-speed sowing operations of agricultural drones. In such cases, the overlap and flow rate will have a significant impact on the detection results due to the large displacement and the continuous high-throughput dense phase of the particle flow. This paper provides a summary of the existing seed and fertiliser particle mass flow detection techniques and their underlying working principles. It compares the direct detection based on mass with the indirect detection methods based on velocity and concentration, and analyses their respective advantages, disadvantages and applicability. It also explores the possibility of optimising the existing detection methods for the specific needs of agricultural UAVs and considers the potential introduction of cutting-edge science and technology in order to develop an efficient, accurate and convenient detection system to meet the growing market demand. © (2025), (INMA Bucharest). All Rights Reserved.

关键词： Precision agriculture

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A Gaze-Guided Human Locomotion Intention Recognition and Volitional control Method for Knee-Ankle Prostheses

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Chen, Xinxing Chen, Zhaokai Wang, Yuxuan Yin, Shucong Chen, Chuheng Huang, Jian Fu, Chenglong Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen518055 China Shanghai Jiaotong University Shanghai China

This paper presents a novel gaze-guided volitional control method for knee-ankle prostheses, designed to enhance the precision and intuitiveness of prosthetic control in complex locomotion tasks. The method utilizes an eye-tracking system coupled with an RGBD camera to estimate 3D gaze points, which are then used to construct a gaze attention map. This map serves as the basis for predicting human locomotion intentions, enabling the prosthesis to accurately step on the intended foot placement. Experimental validation was conducted with both a healthy subject and an amputee subject, focusing on level-ground walking and stair ascent tasks. The results demonstrated that the prosthesis could achieve precise foot placement, with a mean error of 1.22 cm ± 3.14 cm for the healthy subject and 3.40 cm ± 0.66 cm for the amputee subject during level-ground walking. In stair ascent tasks, the system successfully adapted to various gait patterns, with the prosthetic joint angles closely matching natural human movements. The proposed method also effectively reduced the root mean square error (RMSE) of 3D gaze estimation to 2.70 cm, 2.63 cm, and 1.97 cm along the x, y, and z axes, respectively. These findings indicate that the gaze-guided volitional control method not only enhances the functionality of knee-ankle prostheses but also offers a more intuitive interface for users, aligning prosthetic actions with the user’s visual focus and intentions. © 2016 IEEE.

关键词： Knee prostheses

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Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units

Patterns

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Patterns 2025年

作者： Li, Yi-Fan Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

We describe NLSExplorer, an interpretable approach for nuclear localization signal (NLS) prediction. By utilizing the extracted information on nuclear-specific sites from the protein language model to assist in NLS detection, NLSExplorer achieves superior performance with greater than 10% improvement in the F1 score compared with existing methods on benchmark datasets and highlights other nuclear transport segments. We applied NLSExplorer to the nucleus-localized proteins in the Swiss-Prot database to extract valuable segments. A comprehensive analysis of these segments revealed a potential NLS landscape and uncovered features of nuclear transport segments across 416 species. This study introduces a powerful tool for exploring the NLS universe and provides a versatile network that can efficiently detect characteristic domains and motifs. © 2025 The Author(s)

关键词： Deep learning

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Multimodal Brain-Computer Interfaces: AI-powered Decoding Methodologies

arXiv

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arXiv 2025年

作者： Li, Siyang Wang, Hongbin Chen, Xiaoqing Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs. © 2025, CC BY-NC-ND.

关键词： Brain computer interface

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Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Tian, Zhuojun Zhang, Zhaoyang Li, Yiwei Bennis, Mehdi Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Oulu Center for Wireless Communications Oulu90014 Finland State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multimodal Communication Networks and Intelligent Information Processing Hangzhou310027 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China

To jointly tackle the challenges of data and node heterogeneity in decentralized learning, we propose a distributed strong lottery ticket hypothesis (DSLTH), based on which a communication-efficient personalized learning algorithm is developed. In the proposed method, each local model is represented as the ‌Hadamard product of global real-valued parameters and a personalized binary mask for pruning. The local model is learned by updating and fusing the personalized binary masks while the real-valued parameters are fixed among different agents. To further reduce the complexity of hardware implementation, we incorporate a group sparse regularization term in the loss function, enabling the learned local model to achieve structured sparsity. Then, a binary mask aggregation algorithm is designed by introducing an intermediate aggregation tensor and adding a personalized fine-tuning step in each iteration, which constrains model updates towards the local data distribution. The proposed method effectively leverages the relativity among agents while meeting personalized requirements in heterogeneous node conditions. We also provide a theoretical proof for the DSLTH, establishing it as the foundation of the proposed method. Numerical simulations confirm the validity of the DSLTH and demonstrate the effectiveness of the proposed algorithm. © 2015 IEEE.

关键词： Iterative methods

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Distributed Fixed-Time Algorithms for Time-Varying Constrained Optimization Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Xing Li, Yue Zhang, Meng Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed algorithms possess better privacy and scalability. This paper initially proposes a centralized time-varying optimisation algorithm with fixed-time convergence and certain robustness, which is based on the integration-enhanced zeroing neural network. Subsequently, the algorithm is enhanced, and two distributed algorithms are designed separately. Both of these two algorithms have a fixed convergence time and certain robustness. Additionally, this paper utilizes the penalty function approach to handle time-varying optimization problems with inequality constraints, thereby making the algorithm more widely applicable. The effectiveness of the algorithm is verified through several numerical examples, and the applicability of the algorithm is demonstrated by solving the package-level state-of-charge balancing problem. © 2020 IEEE.

关键词： Optimization algorithms

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