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PDDD-PreTrain:A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

Plant Phenomics 2023年第2期5卷 313-332页

作者： Xinyu Dong Qi Wang Qianding Huang Qinglong Ge Kejun Zhao Xingcai Wu Xue Wu Liang Lei Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China The School of Physics and Optoelectronic Engineering Guangdong University of TechnologyGuangzhou 510006China National Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural BioengineeringMinistry of EducationGuizhou UniversityGuiyang 550025China

Plant diseases threaten global food security by reducing crop yield;thus,diagnosing plant diseases is critical to agricultural *** intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming,costly,inefficient,and subjective *** a mainstream AI method,deep learning has substantially improved plant disease detection and diagnosis for precision *** the meantime,most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased ***,the commonly used pre-trained models are from the computer vision dataset,not the botany dataset,which barely provides the pre-trained models sufficient domain knowledge about plant ***,this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic *** address this issue,we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease *** addition,we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification,plant disease detection,plant disease segmentation,and other *** extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time,thereby supporting the better diagnosis of plant *** addition,our pre-trained models will be open-sourced at https://***/and Zenodo platform https://***/10.5281/zenodo.7856293.

关键词： diagnosis Plant thereby

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AMHF-TP:Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期13卷 127-141页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’anChina Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’anChina Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anChina School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’anChina

Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as extensive training durations,limited sample sizes,and inadequate generalization *** address these issues,we present AMHF-TP,an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance *** AMHF-TP is composed of four key components:a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences;a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures;a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences;and a hierarchical feature extraction module that integrates multimodal peptide sequence *** with leading methods,the proposed AMHF-TP demonstrates superior precision,accuracy,and coverage,underscoring its effectiveness and robustness in MFTP *** comparative analysis of separate hierarchical models and the combined model,as well as with five contemporary models,reveals AMHFTP’s exceptional performance and stability in recognition tasks.

关键词： deep learning hypergraph multifunctional therapeutic peptides multi-granularity hierarchical features

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Variational Neural Inference Enhanced Text Semantic Communication System

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China Communications 2024年第7期21卷 50-64页

作者： Zhang Xi Zhang Yiqian Li Congduan Ma Xiao School of Electronics and Communication Engineering Sun Yat-Sen UniversityShenzhen 518107China Guizhou University State Key Laboratory of Public Big Data Guiyang 550025China Shenzhen Key Laboratory of Navigation and Communication Integration Shenzhen 518107China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510006China

Recently,deep learning-based semantic communication has garnered widespread attention,with numerous systems designed for transmitting diverse data sources,including text,image,and speech,*** efforts have been directed toward improving system performance,many studies have concentrated on enhancing the structure of the encoder and ***,this often overlooks the resulting increase in model complexity,imposing additional storage and computational burdens on smart ***,existing work tends to prioritize explicit semantics,neglecting the potential of implicit *** paper aims to easily and effectively enhance the receiver's decoding capability without modifying the encoder and decoder *** propose a novel semantic communication system with variational neural inference for text ***,we introduce a simple but effective variational neural inferer at the receiver to infer the latent semantic information within the received *** information is then utilized to assist in the decoding *** simulation results show a significant enhancement in system performance and improved robustness.

关键词： deep learning semantic communication variational neural inference

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 144-158页

作者： Zhang, Dengyong He, Ruiyi Liao, Xin Li, Feng Chen, Jiaxin Yang, Gaobo Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation the School of Computer and Communication Engineering Changsha410114 China Hunan University Changsha410082 China

Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are invalidated for fake face images with compression. In this work, we propose a two-stream network for deepfake detection. We observed that high-frequency noise features and spatial features are inherently complementary to each other. Thus, both spatial features and high-frequency noise features are exploited for face forgery detection. Specifically, we design a double-frequency transformer module (DFTM) to guide the learning of spatial features from local artifact regions. To effectively fuse spatial features and high-frequency noise features, a dual-domain attention fusion module (DDAFM) is designed. We also introduce a local relationship constraint loss, which requires only image-level labels, for model training. We evaluate the proposed approach on five large-scale benchmark datasets, and extensive experimental results demonstrate the proposed approach outperforms most SOTA works. © 2024 IEEE.

关键词： Frequency domain analysis

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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A new mobility field and gradient-based traffic signal control approach applicable to large-scale road networks

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Transportation Safety and Environment 2025年第1期7卷 62-73页

作者： Hu Yang Bao Guo Changxin Yan Zhiqiang Chen Pu Wang School of Traffic and Transportation Engineering Rail Data Research and Application Key Laboratory of Hunan ProvinceCentral South UniversityChangsha 410000China Changsha Planning&Design Survey Research Institute Changsha 410000China Hunan Engineering Research Center of Urban Transport Data-driven Modeling&Simulation Changsha 410000China

Previous traffic control models were usually developed on small or medium sized road *** traffic control models applicable to large-scale road networks have received growing *** this study,we develop a new mobility field and gradient-based traffic signal control approach applicable to large road ***,we introduce an emerging analytical technique,the mobility field approach,to generate the mobility field of urban travels and measure the gradients of the mobility ***,a gradient-based approach is proposed to identify the signalized intersections for implementing traffic ***,a gradient-based traffic control model is developed to alleviate traffic congestion during mass events.A new solution algorithm,termed DBSCAN-FW-GA,is proposed to solve the developed traffic control *** developed mobility field and gradient-based traffic signal control approach is validated using actual road network data and travel demand *** indicate that the proposed new traffic control approach can reduce by 17.97%the travel time compared with the widely used perimeter control approach.

关键词： mobility field gradient human mobility traffic control congestion

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A Linked List Encryption Scheme for Image Steganography without Embedding

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Computer Modeling in engineering & Sciences 2024年第10期141卷 331-352页

作者： Pengbiao Zhao Qi Zhong Jingxue Chen Xiaopei Wang Zhen Qin Erqiang Zhou Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China Faculty of Data Science City University of MacaoMacao999078China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information *** enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover *** this paper,a novel encrypted communication scheme for image SWE is *** reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer *** employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be *** receiver can restore the secretmessage fromthe received image using only the list header position *** scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover *** boasts high concealment and security,along with a complete message restoration rate,making it resistant to ***,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of *** validate the proposed framework,practical tests and comparisons are conducted using multiple *** results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

关键词： Steganography encryption steganography without embedding coverless steganography

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Robust and Trustworthy data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

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Computers, Materials & Continua 2024年第2期78卷 2159-2179页

作者： Jinyang Yu Xiao Zhang Jinjiang Wang Yuchen Zhang Yulong Shi Linxuan Su Leijie Zeng School of Computer Science Northwestern Polytechnical UniversityXi’an710072China MIIT Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityXi’an710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical UniversityXi’an710072China

The proliferation of Internet of Things(IoT)systems has resulted in the generation of substantial data,presenting new challenges in reliable storage and trustworthy *** distributed storage systems are hindered by centralized management and lack traceability,while blockchain systems are limited by low capacity and high *** address these challenges,the present study investigates the reliable storage and trustworthy sharing of IoT data,and presents a novel system architecture that integrates on-chain and off-chain data manage *** architecture,integrating blockchain and distributed storage technologies,provides high-capacity,high-performance,traceable,and verifiable data storage and *** on-chain system,built on Hyperledger Fabric,manages metadata,verification data,and permission information of the raw *** off-chain system,implemented using IPFS Cluster,ensures the reliable storage and efficient access to massive files.A collaborative storage server is designed to integrate on-chain and off-chain operation interfaces,facilitating comprehensive data *** provide a unified access interface for user-friendly system *** testing validates the system’s reliability and stable *** proposed approach significantly enhances storage capacity compared to standalone blockchain *** reliability tests consistently yield positive *** average upload and download throughputs of roughly 20 and 30 MB/s,respectively,the system’s throughput surpasses the blockchain system by a factor of 4 to 18.

关键词： On-chain and off-chain collaboration blockchain distributed storage system hyperledger fabric IPFS cluster

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Benchmark experiment on slab^(238)U with D-T neutrons for validation of evaluated nuclear data

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Nuclear Science and Techniques 2024年第2期35卷 145-159页

作者： Yan-Yan Ding Yang-Bo Nie Yue Zhang Zhi-Jie Hu Qi Zhao Huan-Yu Zhang Kuo-Zhi Xu Shi-Yu Zhang Xin-Yi Pan Chang-Lin Lan Jie Ren Xi-Chao Ruan Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China Institute of Fluid Physics China Academy of Engineering PhysicsMianyang 62100China

A benchmark experiment on^(238)U slab samples was conducted using a deuterium-tritium neutron source at the China Institute of Atomic *** leakage neutron spectra within energy levels of 0.8-16 MeV at 60°and 120°were measured using the time-of-flight *** samples were prepared as rectangular slabs with a 30 cm square base and thicknesses of 3,6,and 9 *** leakage neutron spectra were also calculated using the MCNP-4C program based on the latest evaluated files of^(238)U evaluated neutron data from CENDL-3.2,ENDF/B-Ⅷ.0,JENDL-5.0,and *** on the comparison,the deficiencies and improvements in^(238)U evaluated nuclear data were *** results showed the following.(1)The calculated results for CENDL-3.2 significantly overestimated the measurements in the energy interval of elastic scattering at 60°and 120°.(2)The calculated results of CENDL-3.2 overestimated the measurements in the energy interval of inelastic scattering at 120°.(3)The calculated results for CENDL-3.2 significantly overestimated the measurements in the 3-8.5 MeV energy interval at 60°and 120°.(4)The calculated results with JENDL-5.0 were generally consistent with the measurement results.

关键词： Leakage neutron spectra Uranium D-T neutron source Evaluated nuclear data

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