会议论文集名称：
Communications in Computer and Information Science
关键词：
Image manipulation detection;frequency information
摘要：
The goal of Image Manipulation Detection (IMD) is to identify and locate manipulated regions within images. Recent approaches have primarily designed sophisticated neural networks to capture highf-requency information for IMD tasks. However, these methods often overemphasize high-frequency information while overlooking the important role of low-frequency information in IMD tasks. To address this issue, we propose a Triple-Branch Frequency-Aware Network (TFNet), which includes an Information Separation Module (ISM), a Main Steam Branch (MSB), a Low-Frequency Learning Branch (LFL), a High-Frequency Learning Branch (HFL), and an Adaptive Aggregate Module (AAM) within a unified framework. Specifically, TFNet initially employs FSM to separate the manipulated image into RGB, high-frequency, and low-frequency components. Then, MSB, LFB, and HFB take the above components as input to learn the distinct features. Furthermore, the HFB is supervised with the boundary information to encourage the network to focus on the high-frequency information. Finally, the outputs of MSB, LFB, and HFB are sent to the ABM to adaptively aggregate features learned from the MSB, LFB, and HFB. Experiments on the CASIA, NIST, and Coverage datasets demonstrate the effectiveness of our TFNet.
期刊：
International Journal of Autonomous and Adaptive Communications Systems,2025年18(1):45-66 ISSN：1754-8632
作者机构：
[Zhilin Chen; Jiaohua Qin] College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China
关键词：
RDH;reversible data hiding;prediction error;Huffman coding;encrypted images;extended run-length coding
摘要：
To reduce prediction errors and create more room for embedding data, the paper proposes a reversible data hiding (RDH) in encrypted images scheme based on histogram shifting and prediction error block coding. Firstly, the histogram of the prediction error image is shifted according to the signs of prediction errors. Next, the prediction error plane is partitioned into uniformly sized blocks, and these blocks are labelled as three types: an all-zero block, a block containing only one 1, and a block containing more than one 1. These three types of blocks are compressed using labelling, binary tree coding, and Huffman coding, respectively. To better compress the label map, an improved extended run-length coding is proposed. Finally, the image is secured by encryption and the secret data is hidden within it. The experimental results indicate a significant improvement in the embedding rate of the scheme compared to other schemes.
通讯机构：
[Li, T ] H;Hunan Univ Sci & Technol, Sch Comp Sci & Engn, Xiangtan 411201, Hunan, Peoples R China.
关键词：
Short texts typically refer to texts of relatively short length;generally not exceeding 160 characters. Short text classification (Chakraborty & Singh;2022) is a form of text mining technology and a significant research direction in natural language processing (NLP) (Hirschberg & Manning;2015). With the rapid development of the internet;a large volume of information in the form of short texts such as news tags;reviews;instant messages;and tweets has emerged on online platforms. These short texts contain a wealth of helpful information that contributes to establishing services in various fields;such as news classification;social media (Kateb & Kalita;sentiment analysis (Balomenos et al.;e-commerce;and spam filtering. As a result;with the growing depth of related research;short text classification has emerged as a critical challenge that urgently needs to be addressed in information retrieval. However;short text classification faces several challenges that complicate the task. First;short texts tend to be brief;with sparse semantic features and weak contextual connections;often requiring additional feature information to assist in classification (Wu;2023). For example;Hua et al. (2024) integrated text;entities;and words into a heterogeneous graph convolutional network;representing features through a word graph and enhancing word features with BiLSTM;thereby enabling document category prediction. However;due to length limitations;GNNs typically do not incorporate additional information when processing short texts;treating each short text as a single node in the graph. This leads to insufficient feature information;which in turn affects performance. Therefore;enhancing feature information and semantic relevance becomes crucial. Second;in rapidly changing text data environments (such as social media and news);expert knowledge required to organize and label training documents properly is often limited;expensive;and sometimes even unavailable. In some cases;training text classifiers using well-organized and accurately labeled documents is prohibitively costly;making unsupervised learning a crucial solution. Unsupervised tasks allow for fast classification without labeled data;avoiding the time-consuming and error-prone process of manual annotation. For example;Cameron-Steinke (2019) employed a novel text expansion approach in an unsupervised manner;whereby new words are generated directly for each input sentence. The newly generated keywords are formed within the hidden states of a pre-trained language model;which are then used to create expanded pseudo-headlines. However;it has limitations in terms of the accuracy and redundancy of the expanded content. In addition;Mishra (2024) compared and analyzed unsupervised text classification techniques;especially Latent Dirichlet Allocation (LDA) and BERTopic;and applied these topic modeling techniques to very short documents. However;since their evaluation datasets are based on specific scenarios;the generalization of these methods is weak. Therefore;how to effectively classify large amounts of unlabeled data using unsupervised methods is a research-worthy problem. To address the aforementioned issues;we propose a novel approach called UEDE: The Unsupervised Short Text Classification Method based on GCN Encoder-Decoder and Local Enhancement. This method uses unsupervised learning to cleverly integrate the GCN encoder–decoder and local enhancement technology under the autoencoder framework to achieve accurate short-text classification. Specifically;we first design the MRFasGCN modeling approach within the autoencoder framework;learning the hierarchical representations of node attributes and the network’s deep structure. We model and infer the relationships between nodes by integrating these deep representations with network structural information;thereby capturing text features and semantic information more effectively. Then;we introduce a dual decoder suited for unsupervised learning;which alleviates the issue of short text feature sparsity from multiple perspectives by reconstructing the topology and node attributes. Furthermore;we further enhance the latent semantic module locally by constructing an information fusion graph;ensuring that each pair of connected nodes shares a similar module distribution and complementary features. This improves the model’s ability to understand the local context of the text while addressing the overfitting issues caused by short text feature sparsity;significantly boosting the model’s performance and classification accuracy. The main contributions of this article can be summarized as follows: • We propose an unsupervised learning-based short text classification method;utilizing multiple convolutional layers as encoders to learn short text attribute networks’ deep representations and structural information. This approach facilitates module partitioning and inference;more effectively capturing text features and semantic information. • We design a dual decoder structure that;through unsupervised learning;separately reconstructs the topology and node attributes while accounting for the heterogeneity of node degrees. This approach compensates for insufficient short text feature information from multiple perspectives. • We introduce a local enhancement method that enables nodes to share more common neighbors and similar node attributes;strengthening the connections between related nodes. This improves the model’s understanding of the text’s local context while alleviating the overfitting issues caused by short text feature sparsity. • We perform comprehensive experiments on real-world datasets encompassing news articles;concise comments;and search snippets to assess the efficacy of our model in comparison to fourteen baseline approaches. The experimental findings unequivocally establish that our model surpasses the current state-of-the-art baseline methods on the benchmark datasets. We propose an unsupervised learning-based short text classification method;utilizing multiple convolutional layers as encoders to learn short text attribute networks’ deep representations and structural information. This approach facilitates module partitioning and inference;more effectively capturing text features and semantic information. We design a dual decoder structure that;through unsupervised learning;separately reconstructs the topology and node attributes while accounting for the heterogeneity of node degrees. This approach compensates for insufficient short text feature information from multiple perspectives. We introduce a local enhancement method that enables nodes to share more common neighbors and similar node attributes;strengthening the connections between related nodes. This improves the model’s understanding of the text’s local context while alleviating the overfitting issues caused by short text feature sparsity. We perform comprehensive experiments on real-world datasets encompassing news articles;concise comments;and search snippets to assess the efficacy of our model in comparison to fourteen baseline approaches. The experimental findings unequivocally establish that our model surpasses the current state-of-the-art baseline methods on the benchmark datasets. The remainder of the paper is structured as follows: Section 2 reviews previous work. Section 3 describes our proposed method and model;including the unsupervised short text classification model and local enhancement method. In Section 4;we perform comprehensive experiments on the datasets and analyze the outcomes. Lastly;Section 5 concludes the paper;offering insights into the future research directions.
摘要：
Like all fields of data science, short text classification seeks to achieve high-quality results with limited data. Although supervised learning methods have made notable progress in this area, they require much-labeled data to achieve adequate accuracy. However, in many practical applications, labeled data is scarce, and manual labeling is not only time-consuming and labor-intensive but also expensive and may require specialized expertise. Therefore, this paper addresses the challenge of insufficient labeled data through unsupervised methods while ensuring the effective extraction of semantic features from the text. Building on this objective, we propose a novel unsupervised short text classification method within the framework of autoencoders. Specifically, we first design the MRFasGCN encoder and derive the relationships between nodes in its hidden layers, thereby enhancing the capture of text features and semantic information. Furthermore, we construct a dual-node-based decoder that reconstructs the topology and node attributes unsupervised. This approach compensates for feature deficiencies from multiple perspectives, alleviating the issue of insufficient features in short texts. Finally, we propose a localized enhancement method that integrates node features and topology, strengthening the connections between relevant nodes. This improves the model’s understanding of the text’s local context while mitigating the overfitting issues caused by feature sparsity in short texts. Extensive experimental results demonstrate the pronounced superiority of our proposed UEDE model over existing methods on the dataset, validating its effectiveness in short-text classification. Our code is submitted in https://github.com/w123yy/UEDE .
Like all fields of data science, short text classification seeks to achieve high-quality results with limited data. Although supervised learning methods have made notable progress in this area, they require much-labeled data to achieve adequate accuracy. However, in many practical applications, labeled data is scarce, and manual labeling is not only time-consuming and labor-intensive but also expensive and may require specialized expertise. Therefore, this paper addresses the challenge of insufficient labeled data through unsupervised methods while ensuring the effective extraction of semantic features from the text. Building on this objective, we propose a novel unsupervised short text classification method within the framework of autoencoders. Specifically, we first design the MRFasGCN encoder and derive the relationships between nodes in its hidden layers, thereby enhancing the capture of text features and semantic information. Furthermore, we construct a dual-node-based decoder that reconstructs the topology and node attributes unsupervised. This approach compensates for feature deficiencies from multiple perspectives, alleviating the issue of insufficient features in short texts. Finally, we propose a localized enhancement method that integrates node features and topology, strengthening the connections between relevant nodes. This improves the model’s understanding of the text’s local context while mitigating the overfitting issues caused by feature sparsity in short texts. Extensive experimental results demonstrate the pronounced superiority of our proposed UEDE model over existing methods on the dataset, validating its effectiveness in short-text classification. Our code is submitted in https://github.com/w123yy/UEDE .
期刊：
JOURNAL OF SUPERCOMPUTING,2025年81(1):1-21 ISSN：0920-8542
通讯作者：
Xuyu Xiang
作者机构：
[Peng, Jianting; Xiang, Xuyu; Qin, Jiaohua; Tan, Yun] College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, China
通讯机构：
[Xuyu Xiang] C;College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, China
摘要：
Cross-modal retrieval can break through the limitations of modalities and carry out information retrieval across data of different modalities to meet the needs of users in obtaining multi-modal correlation retrieval. Cloud computing has the advantages of high efficiency and low cost, but data security hinders its development. While cloud computing offers high efficiency and cost-effectiveness, concerns surrounding data security impede its full potential. Privacy-preserving cross-modal retrieval emerges as a viable solution, catering to users’ demands for efficient retrieval while safeguarding data confidentiality. However, a major challenge still exists in this field: how to bridge the inherent semantic gap within heterogeneous and chaotic information. To address this challenge, this paper proposes dual-branch networks for privacy-preserving cross-modal retrieval in cloud computing. Firstly, a dual-branch feature extraction network of encrypted image-text is constructed, enhancing the extraction of meaningful features from encrypted data. Secondly, a cross-modal alignment method is designed to eliminate the heterogeneous gap between different modalities through the alignment within and between modalities. Finally, to fully exploit the storage and computing advantages of cloud computing, both encrypted data and the cross-modal feature extractor are deployed to the cloud. Leveraging the dynamic update capabilities of cloud-stored encrypted data enables continuous model refinement, enhancing retrieval accuracy while reducing the storage and computational burdens on data owners. Extensive experiments conducted on the publicly available benchmark image-text dataset Wikipedia indicate that, compared to existing methods, our approach achieves improvements of 5.4%, 1%, 1.6%, and 20.1% in the four metrics of image-to-text (i2t), text-to-image (t2i), image-to-all (i2all), and text-to-all (t2all), respectively.
摘要：
With the continuous development of deep learning (DL), the task of multimodal dialog emotion recognition (MDER) has recently received extensive research attention, which is also an essential branch of DL. The MDER aims to identify the emotional information contained in different modalities, e.g., text, video, and audio, and in different dialog scenes. However, the existing research has focused on modeling contextual semantic information and dialog relations between speakers while ignoring the impact of event relations on emotion. To tackle the above issues, we propose a novel dialog and event relation-aware graph convolutional neural network (DER-GCN) for multimodal emotion recognition method. It models dialog relations between speakers and captures latent event relations information. Specifically, we construct a weighted multirelationship graph to simultaneously capture the dependencies between speakers and event relations in a dialog. Moreover, we also introduce a self-supervised masked graph autoencoder (SMGAE) to improve the fusion representation ability of features and structures. Next, we design a new multiple information Transformer (MIT) to capture the correlation between different relations, which can provide a better fuse of the multivariate information between relations. Finally, we propose a loss optimization strategy based on contrastive learning to enhance the representation learning ability of minority class features. We conduct extensive experiments on the benchmark datasets, Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Multimodal EmotionLines Dataset (MELD), which verify the effectiveness of the DER-GCN model. The results demonstrate that our model significantly improves both the average accuracy and the $F1$ value of emotion recognition. Our code is publicly available at https://github.com/yuntaoshou/DER-GCN.
通讯机构：
[Liu, EX ] C;Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410000, Peoples R China.
关键词：
Sensors;Photonic band gap;Couplings;Lattices;Refractive index;Biosensors;Band structures;Refractive index (RI) sensor;topological corner state (TCS);topological photonic crystals (TPC);ultrahigh quality factor
摘要：
Owing to the flexible light control and the immunity to defects and impurities, topological photonic crystals (TPC) are considered an excellent platform in the engineering of optoelectronic devices. Herein, four TPC refractive index (RI) sensors are theoretically proposed based on the su-Schrieffer-Heeger (SSH) model. Zero-dimensional topological corner states (TCS) and 1-D topological edge states (TES) are demonstrated by the band structure and the Zak phases. Four TPC sensors are based on single topological microcavity (TCS formed by two TES and four TES), trivial corner states (TRCS)-TES coupling, and TCS-TES coupling. The TCS-based TPC sensors exhibit an ultrahigh quality factor (Q) of $\gt 10^{{6}}$ . Moreover, TPC sensor based on TCS-TES coupling remain a topological robustness with >99.4% sensitivity quality of reproduction by artificially introducing eight-type defects. Notably, the design based on TCS-TES coupling presents a sensitivity of 413.76 nm/RIU and a Q value of $2.8\times 10^{{6}}$ in a broad RI range of 1.00–1.50. This work not only provides a new way of 2-D photonic crystals (PC) sensor applications but also offers a guidance for the design of TPC sensors.
摘要：
The popularity of smart devices has made it more convenient for users to take screen shots, but it has also made it easier to take clandestine shots, resulting in compromised and untraceable information. Therefore, this paper introduces a screen-shooting robust watermarking algorithm based on the pattern complexity just noticeable difference (PC-JND) model. This approach involves the utilisation of local binary patterns (LBP) for block filtering based on texture complexity in the original image. Stable feature blocks are selected and processed using the speeded-up robust features (SURF) algorithm to extract key feature points, defining them as the watermark embedding regions. Finally, the watermark is embedded in the integer wavelet domain's HH sub-band, guided by the PC-JND model. Experimental results demonstrate that this algorithm not only significantly improves the visual quality of images in a shorter embedding time but also exhibits enhanced robustness against screen captures from various angles.
关键词：
RDH;reversible data hiding;prediction error;Huffman coding;encrypted images;extended run-length coding
摘要：
To reduce prediction errors and create more room for embedding data, the paper proposes a reversible data hiding (RDH) in encrypted images scheme based on histogram shifting and prediction error block coding. Firstly, the histogram of the prediction error image is shifted according to the signs of prediction errors. Next, the prediction error plane is partitioned into uniformly sized blocks, and these blocks are labelled as three types: an all-zero block, a block containing only one 1, and a block containing more than one 1. These three types of blocks are compressed using labelling, binary tree coding, and Huffman coding, respectively. To better compress the label map, an improved extended run-length coding is proposed. Finally, the image is secured by encryption and the secret data is hidden within it. The experimental results indicate a significant improvement in the embedding rate of the scheme compared to other schemes.
通讯机构：
[Zhang, LT ] C;Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410000, Peoples R China.
关键词：
Temperature prediction;Hybrid model;CNN-BiLSTM;RandomForest
摘要：
Temperature fluctuations have profound impacts on both human society and the natural environment. However, the diversity of geographical temperature data and the non-linearity and complexity of meteorological phenomena present significant challenges to accurate prediction. Previous studies in temperature prediction have faced certain limitations, such as inadequate consideration of correlations between multiple meteorological factors in some models or limited modeling capability for non-linear and spatiotemporal relationships. To address these shortcomings, we propose a hybrid model based on CNN-BiLSTM and RandomForest for temperature prediction. Relevant meteorological indicators were selected as model inputs using a combined Pearson's correlation and mutual information analysis. Subsequently, utilizing a sequence-to-sequence framework, the CNN-BiLSTM model effectively explores spatiotemporal relationships and feature representations in time-series data as the encoder for sequence modeling. This aids in learning feature representations and sequence relationships in time series data, thus generating abstract representations for each sample in the hidden or output layers, covering key information learned by the model. Feature representations extracted from the CNN-BiLSTM model are aggregated into a feature matrix. Each sample corresponds to a row of the feature vector, with each column representing a specific feature extracted from the CNN-BiLSTM model. Finally, RandomForest, as an ensemble learning method, is capable of handling complex non-linear relationships during both model training and prediction stages, exhibiting good robustness and predictive capability. Utilizing the feature matrix as input data, it is fed into the RandomForest decoder model along with the corresponding target variables, further enhancing the prediction accuracy and stability of the model to obtain the final temperature prediction results. Simulated experiments were conducted using meteorological data from Changsha, Hunan Province, from 2017 to 2021. A significant number of experiments have demonstrated that, compared to the current leading Dliner method, the hybrid model exhibits higher prediction accuracy and stability in capturing daily temperature trends. The mean absolute error and mean squared error were reduced by 35.6 and 57.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, respectively.
摘要：
Electrolytic double-layer gate insulation transistors (EDLGITs) have gained significant attention recently due to their features for achieving high-density charge accumulation and low operating voltage. This is primarily attributed to the large capacitance of the electrolyte insulation in these devices. This article proposes a physical model for the capacitance of the electrolytic double-layer gate insulation (EDLGI) and the drain current of amorphous InGaZnO (a-IGZO) EDLGITs based on the channel surface potential. It is demonstrated that the EDLGI capacitance, which comprises the compact-layer and diffuse-layer capacitances, is not only influenced by the characteristics of the electrolyte but also by the gate voltage. Furthermore, the frequency-dependent equivalent capacitance of the electrolyte insulation ( ${C} _{\text {eq}}$ ) under ac gate voltage in a-IGZO EDLGITs is analyzed. The drain current and ${C} _{\text {eq}}$ calculated using our model show good consistency with the reported experimental data.
摘要：
Tomatoes are an important global crop, and automating the segmentation of leaf diseases is essential for agricultural security. Effective segmentation of these diseases is vital for timely intervention, which can significantly enhance crop yield and reduce pesticide usage. However, challenges such as background interference, tiny diseases, and blurred disease edges pose immense obstacles to the segmentation of tomato leaf diseases. To address these issues effectively, we propose a fusion adversarial segmentation network for tomato disease segmentation named FATDNet. Firstly, to eliminate background interference effectively, we introduce a dual-path fusion adversarial algorithm (DFAA). This algorithm employs parallel dual-path convolution to extract features of leaf disease regions, merging and adversarially processing complex background noise and disease features. Secondly, to enhance the feature extraction of small lesion regions, we employ a multi-dimensional attention mechanism (MDAM). This mechanism allocates weights in both horizontal and vertical directions, subsequently calculating weights in different channels of the feature map. This enhances the dispersion of semantic information through the adoption of diverse weight calculation strategies. Furthermore, to improve the model’s ability to extract features at the edges of leaf diseases, we introduce a Gaussian weighted edge segmentation module (GWESM). This module calculates weight distribution through a Gaussian-weighted function, guiding the model to highlight features at different scales and reduce information loss caused by pooling operations. To demonstrate the superiority of FATDNet, we conduct comparative experiments using a self-built dataset and a public dataset. Experimental results show that FATDNet outperforms nine state-of-the-art segmentation networks. This validates that FATDNet provides a reliable solution for the automated segmentation of tomato leaf diseases.
Tomatoes are an important global crop, and automating the segmentation of leaf diseases is essential for agricultural security. Effective segmentation of these diseases is vital for timely intervention, which can significantly enhance crop yield and reduce pesticide usage. However, challenges such as background interference, tiny diseases, and blurred disease edges pose immense obstacles to the segmentation of tomato leaf diseases. To address these issues effectively, we propose a fusion adversarial segmentation network for tomato disease segmentation named FATDNet. Firstly, to eliminate background interference effectively, we introduce a dual-path fusion adversarial algorithm (DFAA). This algorithm employs parallel dual-path convolution to extract features of leaf disease regions, merging and adversarially processing complex background noise and disease features. Secondly, to enhance the feature extraction of small lesion regions, we employ a multi-dimensional attention mechanism (MDAM). This mechanism allocates weights in both horizontal and vertical directions, subsequently calculating weights in different channels of the feature map. This enhances the dispersion of semantic information through the adoption of diverse weight calculation strategies. Furthermore, to improve the model’s ability to extract features at the edges of leaf diseases, we introduce a Gaussian weighted edge segmentation module (GWESM). This module calculates weight distribution through a Gaussian-weighted function, guiding the model to highlight features at different scales and reduce information loss caused by pooling operations. To demonstrate the superiority of FATDNet, we conduct comparative experiments using a self-built dataset and a public dataset. Experimental results show that FATDNet outperforms nine state-of-the-art segmentation networks. This validates that FATDNet provides a reliable solution for the automated segmentation of tomato leaf diseases.
作者：
Fan Yang;Tan Zhu*;Jing Huang;Zhilin Huang;Guoqi Xie
期刊：
COMPUTER SPEECH AND LANGUAGE,2025年:101818 ISSN：0885-2308
通讯作者：
Tan Zhu
作者机构：
[Fan Yang; Tan Zhu; Zhilin Huang] College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, 41004, PR China;[Jing Huang] School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan, 411201, PR China;[Guoqi Xie] Key Laboratory for Embedded and Network Computing of Hunan Province, College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, PR China
通讯机构：
[Tan Zhu] C;College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, 41004, PR China
摘要：
Text classification is an important topic in natural language processing. In recent years, both graph kernel methods and deep learning methods have been widely employed in text classification tasks. However, previous graph kernel algorithms focused too much on the graph structure itself, such as the shortest path subgraph,while focusing limited attention to the information of the text itself.Previous deep learning methods have often resulted in substantial utilization of computational resources. Therefore,we propose a new graph kernel algorithm to address the disadvantages. First,we extract the textual information of the document using the term weighting scheme. Second,we collect the structural information on the document graph. Third, graph kernel is used for similarity measurement for text classification. We compared eight baseline methods on three experimental datasets, including traditional deep learning methods and graph-based classification methods, and tested our algorithm on multiple indicators. The experimental results demonstrate that our algorithm outperforms other baseline methods in terms of accuracy. Furthermore, it achieves a minimum reduction of 69% in memory consumption and a minimum decrease of 23% in runtime. Furthermore, as we decrease the percentage of training data, our algorithm continues to achieve superior results compared to other deep learning methods. The excellent experimental results show that our algorithm can improve the efficiency of text classification tasks and reduce the occupation of computer resources under the premise of ensuring high accuracy.
Text classification is an important topic in natural language processing. In recent years, both graph kernel methods and deep learning methods have been widely employed in text classification tasks. However, previous graph kernel algorithms focused too much on the graph structure itself, such as the shortest path subgraph,while focusing limited attention to the information of the text itself.Previous deep learning methods have often resulted in substantial utilization of computational resources. Therefore,we propose a new graph kernel algorithm to address the disadvantages. First,we extract the textual information of the document using the term weighting scheme. Second,we collect the structural information on the document graph. Third, graph kernel is used for similarity measurement for text classification.
We compared eight baseline methods on three experimental datasets, including traditional deep learning methods and graph-based classification methods, and tested our algorithm on multiple indicators. The experimental results demonstrate that our algorithm outperforms other baseline methods in terms of accuracy. Furthermore, it achieves a minimum reduction of 69% in memory consumption and a minimum decrease of 23% in runtime. Furthermore, as we decrease the percentage of training data, our algorithm continues to achieve superior results compared to other deep learning methods. The excellent experimental results show that our algorithm can improve the efficiency of text classification tasks and reduce the occupation of computer resources under the premise of ensuring high accuracy.
期刊：
Biomedical Signal Processing and Control,2025年107:107815 ISSN：1746-8094
通讯作者：
Genhua Liu<&wdkj&>Guoxiong Zhou
作者机构：
["Yang, Yixin; Sun, Lixiang; Tang, Zhiwen; Liu, Genhua; Zhou, Guoxiong; Li, Lin] School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China;[Cai, Weiwei] School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China;[Li, Liujun] Department of Civil, Architectural and Environmental Engineering, Missouri University of Science & Technology, Rolla 65401, USA;[Chen, Lin] Zhuzhou Sany Aier Eye Hospital, Zhuzhou 412002, China;[Hu, Linan"] Department of Radiology, Zhuzhou Central Hospital, Zhuzhou 412001, China
通讯机构：
[Genhua Liu; Guoxiong Zhou] S;School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
摘要：
In 2013, an estimated 64 million people between the ages of 40 and 80 were suffering from eye disease. By 2020, that number had climbed to 76 million. It is predicted that by 2040, there will be a staggering 111.8 million glaucoma patients worldwide. Segmentation of blood vessels in retinal images can be used to investigate many diseases, and the complexity of the blood vessels and the variable conditions inside the retina pose a high challenge for accurate segmentation. Therefore, a competing fusion segmentation network (TAOD −CFNet) with a trumpet-like attention mechanism and optic disc gradient adjustment algorithm for retinal blood vessel segmentation. First, an optic disc gradient adjustment algorithm (ODGA) is proposed, which designs dual threshold weights for accurate localization and optimization of optic disc areas. Then, a competing fusion block (CFB) is proposed to improve the feature dissimilarity between the arteriovenous vascular sensitive area and the interference area. Finally, a Trumpet Attention Mechanism (TAM) is proposed to enhance the edge features of fine and peripheral blood vessels. TAOD-CFNet outperforms ten SOTA methods in ten-fold cross-validation, with IOU, F1-Score, Dice, Jaccard, ACC and MCC metrics reaching 83.28%, 89.41%, 84.28%, 80.35%, 96.94% and 88.81%. To demonstrate the generalization performance of the model, TAOD-CFNet outperforms ten SOTA image segmentation methods on six retinal image datasets (DRIVE, CHASEDB1, STARE, HRF, IOSTAR, and LES). The experimental results proved that TAOD-CFNet possesses better segmentation performance and can assist clinicians in determining the condition of retinopathy patients.
In 2013, an estimated 64 million people between the ages of 40 and 80 were suffering from eye disease. By 2020, that number had climbed to 76 million. It is predicted that by 2040, there will be a staggering 111.8 million glaucoma patients worldwide. Segmentation of blood vessels in retinal images can be used to investigate many diseases, and the complexity of the blood vessels and the variable conditions inside the retina pose a high challenge for accurate segmentation. Therefore, a competing fusion segmentation network (TAOD −CFNet) with a trumpet-like attention mechanism and optic disc gradient adjustment algorithm for retinal blood vessel segmentation. First, an optic disc gradient adjustment algorithm (ODGA) is proposed, which designs dual threshold weights for accurate localization and optimization of optic disc areas. Then, a competing fusion block (CFB) is proposed to improve the feature dissimilarity between the arteriovenous vascular sensitive area and the interference area. Finally, a Trumpet Attention Mechanism (TAM) is proposed to enhance the edge features of fine and peripheral blood vessels. TAOD-CFNet outperforms ten SOTA methods in ten-fold cross-validation, with IOU, F1-Score, Dice, Jaccard, ACC and MCC metrics reaching 83.28%, 89.41%, 84.28%, 80.35%, 96.94% and 88.81%. To demonstrate the generalization performance of the model, TAOD-CFNet outperforms ten SOTA image segmentation methods on six retinal image datasets (DRIVE, CHASEDB1, STARE, HRF, IOSTAR, and LES). The experimental results proved that TAOD-CFNet possesses better segmentation performance and can assist clinicians in determining the condition of retinopathy patients.
作者：
Li Peng;Wang Wang;Cheng Yang;Wenhui Xiao;Xiangzheng Fu;...
作者机构：
[Li Peng; Wang Wang; Cheng Yang; Wenhui Xiao] College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China;[Yifan Chen] College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China;[Xiangzheng Fu] College of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR China
会议名称：
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
03 December 2024
会议地点：
Lisbon, Portugal
会议论文集名称：
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
关键词：
miRNA-drug sensitivity;graph neural network;zero-shot embeddings;large language models
摘要：
MicroRNAs (miRNAs) are a class of non-coding RNA molecules that have been shown to be closely associated with the sensitivity of chemotherapeutic drugs in cancer treatment. Given the high cost and extended duration of traditional biological experiments, there is an urgent need to develop computational models to predict the sensitivity scores between miRNAs and drugs. In this study, we proposed a dual-stream graph neural network method based on Zero-Shot Embeddings, named DSHGZS, to explore the potential sensitivity scores between miRNAs and drugs. DSHGZS first constructs two heterogeneous graphs with different isomorphic subgraphs based on zero-shot embeddings obtained from large language models (LLMs) and known miRNA-drug association data. It then utilized the enhanced LLM-derived node feature representations, embedding them into the layer feature learning process of the two heterogeneous graphs to generate high-quality vector representations of miRNAs and drugs. The learned high-quality feature embeddings are subsequently used in a segmented inner product decoder to evaluate the sensitivity association scores between miRNAs and drugs. To address the model’s excessive reliance on high-quality feature representations, we employed PCA to extract the core representations of the LLM-derived node features for data augmentation. Case studies demonstrated that DSHGZS is an effective tool for predicting potential sensitivity scores between miRNAs and drugs.
通讯机构：
[Zhong, SH ] C;Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Peoples R China.
关键词：
Age of Information;resource scheduling;Industrial Internet of Things;deep reinforcement learning
摘要：
Effective resource scheduling methods in certain scenarios of Industrial Internet of Things are pivotal. In time-sensitive scenarios, Age of Information is a critical indicator for measuring the freshness of data. This paper considers a densely deployed time-sensitive Industrial Internet of Things scenario. The industrial wireless device transmits data packets to the base station with limited channel resources under the constraints of Age of Information. It is assumed that each device has the capacity to store the packets it generates. The device will discard the data to alleviate the data queue backlog when the Age of Information of the data packet exceeds the threshold. We developed a new system utility equation to represent the scheduling problem and the problem is expressed as a trade-off between minimizing the average Age of Information and maximizing network throughput. Inspired by the success of reinforcement learning in decision-processing problems, we attempt to obtain an optimal scheduling strategy via deep reinforcement learning. In addition, a reward function is constructed to enable the agent to achieve improved convergence results. Compared with the baseline, our proposed algorithm can achieve better system utility and lower Age of Information violation rate.
期刊：
Computer-Aided Civil and Infrastructure Engineering,2024年39(4):617-634 ISSN：1093-9687
通讯作者：
Zhou, GX
作者机构：
[Yang, Yixin; Sun, Lixiang; Zhou, Guoxiong; Chen, Aibin; Zhang, Yukai] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Peoples R China.;[Cai, Weiwei] Jiangnan Univ, Coll Artificial Intelligence & Comp Sci, Wuxi, Peoples R China.;[Li, Liujun] Univ Idaho, Dept Soil & Water Syst, Moscow, ID USA.
通讯机构：
[Zhou, GX ] C;Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Peoples R China.
摘要：
<jats:title>Abstract</jats:title><jats:p>The segmentation accuracy of bridge crack images is influenced by high‐frequency light, complex scenes, and tiny cracks. Therefore, an integration–competition network (complex crack segmentation network [CCSNet]) is proposed to address these problems. First, a grayscale‐oriented adjustment algorithm is proposed to solve the high‐frequency light problem. Second, an integration–competition mechanism is proposed to detach complex backgrounds and grayscale features of cracks. Finally, a tiny attention mechanism is proposed to extract the shallow features of tiny cracks. CCSNet outperforms seven state‐of‐the‐art crack segmentation methods in both generalization and comparison experiments on self‐built dataset and four public datasets. It also achieved excellent performance in practical bridge crack tests. Therefore, CCSNet is an effective auxiliary method for lowering the cost of bridge safety detection.</jats:p>
摘要：
<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Background</jats:title>
<jats:p><jats:italic>Camellia oleifera</jats:italic>, an essential woody oil tree in China, propagates through grafting. However, in production, it has been found that the interaction between rootstocks and scions may affect fruit characteristics. Therefore, it is necessary to predict fruit characteristics after grafting to identify suitable rootstock types.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>This study used Deep Neural Network (DNN) methods to analyze the impact of 106 6-year-old grafting combinations on the characteristics of <jats:italic>C.oleifera</jats:italic>, including fruit and seed characteristics, and fatty acids. The prediction of characteristics changes after grafting was explored to provide technical support for the cultivation and screening of specialized rootstocks. After determining the unsaturated fat acids, palmitoleic acid C16:1, cis-11 eicosenoic acid C20:1, oleic acid C18:1, linoleic acid C18:2, linolenic acid C18:3, kernel oil content, fruit height, fruit diameter, fresh fruit weight, pericarp thickness, fresh seed weight, and the number of fresh seeds, the DNN method was used to calculate and analyze the model. The model was screened using the comprehensive evaluation index of Mean Absolute Error (MAPE), determinate correlation <jats:italic>R</jats:italic><jats:sup>2</jats:sup> and and time consumption.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>When using 36 neurons in 3 hidden layers, the deep neural network model had a MAPE of less than or equal to 16.39% on the verification set and less than or equal to 13.40% on the test set. Compared with traditional machine learning methods such as support vector machines and random forests, the DNN method demonstrated more accurate predictions for fruit phenotypic characteristics, with MAPE improvement rates of 7.27 and 3.28 for the 12 characteristics on the test set and maximum <jats:italic>R</jats:italic><jats:sup>2</jats:sup> improvement values of 0.19 and 0.33. In conclusion, the DNN method developed in this study can effectively predict the oil content and fruit phenotypic characteristics of <jats:italic>C. oleifera</jats:italic>, providing a valuable tool for predicting the impact of grafting combinations on the fruit of <jats:italic>C. oleifera</jats:italic>.</jats:p>
</jats:sec>
摘要：
Camellia oleifera, a woody oil tree, is widely recognized for its valuable oil. Different cultivars of C.oleifera exhibit distinct growth characteristics, oil content, and oil composition. Therefore, the classification of C.oleifera cultivars can aid in the better utilization of C.oleifera resources and improve yield and quality. However, the identification of cultivars remains challenging due to genetic diversity, similarities in leaf morphology, and the influence of geographical environment, among other factors. Comprehensive cultivar identification methods for studying C.oleifera must be established to overcome these obstacles. We selected 118 varieties that grew under natural light conditions and collected whole pest-free mature leaves. After filtering out invalid images, we constructed a leaf cultivar dataset consisting of 30890 images of C. oleifera. The results showed that RegNetY-4.0GF-Convolutional Block Attention Module provides significant advantages over other methods in cultivar recognition, including VGG16, ResNet50, EffificientNet-B4, and EffificientNet-B4-CBAM. It achieved an overall accuracy of 93.7 % and an F1-score of 0.945, much higher than the accuracy of other compared methods. CBAM can significantly improve the accuracy of varieties recognized. The overall results showed that deep learning could effectively distinguish C.oleiera leaves of different varieties. This method provided an effective way to identify C.oleifera varieties quickly and nondestructively.
期刊：
IEEE Transactions on Cybernetics,2024年54(5):3286-3298 ISSN：2168-2267
作者机构：
[Lv, Mingjie; Yang, Chunhua; Fan, Maosen; Liu, Shengyu; Huang, Keke; Liu, Gengchen; Sun, Bei] Cent South Univ, Sch Automat, Changsha 410083, Hunan, Peoples R China.;[Sun, Bei] Peng Cheng Lab, Ind Intelligence Basic Res Studio, Shenzhen 518000, Guangdong, Peoples R China.;[He, Mingfang] Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Peoples R China.
关键词：
Estimation;Logic gates;Observers;Inductors;Testing;Computational modeling;Production;Dynamics learning;long short-term memory;multirate estimation;quality-related indices (QRIs)
摘要：
In this study, we propose a dynamics-learning multirate estimation approach to perceive the quality-related indices (QRIs) of the feeding solution of a unit process. A quality-related index for estimation is an intermediate technical indicator between a unit process and a proceeding unit process; hence, the estimation problem is formulated as a two-stage estimation problem utilizing the production data of both unit processes. Dynamics-learning bidirectional long short-term memory (BiLSTM) with different inputs for the forward and backward layers is proposed to manage the input data from the different unit processes. In the dynamics-learning BiLSTM, a cycle control gate is added in the memory cell to learn the dynamics of the QRIs, thereby enabling a high-rate estimation under multirate conditions. A Bayesian estimation model is then combined with the dynamics-learning BiLSTM model to manage the process delay. Ablation and comparative experiments are conducted to evaluate the feasibility and effectiveness of the proposed estimation approach. The experimental results illustrate the performance and high-rate estimation ability of the proposed approach.
