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arXiv 2025年

作者： Shi, Junfei Nie, Mengmeng Li, Yuke Jin, Haiyan Lin, Weisi Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China College of Computing and Data Science Nanyang Technological University 639798 Singapore

Deep learning can learn high-level semantic features in Euclidean space effectively for PolSAR images, while they need to covert the complex covariance matrix into a feature vector or complex-valued vector as the network input. However, the complex covariance matrices are essentially a complex Hermit positive definite (HPD) matrix endowed in Riemannian manifold rather than Euclidean space. The matrix’s real and imagery parts are with the same significance, as the imagery part represents the phase information. The matrix vectorization will destroy the geometric structure and manifold characteristics of complex covariance matrices. To learn complex HPD matrices directly, we propose a Riemannian complex HPD convolution network(HPD CNN) for PolSAR images. This method consists of a complex HPD unfolding network(HPDnet) and a CV-3DCNN enhanced network. The proposed complex HPDnet defines the HPD mapping, rectifying and the logEig layers to learn geometric features of complex matrices. In addition, a fast eigenvalue decomposition method is designed to reduce computation burden. Finally, a Riemannian-to-Euclidean enhanced network is defined to enhance contextual information for classification. Experimental results on two real PolSSAR datasets demonstrate the proposed method can achieve superior performance than the state-of-the-art methods especially in heterogeneous regions. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection

arXiv

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arXiv 2024年

作者： Guo, Hao Ma, Zihan Zeng, Zhi Luo, Minnan Zeng, Weixin Tang, Jiuyang Zhao, Xiang Laboratory for Big Data and Decision Nation University of Defence Technology China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research. © 2024, CC BY-NC-ND.

关键词： Fake detection

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Causal Logic Discovery and Rule Validation Using Evolutionary Integration Model Global Fit U-Net

Causal Logic Discovery and Rule Validation Using Evolutionar...

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Natural Language Processing (ICNLP), International Conference on

作者： Xuelian Yang Fangyuan Du Kunjun Chen Hao Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In order to explore the correlation between different MRI sequences and the results of U-Net segmentation of glioma subregions, this paper proposes an interpretable method based on an evolutionary integration algorithm for logical discovery of the segmentation process of U-Net. Our approach consists of three steps: 1) Global fitting of the U-Net model to the segmentation results of gliomas using a dual evolutionary algorithm to generate a fitted model with both accuracy and interpretability.2) Extracting decision rules from the fitted model according to a specific target interpretable region and generating a complete set of interpretable rules after optimisation.3) Proposing a decision path integrator modeling method for the target region of decision paths for experimental validation. In this study, 293 patients from the BraTS2020 dataset are used as research data, and the accuracy of the fitted model is obtained to be 0.92, which is basically the same as that of Random Forest, but the model in this study has a better and simpler internal structure. At the same time, this study validated the relationship between Flair sequence and the edema region of glioma, and the experimental results showed that our extracted decision paths have a certain auxiliary effect on the segmentation of U-Net, and also proved the effectiveness of our proposed interpretability method.

关键词： Accuracy Correlation Magnetic resonance imaging Fitting Evolutionary computation data models Natural language processing Logic Random forests

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LFD-RSNet: Lightweight Face Detection network for Rainy Weather Scenes

LFD-RSNet: Lightweight Face Detection Network for Rainy Weat...

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Natural Language Processing (ICNLP), International Conference on

作者： Xiaoying Pan Yamie Ding Nijuan Zhang Yaya Mu Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In response to the problem of low detection accuracy of lightweight models for face detection in complex rainy scenes, this paper proposes a face detection network FDL-RSNet for complex rainy scenes. This network integrates an adaptive image denoising module, an optimizer module, an HCA attention module, and a balanced branching strategy. The FDL-RSNet network can effectively address various challenges in complex rainy backgrounds, such as raindrop noise, differences in image lighting and contrast, and face detection challenges caused by low-quality images with dense targets. Experimental results on the UFDD Rain datasets show that FDL-RSNet has superior face detection capabilities in real-world unconstrained complex rainy scenes, Proved its effectiveness and reliability under such highly challenging conditions.

关键词： Adaptation models Rain Adaptive systems Noise Lighting Natural language processing Face detection Reliability Meteorology Image denoising

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A Multi-Objective Evolutionary Stacking Integration Approach for Survival Prediction in GBM Patients

A Multi-Objective Evolutionary Stacking Integration Approach...

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Natural Language Processing (ICNLP), International Conference on

作者： Fangyuan Du Hao Chen Kunjun Chen Xuelian Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

To address the challenge of predicting survival rates for patients with Glioblastoma (GBM) after radiation therapy, we propose a two-stage integrated learning method that is based on Stacking and multi-objective differential evolution algorithm. Our aim is to achieve a balance between accuracy and interpretability. We have created a framework for survival prediction using radiomics features of gliomas that are imaged in subregions. Our method constructs a model for predicting GBM survival and visualizes decision rules that are easy to interpret. The method involves two primary steps: 1) Various combinations of MRI sequences and glioma subregions were evaluated to determine the optimal sequence, and the GBM long and short survival classification task in the first stage of the task was achieved by compressing features using spectral clustering. 2) An evolutionary integrated learning method based on Stacking was proposed. It uses a Random Forest classifier as the basic learning module of the integrated model and relies on the multi-objective optimization algorithm NSGA-II to continuously iterate with the aim of maximizing the regression accuracy while minimizing the model's integration complexity. We used 119 GBM patients in the BraTS2020 dataset as our study data. Based on the five-fold cross-validation, the survival classification Accuracy and Recall of our long- and short-term classification algorithm for GBM patients were 0.981 and 0.943, respectively; and the Mean-Square Error (MSE) of the survival prediction of GBM patients with the stack-based evolutionary integration method was 55713.08. The experimental results showed that our proposed method for GBM survival days prediction is significantly better than existing Machine Learning (ML) methods. This finding enables us to stratify patients more effectively and provides a more reliable basis for achieving personalized treatment for GBM patients.

关键词： Learning systems Accuracy Protocols Stacking Semantics Classification algorithms Radiation therapy Reliability Random forests Radiomics

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3D Hand Pose Estimation Based on External Attention

3D Hand Pose Estimation Based on External Attention

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Natural Language Processing (ICNLP), International Conference on

作者： Shoukun Li Xiaoying Pan Beibei Wang Jialong Gao School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

3D hand pose for a single depth image is an important topic in computer vision and human-computer interaction, and although significant progress has been made in this field in recent years, there is still room for improvement in accuracy for some specific application scenarios. To address this problem, a 3D hand pose estimation algorithm based on external attention is proposed. First, the target features are extracted by an hourglass network; then, a HEM (Hard Example Mining) loss based on a mean-variance loss function is proposed, which firstly calculates the L2 loss values of all N keypoints, and then sorts these loss values, and back-propagates the gradient only to the first m loss values. Meanwhile, external attention is introduced to enhance the ability to perceive the global information of the target, and the recognition ability of the features is improved by giving the features different influences through the attention weights. Experimental results show that the algorithm achieves an average distance error of 5.42 mm on the ICVL dataset and 7.11 mm on the MSRA dataset, which further improves the detection performance of the 3D hand pose estimation algorithm.

关键词： Human computer interaction Computer vision Three-dimensional displays Accuracy Target recognition Pose estimation Feature extraction Prediction algorithms Natural language processing

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TSARNet :Integrated spatio-temporal attention mechanism for multivariate time series prediction deep learning framework

TSARNet :Integrated spatio-temporal attention mechanism for ...

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Natural Language Processing (ICNLP), International Conference on

作者： Xiaoying Pan Hao Wang Jia Sun Yaya Mu Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Multivariate time series forecasting is particularly critical in a number of areas involving air forecasting, electricity consumption and exchange rate transformations. The time series data in these fields often contain present multivariate nature, fusing long and short-term patterns, and traditional forecasting methods are often difficult to work. To address this challenge, this paper proposes a new deep learning framework, TSARNet, which combines gated recurrent units (GRUs) and an adaptive scale processing layer to effectively capture short-term localized dependency patterns among multivariate variables and reveal long-term trends through a spatio-temporal attention mechanism. In addition, an autoregressive model is incorporated to enhance the network's sensitivity to time series scale changes. Comprehensive experiments on four publicly available datasets show that the proposed TSARNet algorithm outperforms other comparative methods in most cases in terms of mean absolute error (MAE) and root mean square error (RMSE) in the long term prediction of different datasets. The TSARNet method also achieves optimal results for doing long-term prediction for each dimension of the power transformer temperature dataset.

关键词： Deep learning Attention mechanisms Accuracy Sensitivity Time series analysis Predictive models Market research Prediction algorithms Forecasting Root mean square

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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 44-58页

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

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 capabilities. To address these issues, we present AMHF-TP, an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance performance. The 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 features. Compared with leading methods,the proposed AMHF-TP demonstrates superior precision, accuracy, and coverage, underscoring its effectiveness and robustness in MFTP recognition. The comparative analysis of separate hierarchical models and the combined model, as well as with five contemporary models, reveals AMHF-TP's exceptional performance and stability in recognition tasks.

关键词：

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Landmark-based multi-objective route planning for large-scale road net 21

Landmark-based multi-objective route planning for large-scal...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Sun, Jiaze Han, Nan Huang, Jianbin Deng, Jiahui Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an China Xidian University Xi'an China Northwest University Xi'an China

ISBN: (纸本)9781450383516

In this paper, for two objectives of the minimum driving distance and the maximum number of passengers compatible, a landmark-based multi-objective route planning algorithm (LB-MORPA) is proposed. LB-MORP incorporates the idea of a bi-directional random walk into the generation of the initial population, designs a new crossover and mutation operator for the evolutionary process, and predicts a set of the nondominated solution set to provide drivers with multiple route options. The comparison experiments with other classical multi-objective evolutionary algorithms prove that the LB-MORP has good performance. © 2021 Owner/Author.

关键词： Multiobjective optimization

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Robust Visual Question Answering: datasets, Methods, and Future Challenges

arXiv

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arXiv 2023年

作者： Ma, Jie Wang, Pinghui Kong, Dechen Wang, Zewei Liu, Jun Pei, Hongbin Zhao, Junzhou Di, Jia The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China

Visual question answering requires a system to provide an accurate natural language answer given an image and a natural language question. However, it is widely recognized that previous generic VQA methods often tend to memorize biases present in the training data rather than learning proper behaviors, such as grounding images before predicting answers. Therefore, these methods usually achieve high in-distribution but poor out-of-distribution performance. In recent years, various datasets and debiasing methods have been proposed to evaluate and enhance the VQA robustness, respectively. This paper provides the first comprehensive survey focused on this emerging fashion. Specifically, we first provide an overview of the development process of datasets from in-distribution and out-of-distribution perspectives. Then, we examine the evaluation metrics employed by these datasets. Thirdly, we propose a typology that presents the development process, similarities and differences, robustness comparison, and technical features of existing debiasing methods. Furthermore, we analyze and discuss the robustness of representative vision-and-language pre-training models on VQA. Finally, through a thorough review of the available literature and experimental analysis, we discuss the key areas for future research from various viewpoints. Copyright © 2023, The Authors. All rights reserved.

关键词： Visual languages

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