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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Yu, Guohui Wang, Lihui Chen, Qijian Wang, Li Tang, Kun Cheng, Xinyu Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France

ISBN: (纸本)9798350387384

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. The MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. The MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. The convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets. © 2024 IEEE.

关键词： Semantic Segmentation

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xinzheng Li Yuhong Zhang Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Cross-network node classification aims to use a labeled source network to classify nodes in an unlabeled target network. Most of the existing cross-network node classification methods learn the network representations by capturing the node neighborhood and train the classifier on these representations. The performance is highly dependent on the high-quality neighborhood in the network. However, in applications, the degree of nodes generally follows a long-tail distribution, i.e., a significant proportion of nodes are tail nodes with sparse neighborhood. It poses a challenge to existing methods. To this end, a structure similarity graph for cross-network node classitication method (SCNC) is proposed in this paper. Firstly, the potential links between nodes are predicted with the structural similarity metric to construct structure similarity graph, which can enrich the neighborhood of tail nodes. Then, the embedding representations of the structural similarity graph are learned to capture more neighborhood information. Finally, the adversarial is used to learn the domain invariant representations to address cross-network divergence. Extensive experimental results show that our SCNC outperforms the state-of-the-art methods.

关键词： Network topology Tail Learning (artificial intelligence) Prediction algorithms Classification algorithms Noise measurement Task analysis

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A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment Analysis 24

A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment An...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Dang, Lanxue Wang, Chunyu Han, Hongyu Hou, Yan-E Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China School of Computer Science and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9798350319934

Sentiment analysis is an important and challenging task in the field of natural language processing. Researchers have used vocabulary-based methods and machine learning methods to conduct research on sentiment analysis tasks. In recent years, deep learning has achieved great success in the field of sentiment analysis. However, for complex textual data, using a single model is often insufficient. Inspired by deep learning models, in this study, we proposed a hybrid model using BiLSTM and attention mechanism, called BiLSTM-ATT model, to solve the problem of sentiment analysis. First, we adopted the GloVe method to train the initialized word embeddings. GloVe converts textual information into word vectors, which can calculate the distance between words. Next, we used a convolutional neural network that can extract local features, and a BiLSTM that can extract long-range semantic features of text with bidirectional extraction of long-term dependencies. Finally, the attention mechanism was used to improve the performance of the model by calculating the weight of the data. Experimental results demonstrate that our proposed hybrid BiLSTM-ATT model outperforms traditional deep learning methods in Accuracy, Recall, and Fl-Score. Our method was compared with deep learning methods on the IMDB movie review dataset. © 2022 IEEE.

关键词： Sentiment analysis

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Deep Learning-Based Prediction of Myelosuppression in Lymphoma Patients During Chemotherapy Using Multimodal Radiological Images with Subcutaneous Adipose Tissue

Deep Learning-Based Prediction of Myelosuppression in Lympho...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Du, Tianming Sun, Hongzan Yang, Jinzhu Grzegorzek, Marcin Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Shengjing Hospital China Medical University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland

ISBN: (纸本)9789819708543

Lymphoma is a malignant tumor, and diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma. Due to its biological characteristics, surgical treatment is difficult. The main treatment for DLBCL is chemotherapy, with the R-CHOP regimen being the most common. The vast majority of patients require lifelong treatment. Myelosuppression during chemotherapy is the most common adverse reaction in DLBCL patients and directly affects the progress of chemotherapy. Accurately predicting whether patients need early intervention before chemotherapy can greatly improve their prognosis. In this paper, we propose a neural network that uses PET/CT images of subcutaneous adipose tissue before treatment to predict myelosuppression in DLBCL patients. The model achieves a classification accuracy of 93.57%. This indicates that the growth distribution pattern and metabolic characteristics of adipose tissue are important for DLBCL patients. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Chemotherapy

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Nearest is Not Dearest: Towards Practical Defense Against Quantization-Conditioned Backdoor Attacks

Nearest is Not Dearest: Towards Practical Defense Against Qu...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Boheng Li Yishuo Cai Haowei Li Feng Xue Zhifeng Li Yiming Li The State Key Laboratory of Blockchain and Data Security Zhejiang University School of Cyber Science and Engineering Wuhan University School of Computer Science and Engineering Central South University X Digital Dynamics Tencent Data Platform

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Model quantization is widely used to compress and ac-celerate deep neural networks. However, recent studies have revealed the feasibility of weaponizing model quan-tization via implanting quantization-conditioned backdoors (QCBs). These special backdoors stay dormant on released full-precision models but will come into effect after stan-dard quantization. Due to the peculiarity of QCBs, existing defenses have minor effects on reducing their threats or are even infeasible. In this paper, we conduct the first in-depth analysis of QCBs. We reveal that the activation of existing QCBs primarily stems from the nearest rounding operation and is closely related to the norms of neuron-wise truncation errors (i.e., the difference between the continuous full-precision weights and its quantized version). Motivated by these insights, we propose Error-guided Flipped Rounding with Activation Preservation (EFRAP), an effective and practical defense against QCBs. Specifically, EFRAP learns a non-nearest rounding strategy with neuron-wise er-ror norm and layer-wise activation preservation guidance, flipping the rounding strategies of neurons crucial for back-door effects but with minimal impact on clean accuracy. Ex-tensive evaluations on benchmark datasets demonstrate that our EFRAP can defeat state-of-the-art QCB attacks under various settings. Code is available here.

关键词： Computer vision Quantization (signal) Accuracy Codes Weapons Neurons Pattern recognition

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Privacy Protection for Image Sensors Using Convolutional Neural Network and Generative Adversarial Nets

Privacy Protection for Image Sensors Using Convolutional Neu...

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International Conference on Computer and Communications (ICCC)

作者： Shihao Tang Yaoqi Wang Tao Meng Ji Qin Liu Lin School of Mechanical Engineering Guizhou University Guiyang China Guizhou Provincial Key Laboratory of Public Big Data Guiyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

An image privacy information protection method that does not damage the original image quality is proposed considering the risk of privacy information leakage due to the widespread use of image sensors and image sharing in social platforms. A convolutional neural network is used to detect the privacy information of the image. A generative adversarial network is then employed for the detected privacy content to protect image privacy by transforming the privacy information into non-privacy information without damaging the original image quality. Numerous studies have been conducted on real-world images., and results prove the efficiency and effectiveness of the proposed method.

关键词： Image sensors Image quality Privacy data privacy Three-dimensional displays Psychology Generative adversarial networks User experience Convolutional neural networks Protection

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Learning with Open-world Noisy data via Class-independent Margin in Dual Representation Space

arXiv

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arXiv 2025年

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Ion-acoustic wave structures in the fluid ions modeled by higher dimensional generalized Korteweg-de Vries-Zakharov-Kuznetsov equation

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Journal of Ocean engineering and Science 2023年第6期8卷 623-635页

作者： U.Younas J.Ren Muhammad Z.Baber Muhammad W.Yasin T.Shahzad State Key Laboratory of Power Grid Environmental Protection/Henan Academy of Big Data Zhengzhou UniversityZhengzhou 450001China Department of Mathematics and Statistics The University of LahoreLahorePakistan Department of mathematics University of NarowalNarowalPakistn Department of Basic Sciences and Humanities Narowal CampusUniversity of Engineering and Technology Lahore 54890Pakistan

In this paper,the higher dimensional generalized Korteweg-de-Varies-Zakharov-Kuznetsov(gKdV-ZK)equation is under *** model is used in the field of plasma physics which describes the effects of magnetic field on the weak ion-acoustic *** have applied two techniques,called asφ^(6)-model expansion method and the Hirota bilinear method(HBM)to explore the diversity of wave *** solutions are expressed in the form of hyperbolic,periodic and Jacobi elliptic function(JEF)***,the solitary wave solutions are also extracted.A comparison of our results to well-known results is made,and the study concludes that the solutions achieved here are ***,3-dimensional and contour profiles of achieved outcomes are drawn in order to study their dynamics as a function of parameter *** the basis of the obtained results,we can assert that the pro-posed computational methods are straightforward,dynamic,and well-organized,and will be useful for solving more complicated nonlinear problems in a variety of fields,particularly in nonlinear sciences,in conjunction with symbolic ***,our discoveries provide an important milestone in comprehending the structure and physical behavior of complex *** hope that our findings will contribute significantly to our understanding of ocean *** study,we hope,is appropriate and will be of significance to a broad range of experts involved in ocean engineering models.

关键词： Wave solutions gKdV-ZK equation Bilinear transformation φ^(6)-model expansion method

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data Processing Technology for Network Abnormal Traffic Detection 4

Data Processing Technology for Network Abnormal Traffic Dete...

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4th International Conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Wang, Kun Fu, Yu Duan, Xueyuan Xu, Jianqiao Liu, Taotao Department of Information Security Naval University of Engineering Wuhan430033 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Xinyang464000 China College of Computer and Information Technology Xinyang Normal University Xinyang464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang464000 China

At present, the threats to network security are also increasing, among which abnormal traffic detection is the key link to ensure network security. Traditional detection methods based on signature or threshold are often difficult to adapt to the increasingly complex network environment and new attack methods. Therefore, this paper optimizes and improves the data processing technology, proposes a network ATD method based on particle swarm optimization(PSO) algorithm, and explores in detail the traffic data collection and pre-processing, the feature recognition of abnormal traffic, the application of PSO algorithm, real-time monitoring and response mechanism. The results of two sets of simulation experiments are as follows: compared with the traditional model, the accuracy rate of ATD of the improved algorithm is increased by 7.2% on average, and the detection time is reduced by 7.35s on average. This method not only enhances the adaptability of the model to new attacks, but also improves the degree of automation of detection. © 2024 The Authors.

关键词： Particle swarm optimization (PSO)

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Fuzzy cognitive maps learning algorithm based on modified covariance matrix adaptation evolution strategy

Fuzzy cognitive maps learning algorithm based on modified co...

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2023 International Conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2023

作者： Li, Zhuofan Wang, Jingping Zhang, Yingjun Huang, Hua Yin, Hui Xu, Hongli Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Information Technology Center Beijing Jiaotong University Beijing100044 China Key Laboratory of Beijing for Railway Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510666474

Fuzzy cognitive maps (FCMs) are an efficient soft computing tool commonly used for modeling and analyzing complex systems, and have significant advantages in knowledge representation and fast reasoning. At present, many researchers have devoted themselves to solving the learning problem of FCMs. However, most of the existing learning algorithms are prone to local convergence and are not suitable for large-scale FCMs learning problems. Moreover, the learning problem of FCMs is often a complex non-convex optimization problem, which is easy to cause the final learning result of the algorithm to be only a local optimal solution. To address these limitations, this paper proposes a novel algorithm for learning FCMs by combining multimodal optimization and modified covariance matrix adaptation evolution strategy (CMA-ES), termed as MCMA-ES. First, the learning problem of FCMs is modeled as a multimodal optimization problem (MMOP), and then a multimodal optimization algorithm is proposed by combining the detect multimodal clustering (DMC) and CMA-ES, and the cubic chaos factor is combined to enhance the capability of local search. Finally the proposed algorithm is combined with the decomposition strategy to learn FCMs. Experiments are carried out on 5 benchmark datasets, and MCMA-ES is also applied to 15 groups of large-scale gene regulatory system reconstruction problems. The experimental results show that MCMA-ES has high learning generalization and accuracy. © 2023 SPIE.

关键词： Fuzzy Cognitive Maps

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