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12th IFIP TC 12 International Conference on intelligent information processing, IIP 2022

作者： Sun, Xiaoping Zhang, Junsheng Sang, Yufei Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Scientific and Technical Information of China Beijing China School of Economics Capital University of Economics and Business Beijing China

ISBN: (纸本)9783031039478

Preventing explosion of rumors on the Internet asks for a quick automatic detection mechanism that can detect rumors according to the given true information. Previous automatic rumors detection models are mainly built by training a supervised classification model on a labeled dataset containing rumor samples and true information samples. However, in many real cases, there is only one short piece of available true information sample given by an authority in form of an explanation or a correction of a rumor. The explanation sample is often short and very similar to rumor samples, making it difficult to train a discriminative classifier to check whether a piece of information is a rumor or an explanation of rumors. It is necessary to build a model to detect whether a short text is a rumor text or an explanation of rumors, which can be used to as evidences for detecting and refuting rumors. In this paper, we presented a sentence preprocessing method that extracts the leftmost longest common sequence to obtain the common and difference subsequences between the rumor text and its explanation text to compose samples and train a supervised model for classification between rumors and explanation of rumors. Experiments show the effectiveness of the proposed method. © 2022, IFIP International Federation for information processing.

关键词： Classification (of information)

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StylizedGS: Controllable Stylization for 3D Gaussian Splatting

arXiv

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arXiv 2024年

作者： Zhang, Dingxi Chen, Zhuoxun Yuan, Yu-Jie Zhang, Fang-Lue He, Zhenliang Shan, Shiguang Gao, Lin The University of Chinese Academy of Sciences Beijing China The Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China Victoria University of Wellington New Zealand The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

With the rapid development of XR, 3D generation and editing are becoming more and more important, among which, stylization is an important tool of 3D appearance editing. It can achieve consistent 3D artistic stylization given a single reference style image and thus is a user-friendly editing way. However, recent NeRF-based 3D stylization methods face efficiency issues that affect the actual user experience and the implicit nature limits its ability to transfer the geometric pattern styles. Additionally, the ability for artists to exert flexible control over stylized scenes is considered highly desirable, fostering an environment conducive to creative exploration. In this paper, we introduce StylizedGS, a 3D neural style transfer framework with adaptable control over perceptual factors based on 3D Gaussian Splatting (3DGS) representation. The 3DGS brings the benefits of high efficiency. We propose a GS filter to eliminate floaters in the reconstruction which affects the stylization effects before stylization. Then the nearest neighbor-based style loss is introduced to achieve stylization by fine-tuning the geometry and color parameters of 3DGS, while a depth preservation loss with other regularizations is proposed to prevent the tampering of geometry content. Moreover, facilitated by specially designed losses, StylizedGS enables users to control color, stylized scale and regions during the stylization to possess customized capabilities. Our method can attain high-quality stylization results characterized by faithful brushstrokes and geometric consistency with flexible controls. Extensive experiments across various scenes and styles demonstrate the effectiveness and efficiency of our method concerning both stylization quality and inference FPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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Multi-perspective Text Matching Algorithm Based on Multi-granularity Feature Convolution 12

Multi-perspective Text Matching Algorithm Based on Multi-gra...

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12th International Conference on computing and Pattern Recognition, ICCPR 2023

作者： Qiang, Baohua Guo, Zhiwen Xi, Guangyong Guo, Shuiping Wang, Yufeng Yang, Xianyi Wang, Yuemeng Guilin University of Electronic Technology Guilin China The 7th Research Institute of Cetc Guangzhou China The 54th Research Institute of Cetc Shijiazhuang China Hebei Key Laboratory of Intelligent Information Perception and Processing Shijiazhuang China

ISBN: (纸本)9798400707988

The core of Chinese text matching task lies in mining the deep semantic information inside the text, exploring the semantic similarities and differences between different texts, and then analyzing the semantic similarity between two texts to be matched. To address the problems of single text granularity feature, insufficient capture of potential semantic information at multiple granularities and weak interactive matching of coding features in Chinese text matching, we propose a multi-perspective text matching model based on multi-granularity features convolution (MpmMfc). The model first extracts characters, words and associated phrases by multi-pattern partitioning and performs initial encoding, then uses a two-way gate loop control unit to initially extract the contextual semantic information in the encoding. Then a multi-grain size high-dimensional encoding matrix is constructed and convolutional neural network is used to capture the granularity features to improve the characterization of multi-granularity semantic information. Finally, the multi-granularity convolutional matrices of two texts are cross-cosine matched by multi-perspective matching patterns to enhance the interaction strength of multi-granularity feature information. The results achieved in the experiments of LCQMC, a spoken expression class dataset, and BQ, a financial class dataset, are better than the currently available non-BERT type text matching models. © 2023 ACM.

关键词： Convolution

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Facial StO2: A New Promising Biometric Identity

Facial StO2: A New Promising Biometric Identity

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Peng, Dairong Sun, Sirui Liu, Xinyu Zhou, Ju Chen, Tong Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing400715 China Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Chongqing400715 China Southwest University School of Electronic and Information Engineering Chongqing400715 China

ISBN: (纸本)9781665468190

In this paper, we introduce a new biometric identity, facial tissue oxygen saturation (StO2). StO2 is an index of blood oxygen content in tissues and is related to blood vessel distribution pattern and metabolic rate. Experimental results show that classification accuracy can reach 83.33% in 42 participants with different stress states by using StO2 as the only input to the ResNet-50 model. We also proposed a module called StO2Net to eliminate the effects of stress on classification. The highest accuracy can reach up to 90.48% when the module is used. This pilot study shows that facial StO2 can be a promising biometric feature for identity recognition. © 2022 IEEE.

关键词： Biometrics

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Efficient Finite-Difference Time-Domain Method for Solving Electromagnetic Scattering in Layered Anisotropies

Efficient Finite-Difference Time-Domain Method for Solving E...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yilin Kang Yuxian Zhang Xiaoli Feng Lixia Yang Zhixiang Huang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education and with the Information Materials and Intelligent Sensing Laboratory of Anhui Province Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits and with School of Electronic and Information Engineering Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

In this article, we propose an efficient numerical method for layered models based on the conventional finite difference time-domain (FDTD) method. The three-dimensional (3-D) layered model is compressed into one-dimensional (1-D) calculation, which improves the problem of large space memory and long running time for 3-D modeling while ensuring the accuracy and feasibility of the algorithm. On the one hand, by employing the transformation relationship between the transverse wave vector and the field in space, the expanded Maxwell's rotation equations are simplified, so that the equation system only retains the spatial transformation in the propagation direction. This processing allows for the calculation of the propagation characteristics of layered media without requiring more computational resources. On the other hand, to ensure the universality of the algorithm, a fully tensor anisotropic medium is selected for detecting the propagation data. At the same time, we design the layered structure and conduct the consistent 3-D modeling in the simulation software COMSOL. The results showed that our proposed efficient numerical algorithm greatly reduced the loss of computing resources while ensuring accuracy.

关键词： Solid modeling Three-dimensional displays Accuracy Tensors Software algorithms Vectors Mathematical models Numerical models Time-domain analysis Finite difference methods

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A Dual-Branch Network Based on ViT and Mamba for Semantic Segmentation of Remote Sensing Image

A Dual-Branch Network Based on ViT and Mamba for Semantic Se...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Ke An Ying Wang Liang Chen Yupie Wang National Key Laboratory for Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China Shanghai Academy of Spaceflight Technology China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Semantic segmentation of remote sensing images has significant applications across various scenarios. The prevailing frameworks include Convolutional Neural Network (CNN) and Transformer. However, CNN is limited by the receptive field of convolutions, while the Transformer is constrained by computational complexity, which restricts attention calculations to local windows and fails to effectively address long-range dependency modeling. The efficient Mamba architecture, characterized by linear complexity, offers a promising solution to these challenges. Inspired by Mamba, we propose a dual-branch network based on ViT and Mamba. The Vision Transformer (ViT) branch incorporates the Swin Transformer to model spatial details while maintaining computational complexity within acceptable bounds. Complementarily, the Mamba branch efficiently captures global context and long-range dependencies. Additionally, to suppress noise and conflicting information arising from the fusion of features from different frameworks, we design the Cross-Model Fusion Module (CMFM) and the Cross-Model Relevance Loss (CMRLoss) to achieve semantic consistency in the fusion process. The comprehensive experimental findings on the commonly utilized GaoFen-2 and iSAID datasets clearly illustrate the advantages of our proposed approach compared to the leading methods in the field.

关键词： Semantic segmentation Computational modeling Semantics Noise Transformers Data processing Sensors Convolutional neural networks Computational complexity Remote sensing

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SELF-SUPERVISED DIFFUSION MRI DENOISING VIA ITERATIVE AND STABLE REFINEMENT

arXiv

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arXiv 2025年

作者： Wu, Chenxu Kong, Qingpeng Jiang, Zihang Kevin Zhou, S. School of Biomedical Engineering Division of Life Sciences and Medicine USTC China MIRACLE Center Suzhou Institute for Advance Research USTC China State Key Laboratory of Precision and Intelligent Chemistry USTC China Key Laboratory of Intelligent Information Processing of CAS Institute of Computing Technology CAS China

Magnetic Resonance Imaging (MRI), including diffusion MRI (dMRI), serves as a "microscope" for anatomical structures and routinely mitigates the influence of low signal-to-noise ratio scans by compromising temporal or spatial resolution. However, these compromises fail to meet clinical demands for both efficiency and precision. Consequently, denoising is a vital preprocessing step, particularly for dMRI, where clean data is unavailable. In this paper, we introduce Di-Fusion, a fully self-supervised denoising method that leverages the latter diffusion steps and an adaptive sampling process. Unlike previous approaches, our single-stage framework achieves efficient and stable training without extra noise model training and offers adaptive and controllable results in the sampling process. Our thorough experiments on real and simulated data demonstrate that Di-Fusion achieves state-of-the-art performance in microstructure modeling, tractography tracking, and other downstream tasks. Code is available at https://***/FouierL/Di-Fusion. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal to noise ratio

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M²-Net: Multitask-Learning-Based Multiband Signal Recognition Network

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IEEE Internet of Things Journal 2025年第11期12卷 16543-16558页

作者： Xingjian Zhang Pengxu Wang Yuan Ma Jian Jiao Shaohua Wu Qinyu Zhang Guangdong Provincial Key Laboratory of ACNT Harbin Institute of Technology (Shenzhen) Shenzhen China Department of Boardband Communications Pengcheng Laboratory Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration the Guangdong Key Laboratory of Intelligent Information Processing and the College of Electronic and Information Engineering Shenzhen University Shenzhen China

Traditional signal recognition requires the design of multiple different deep neural networks to handle different signal recognition tasks, which not only fails to take into account the correlation among different subtasks, but also leads to large model size and higher computational complexity. In this work, we propose a multitask-learning-based multiband signal recognition network $(\text {M}^{2}\text {-Net})$ to simultaneously recognize the location of occupied frequency bands, modulation types, and signal types. The proposed $\text {M}^{2}\text {-Net}$ consists of two main parts: 1) shared feature extraction network (SFEN) and 2) multitask classification header (MCH). In SFEN, a plug-and-play multitask feature extraction convolution and an adaptive threshold denoising module are introduced to provide better shared feature extraction and denoising performance. In MCH, the shared features obtained from SFEN are further processed for different recognition tasks. Furthermore, during the multitask model training, homoscedastic uncertainty is introduced as a task-dependent weight to adaptively balance the training loss of different tasks. To evaluate the recognition performance of the proposed method, we construct a multiband signal dataset and compare $\text {M}^{2}\text {-Net}$ with several state-of-the-art models in signal recognition field. Experiment results show that the proposed $\text {M}^{2}\text {-Net}$ has significant performance improvements in terms of recognition accuracy and model complexity, especially under low signal-to-noise ratio conditions.

关键词： Sensors Training Feature extraction Multitasking Wireless communication Accuracy Wireless sensor networks Pattern classification Narrowband Computational modeling

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BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance 26

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

引用

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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TFGDA: Exploring Topology and Feature Alignment in Semi-supervised Graph Domain Adaptation through Robust Clustering 38

TFGDA: Exploring Topology and Feature Alignment in Semi-supe...

引用

38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Dan, Jun Liu, Weiming Xie, Chunfeng Yu, Hua Dong, Shunjie Tan, Yanchao Zhejiang University China Queen Mary University of London United Kingdom Dalian University of Technology China Shanghai Jiao Tong University China Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China Fujian Key Laboratory of Network Computing and Intelligent Information Processing China

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, most existing studies primarily concentrate on aligning feature distributions directly to extract domain-invariant features, while ignoring the utilization of the intrinsic structure information in graphs. Inspired by the significance of data structure information in enhancing models' generalization performance, this paper aims to investigate how to leverage the structure information to assist graph transfer learning. To this end, we propose an innovative framework called TFGDA. Specially, TFGDA employs a structure alignment strategy named STSA to encode graphs' topological structure information into the latent space, greatly facilitating the learning of transferable features. To achieve a stable alignment of feature distributions, we also introduce a SDA strategy to mitigate domain discrepancy on the sphere. Moreover, to address the overfitting issue caused by label scarcity, a simple but effective RNC strategy is devised to guide the discriminative clustering of unlabeled nodes. Experiments on various benchmarks demonstrate the superiority of TFGDA over SOTA methods. © 2024 Neural information processing systems foundation. All rights reserved.

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