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Multi-domain fake news detection method based on generative adversarial network and graph network

Knowledge-Based Systems 2025年 319卷

作者： Li, Xuefeng Wei, Jian Zhao, Chensu Fan, Xiaqiong Wang, Yuhang Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou450001 China Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology Zhengzhou China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China Shandong Woman's University Jinan250002 China

The proliferation of misinformation in today's digital era poses significant challenges, with fake news detection becoming critical to mitigate economic losses and social instability. Despite extensive research efforts, most existing approaches are tailored for single-domain fake news detection, struggling with data distribution discrepancies and domain shifts when applied to multi-domain scenarios. This limitation underscores the urgent need for solutions that address the complexities of cross-domain detection. Here, we propose a novel framework MFGAG that synergistically integrates adversarial networks and graph neural networks with emotional, stylistic, and semantic features to enable precise domain localization. By leveraging these features, the framework effectively models intricate relationships among news articles within the same temporal context, addressing the challenges posed by multi-domain datasets. Experimental evaluations demonstrate that our approach outperforms state-of-the-art methods, achieving an average accuracy improvement of 3.3 percentage points for single-domain news and nearly 1 percentage point for mixed-domain data, culminating in an overall accuracy of 93.1 %. The code involved in this study is publicly available on website https://***/SWLee777/MFGAG. © 2025 Elsevier B.V.

关键词： Network theory (graphs)

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Sandwich-structured flexible strain sensors for gesture recognition in human–computer interaction

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The European Physical Journal Special Topics 2025年 1-11页

作者： Chen, Guanzheng Zhang, Xin Sun, Zeng Luo, Xuanzi Fang, Guoqing Wu, Huaping Cheng, Lin Liu, Aiping Zhejiang Key Laboratory of Quantum State Control and Optical Field Manipulation Department of Physics Zhejiang Sci-Tech University Hangzhou China Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education and Zhejiang Province College of Mechanical Engineering Zhejiang University of Technology Hangzhou China

Flexible strain sensors are crucial for advancing human–computer interaction (HCI) technologies, especially in applications that require precise and adaptive sensing capabilities. In this study, we use polydimethylsiloxane (PDMS) as the substrate material to construct sandwich-structured flexible strain sensors, incorporating a CNT/PDMS conductive polymer (CP) layer between two cross-positioned PDMS-transferred laser-induced graphene (LIG) strips (PL). The sandwich-structured PL/CP/PL sensor exhibits excellent flexibility, structural stability, and component compatibility, effectively enabling the detection of tensile, compressive, and bending strains while overcoming the limitations of single-layer strain sensors and facilitating the measurement of two-dimensional directional forces. Leveraging these capabilities, we apply the sensor to sign language recognition and develop a gesture-based HCI system that utilizes Morse and binary codes to enable intuitive, intelligent communication with the sensors. This advancement holds significant potential to enhance user experience and operational efficiency in virtual reality environments, coded communications systems, and robotics.

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Dynamic View Synthesis from Small Camera Motion Videos

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IEEE Transactions on Visualization and Computer Graphics 2025年 PP卷 PP页

作者： Sun, Huiqiang Li, Xingyi Peng, Juewen Shen, Liao Cao, Zhiguo Xian, Ke Lin, Guosheng Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China S-Lab 639798 Singapore Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China

Novel view synthesis for dynamic 3D scenes poses a significant challenge. Many notable efforts use NeRF-based approaches to address this task and yield impressive results. However, these methods rely heavily on sufficient motion parallax in the input images or videos. When the camera motion range becomes limited or even stationary (i.e., small camera motion), existing methods encounter two primary challenges: incorrect representation of scene geometry and inaccurate estimation of camera parameters. These challenges make prior methods struggle to produce satisfactory results or even become invalid. To address the first challenge, we propose a novel Distribution-based Depth Regularization (DDR) that ensures the rendering weight distribution to align with the true distribution. Specifically, unlike previous methods that use depth loss to calculate the error of the expectation, we calculate the expectation of the error by using Gumbel-softmax to differentiably sample points from discrete rendering weight distribution. Additionally, we introduce constraints that enforce the volume density of spatial points before the object boundary along the ray to be near zero, ensuring that our model learns the correct geometry of the scene. To demystify the DDR, we further propose a visualization tool that enables observing the scene geometry representation at the rendering weight level. For the second challenge, we incorporate camera parameter learning during training to enhance the robustness of our model to camera parameters. We conduct extensive experiments to demonstrate the effectiveness of our approach in representing scenes with small camera motion input, and our results compare favorably to state-of-the-art methods. © 1995-2012 IEEE.

关键词： Geometry

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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A promising strategy for assessing the uricosuric effect of anti-hyperuricemia candidates: a case study of caffeoylquinic acids extract of Artemisia selengensis Turcz. leaves

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Food, Nutrition and Health 2025年第1期2卷 1-12页

作者： Xiang, Lin Mansour, Mohammed Huang, Yuting Wu, Ting Pan, Siyi Xu, Xiaoyun College of Food Science and Technology Huazhong Agricultural University Wuhan China Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control Huazhong Agricultural University Wuhan China Key Laboratory of Environment Correlative Dietology (Ministry of Education) Huazhong Agricultural University Wuhan China National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops Huazhong Agricultural University Wuhan China

Caffeoylquinic acids (CQAs) are phenolic acids found in various plants that possess multiple biological activities including anti-oxidation, anti-inflammation, and xanthine oxidase inhibitory activity. Our previous research demonstrated that CQAs-rich Artemisia selengensis Turcz. leaves extract (ASTLE) effectively lowered uric acid levels in hyperuricemia mice. However, further evidence is needed to elucidate the mechanism of the uric acid-lowering effect of ASTLE. In this study, a sensitive but non-radioactive in vitro method based on 6-carboxyfluorescein (6-CFL) was established, validated, and further applied to assess the uricosuric potential of ASTLE. Furthermore, molecular docking, qRT-PCR, and western blot were employed to evidence the uricosuric effect of ASTLE. The results demonstrated that ASTLE exhibited the potential to inhibit uric acid reabsorption, manifested as the inhibition of 6-CFL uptake by human kidney 2 (HK-2) cells (IC50 = 533.67 ± 19.91 µg/mL), and CQAs were identified as its dominant bioactive compounds. Mechanism investigation further revealed that the uptake inhibitory effect of ASTLE was attributed to the hydrogen bonding interactions of CQAs, particularly di-CQAs, with uric acid transporters URAT1 and GLUT9, as well as to the down-regulation of mRNA and protein expression of URAT1 and GLUT9 and the up-regulation of mRNA and protein expression of ABCG2 by ASTLE. This study indicated that ASTLE has potent uricosuric effect. Moreover, this study suggested that the combination of 6-CFL uptake inhibition assessment, molecular docking, qRT-PCR, and western blot analysis could be a promising strategy for revealing the role of anti-hyperuricemia candidates in promoting uric acid excretion.

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MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral image Segmentation

arXiv

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arXiv 2025年

作者： Lin, Shijie Yun, Boxiang Shen, Wei Li, Qingli Yang, Anqiang Wang, Yan Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China Department of Pathology Changning Maternal and Infant Health Hospital Shanghai China

Medical Hyperspectral Imaging (MHSI) offers potential for computational pathology and precision medicine. However, existing CNN and Transformer struggle to balance segmentation accuracy and speed due to high spatial-spectral dimensionality. In this study, we leverage Mamba’s global context modeling to propose a dual-stream architecture for joint spatial-spectral feature extraction. To address the limitation of Mamba’s unidirectional aggregation, we introduce a recurrent spectral sequence representation to capture low-redundancy global spectral features. Experiments on a public Multi-Dimensional Choledoch dataset and a private Cervical Cancer dataset show that our method outperforms state-of-the-art approaches in segmentation accuracy while minimizing resource usage and achieving the fastest inference speed. Our code will be available at https://***/DeepMed-Lab-ECNU/MDN. Copyright © 2025, The Authors. All rights reserved.

关键词： image segmentation

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MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral image Segmentation

MDN: Mamba-Driven Dualstream Network For Medical Hyperspectr...

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shijie Lin Boxiang Yun Wei Shen Qingli Li Anqiang Yang Yan Wang Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China Department of Pathology Changning Maternal and Infant Health Hospital Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： image segmentation Accuracy Precision medicine Redundancy Signal processing Transformers Speech processing Hyperspectral imaging Biomedical imaging Context modeling

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Information disentanglement for unsupervised domain adaptive Oracle Bone Inscriptions detection

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Signal processing: image Communication 2025年 138卷

作者： Gao, Feng Liu, Yongge Li, Deng Chen, Xu Jiang, Runhua Han, Yahong School of Computer and Information Engineering Anyang Normal University Henan Anyang455000 China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Henan Anyang455000 China College of Intelligence and Computing Tianjin University Tianjin300350 China

The detection of Oracle Bone Inscriptions (OBIs) is the foundation of studying the OBIs via computer technology. Oracle bone inscription data includes rubbings, handwriting, and photos. Currently, most detection methods primarily focus on rubbings and rely on large-scale annotated datasets. However, it is necessary to detect oracle bone inscriptions on both handwriting and photo domains in practical applications. Additionally, annotating handwriting and photos is time-consuming and requires expert knowledge. An effective solution is to directly transfer the knowledge learned from the existing public dataset to the unlabeled target domain. However, the domain shift between domains heavily degrades the performance of this solution. To alleviate this problem and based on the characteristics of different domains of oracle bone, in this paper, we propose an information disentanglement method for the Unsupervised Domain Adaptive (UDA) OBIs detection to improve the detection performance of OBIs in both handwriting and photos. Specifically, we construct an image content encoder and a style encoder module to decouple the oracle bone image information. Then, a reconstruction decoder is constructed to reconstruct the source domain image guided by the target domain image information to reduce the shift between domains. To demonstrate the effectiveness of our method, we constructed an OBI detection benchmark that contains three domains: rubbing, handwriting, and photo. Extensive experiments verified the effectiveness and generality of our method on domain adaptive OBIs detection. Compared to other state-of-the-art UDAOD methods, our approach achieves an improvement of 0.5% and 0.6% in mAP for handwriting and photos, respectively. © 2025 Elsevier B.V.

关键词： Object detection

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Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB images

arXiv

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arXiv 2025年

作者： Feng, Fuxiang Cong, Runmin Wei, Shoushui Zhang, Yipeng Li, Jun Kwong, Sam Zhang, Wei The School of Control Science and Engineering Shandong University Jinan250061 China The Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China The University of California Los Angeles United States The Hubei Key Laboratory of Intelligent Geo-Information Processing The School of Computer Science China University of Geo-sciences Wuhan430078 China The Lingnan University Hong Kong

Reconstructing Hyperspectral images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals that local correlation and global continuity of the spectral characteristics are crucial for HSI reconstruction tasks. Therefore, we fully explore these inter-spectral relationships and propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images. For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module, which efficiently establishes spectral band similarity within a localized range. For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module, which employs memory units to recursively model the progressive variation characteristics at the global level. In order to explore the inherent complementarity of these two modules, we design the Patch-wise Adaptive Fusion (PAF) module to efficiently integrate global continuity features into the spectral features in a patch-wise adaptive manner. These innovations enhance the quality of reconstructed HSI. We perform comprehensive comparison and ablation experiments on the mainstream datasets NTIRE2022 and NTIRE2020 for the spectral reconstruction task. Compared to the current advanced spectral reconstruction algorithms, our designed algorithm achieves State-Of-The-Art (SOTA) performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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ColorDynamic: Generalizable, Scalable, Real-time, End-to-end Local Planner for Unstructured and Dynamic Environments

arXiv

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arXiv 2025年

作者： Xin, Jinghao Liang, Zhichao Zhang, Zihuan Wang, Peng Li, Ning Department of Automation Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Deep Reinforcement Learning (DRL) has demonstrated potential in addressing robotic local planning problems, yet its efficacy remains constrained in highly unstructured and dynamic environments. To address these challenges, this study proposes the ColorDynamic framework. First, an end-to-end DRL formulation is established, which maps raw sensor data directly to control commands, thereby ensuring compatibility with unstructured environments. Under this formulation, a novel network, Transqer, is introduced. The Transqer enables online DRL learning from temporal transitions, substantially enhancing decision-making in dynamic scenarios. To facilitate scalable training of Transqer with diverse data, an efficient simulation platform E-Sparrow, along with a data augmentation technique leveraging symmetric invariance, are developed. Comparative evaluations against state-of-the-art methods, alongside assessments of generalizability, scalability, and real-time performance, were conducted to validate the effectiveness of ColorDynamic. Results indicate that our approach achieves a success rate exceeding 90% while exhibiting real-time capacity (1.2–1.3 ms per planning). Additionally, ablation studies were performed to corroborate the contributions of individual components. Building on this, the OkayPlan-ColorDynamic (OPCD) navigation system is presented, with simulated and real-world experiments demonstrating its superiority and applicability in complex scenarios. The codebase and experimental demonstrations have been open-sourced on our website1 to facilitate reproducibility and further research. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

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