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Efficient and Trustworthy Block Propagation for Blockchain-enabled Mobile Embodied AI Networks: A Graph Resfusion Approach

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arXiv 2025年

作者： Kang, Jiawen Liao, Jiana Gao, Runquan Wen, Jinbo Huang, Huawei Zhang, Maomao Yi, Changyan Zhang, Tao Niyato, Dusit Zheng, Zibin Anhui Engineering Research Center for Agricultural Product Quality Safety Digital Intelligence Fuyang Normal University Fuyang236037 China School of Automation Guangdong University of Technology Guangzhou510006 China School of Software Engineering Sun Yat-Sen University Zhuhai519082 China School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen518107 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China College of Computing and Data Science Nanyang Technological University Singapore

By synergistically integrating mobile networks and embodied artificial intelligence (AI), Mobile Embodied AI Networks (MEANETs) represent an advanced paradigm that facilitates autonomous, context-aware, and interactive behaviors within dynamic environments. Nevertheless, the rapid development of MEANETs is accompanied by challenges in trustworthiness and operational efficiency. Fortunately, blockchain technology, with its decentralized and immutable characteristics, offers promising solutions for MEANETs. However, existing block propagation mechanisms suffer from challenges such as low propagation efficiency and weak security for block propagation, which results in delayed transmission of vehicular messages or vulnerability to malicious tampering, potentially causing severe traffic accidents in blockchain-enabled MEANETs. Moreover, current block propagation strategies cannot effectively adapt to real-time changes of dynamic topology in MEANETs. Therefore, in this paper, we propose a graph Resfusion model-based trustworthy block propagation optimization framework for consortium blockchain-enabled MEANETs. Specifically, we propose an innovative trust calculation mechanism based on the trust cloud model, which comprehensively accounts for randomness and fuzziness in the miner trust evaluation. Furthermore, by leveraging the strengths of graph neural networks and diffusion models, we develop a graph Resfusion model to effectively and adaptively generate the optimal block propagation trajectory. Simulation results demonstrate that the proposed model outperforms other routing mechanisms in terms of block propagation efficiency and trustworthiness. Additionally, the results highlight its strong adaptability to dynamic environments, making it particularly suitable for rapidly changing MEANETs. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching Transformer

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arXiv 2025年

作者： Chen, Ziyang Zhang, Yongjun Li, Wenting Wang, Bingshu Wu, Yabo Zhao, Yong Chen, C.L. Philip College of Computer Science The State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Information Engineering Guizhou University of Commerce Guiyang550021 China School of Software Northwestern Polytechnical University Xi’an710129 China Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University Shenzhen518055 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

In light of the advancements in transformer technology, extant research posits the construction of stereo transformers as a potential solution to the binocular stereo matching challenge. However, constrained by the low-rank bottleneck and quadratic complexity of attention mechanisms, stereo transformers still fail to demonstrate sufficient nonlinear expressiveness within a reasonable inference time. The lack of focus on key homonymous points renders the representations of such methods vulnerable to challenging conditions, including reflections and weak textures. Furthermore, a slow computing speed is not conducive to the application. To overcome these difficulties, we present the Hadamard Attention Recurrent Stereo Transformer (HART) that incorporates the following components: 1) For faster inference, we present a Hadamard product paradigm for the attention mechanism, achieving linear computational complexity. 2) We designed a Dense Attention Kernel (DAK) to amplify the differences between relevant and irrelevant feature responses. This allows HART to focus on important details. DAK also converts zero elements to non-zero elements to mitigate the reduced expressiveness caused by the low-rank bottleneck. 3) To compensate for the spatial and channel interaction missing in the Hadamard product, we propose MKOI to capture both global and local information through the interleaving of large and small kernel convolutions. Experimental results demonstrate the effectiveness of our HART. In reflective area, HART ranked 1st on the KITTI 2012 benchmark among all published methods at the time of submission. Code is available at https://***/ ZYangChen/HART. Copyright © 2025, The Authors. All rights reserved.

关键词： Hadamard transforms

Four Diagnostic Description Extraction in Clinical Records of Traditional Chinese Medicine with Batch data Oversampling 21

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Four Diagnostic Description Extraction in Clinical Records o...

21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： Wang, Yaqiang Li, Kailun Jiang, Yongguang Shu, Hongping College of Software Engineering Chengdu University of Information Technology China Institute for Data Science and Engineering Chengdu University of Information Technology China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service China Department of Preclinical Medicine Chengdu University of Traditional Chinese Medicine China

Four diagnostic description extraction in clinical records of traditional Chinese medicine (TCM) has important application value in improving the quality and efficiency of TCM clinical syndrome differentiation and treatment. However, the extraction task is yet to be explored, and imbalanced class distribution is one of the key challenges of this task. As a first exploration of this task, we firstly constructed a TCM clinical four diagnostic description extraction corpus and then solved the domain adaptation by fine-tuning the general domain pre-trained language model based on unlabeled TCM clinical records. At last, we trained our proposed four diagnostic description extraction model by utilizing a small labeled dataset through a well-designed batch data oversampling algorithm. The experimental results show that the performance of the proposed method in this paper is better than that of the compared methods, and the proposed method improves the extraction performance F1 score of the rare class by 2.13% on average. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Extraction

Influence of Lung Cancer Model Characteristics on Nanodrugs Tumor Targeting Behavior Based on Convolutional Kernel Extreme Learning Machine Model

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Influence of Lung Cancer Model Characteristics on Nanodrugs ...

Advancement in Technology (ICONAT), International Conference for

作者： Manthan S. Manavadaria Srikanth Chintakindi Rajasree Rajamohanan S Krishna Prasad P. Janardhan Saikumar Neerav Nishant EC Department CSPIT CHARUSAT Changa Petlad Gujarat India Associate Project Manager Cloud Software Group Department of Computer Science (Artificial intelligence and Data Science) Karpagam Academy of Higher Education Coimbatore India Department of Mechanical Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte Udupi India Department of ECE Audisankara College of Engineering and Technology Gudur India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

One of the main killers on a worldwide basis is cancer. In terms of global persistence, lung cancer stands out among the most frequent cancers. For lung cancer, the standard medical approach includes chemotherapy, surgical removal, and radiation therapy. When compared to other methods, this one isn’t extremely targeted and may even damage surrounding healthy cells. There has been recent recognition of nanotechnology as a possible tool for the treatment and management of lung cancer. Preprocessing, feature selection, segmentation, and model training must be executed in this precise sequence to ensure accuracy. As part of the preprocessing, the proposed approach employ the kernel correlation approach. Splitting the input image into smaller pieces with the goal of reducing the overall representation of the image is called image segmentation. To find features with high predictive power and remove redundant ones, feature selection was done in the training cohort. The model was trained using a Convolutional-KELM. As compared to CNN and ELM, the suggested approach performs better. Following implementation of the technique, the accuracy increased by 94.25%.

关键词： Training Image segmentation Accuracy Lungs Lung cancer Feature extraction Convolutional neural networks Kernel Medical diagnostic imaging Tumors

Enhancing Dental Caries Identification with Deep Learning: A Study of Convolutional Neural Networks and Transfer Learning Approaches

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SSRN

SSRN 2024年

作者： Mannan, Fayqa Adeel, Muhammad Arif, Erssa Li, Jianqiang Ullah, Amin Nawazish, Samra Mahmood, Tariq Rehman, Amjad Shoukat, Ijaz Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Government College University Faisalabad38000 Pakistan Department of Computer Science School of Science National Textile University Faisalabad37610 Pakistan Department of Computer Science Faculty of Computing Riphah International University Faisalabad38000 Pakistan Beijing Engineering Research Center for IoT Software and Systems 100124 China Department of Computer Science Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan School of Computer Science and Engineering Southeast University Nanjing China Software Engineering Department University of Engineering and Technology Taxila Pakistan Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia Faculty of information Sciences University of Education Vehari Campus Vehari61100 Pakistan

Various deep learning techniques have been employed to diagnose dental caries using X-ray images. In this study, we utilized deep learning models, including Convolutional Neural Networks (CNNs) and transfer learning models such as Visual Geometry Group (VGG16), VGG19, ResNet50, and Inception V3, to identify dental caries in periapical radiographs. The traditional CNN model comprises three sets of 2D convolutional layers, followed by an activation layer, a max-pooling layer, a flatten layer, one dense layer, another activation layer, dropout, another dense layer, and a final activation layer. For the VGG models, ResNet50, and Inception V3, the top convolutional layers were frozen to prevent retraining of the model, with only the last layers trained to produce the required output. The accuracy achieved by CNN, VGG16, VGG19, ResNet50, and Inception V3 was 90%, 96%, 73%, 70%, and 73%, respectively. VGG16 exhibited the highest accuracy among the models. Hyperparameters were carefully selected using a grid search approach, and the results were validated using the Shuffle-Split-Cross (SSC) validation method. These findings underscore the effectiveness of transfer learning in analyzing medical images, particularly in the diagnosis of dental caries. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

Document-level Relation Extraction with Relation Correlations

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arXiv 2022年

作者： Han, Ridong Peng, Tao Wang, Benyou Liu, Lu Wan, Xiang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China College of Software Jilin University China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China

Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into DocRE task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular DocRE datasets are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

Extracting and Classifying 3D Medical Image Texture Characteristics using GLCM and LBP-Based Techniques

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Extracting and Classifying 3D Medical Image Texture Characte...

data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Srinath R. Sharmila N. Raghavendran M. C. Babu K. Ramanan R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Date Science RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Recent advances in 3D sensor technology have advanced 3D imaging systems. This sparks interest in 3D picture feature extraction and categorization. According to the literature, textural content is essential to visuals. In this case, we extract texture characteristics from three-dimensional photos to improve feature differentiation. Category of volumetric data with texture is possible using this method. We employ feature vectors from Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix methods to achieve this. The visual pattern and volumetric data contrast, homogeneity, and local anisotropy are described. The proposed approach was tested using a publicly accessible dataset of modified volumetric textured pictures. Generative Adversarial Networks (GAN) and recurrent Neural Networks (RNN) are classified. Our solution outperforms manually constructed techniques for extracting texture characteristics from 3D and 2D data and standard deep-learning networks. Even with few photographs per class, the recommended method improves class distinction and yields good results. Show exceptional accuracy. Integrating the Gray Level Co-occurrence Matrix (GLCM) with Local Binary Patterns (LBP) improved classification performance. The GLCM approach, which captures spatial relationships, and the LBP method, which depicts local texture, was advantageous. This hybrid method improved feature extraction and classification, notably for complex 3D medical pictures with subtle texture changes.

关键词： Deep learning Visualization Three-dimensional displays Machine learning algorithms Recurrent neural networks Liver Feature extraction Generative adversarial networks Classification algorithms Tumors

KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

Towards Seamless Connectivity: Implementing 6G Communication Technologies In Next-Generation Networks

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Towards Seamless Connectivity: Implementing 6G Communication...

Advancement in Technology (ICONAT), International Conference for

作者： K. Kalaiselvi R. Sankar S Supriya G Kaushik Hemanth Swamy M Devika Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of Electrical and Electronics Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Senior Software Engineer Motorola Solutions Department of AI& DS Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). Despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can’t meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, AI-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues.

关键词： 6G mobile communication 5G mobile communication Wireless networks Complex networks Market research Communications technology Internet of Things Optimization Next generation networking Edge computing