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Rock Mechanics Bulletin 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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ST-Petri: A Visual Executable Semantic Model for PLC Structured Text Language 22

ST-Petri: A Visual Executable Semantic Model for PLC Structu...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Hu, Xiaoyu Liang, Yonghui Zhu, Shibo Li, Hui Zhu, Shanying Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

ISBN: (纸本)9798331527471

As very important controllers in automated factories, the correctness and safety of Programmable Logic Controller (PLC) and their programs determine the stability of the production process. Structured Text (ST) is one of the most widely used PLC programming languages. However, there are not enough formal semantics and different venders may have their own implementations. In this work, a visual executable semantic model ST-Petri for ST has been proposed. The model uses Petri nets to formally represent ST semantics while using graphs to visualise program control flow. To validate the correctness of the semantic model, we use programs from Github and mutations of existing source programs as a test set to execute and compare the results with the open source OpenPLC platform. Experimental results show that ST-Petri has significant advantages in terms of compilation pass rate and error indication as a semantic model and a ST program compiler. © 2024 IEEE.

关键词： Petri nets

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Three-Channel Graph Convolutional Network 24

Three-Channel Graph Convolutional Network

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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： Meng, Lei Ye, Zhonglin Zhao, Haixing Yang, Yanlin Chen, Yang Wang, Zhaoyang College of Computer Qinghai Normal University Tibetan Information Processing Engineering Technology and Research Center of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China

ISBN: (纸本)9798350319934

At present, as an emerging direction of artificial intelligence, graph neural networks (GNNs) have developed rapidly and can solve many graph data problems efficiently. The real world is composed of a large number of networks. Compared with the widely studied single-relationship feature information networks, the real-world networks are mostly composed of multi-relationship feature information networks in which different types of objects are interconnected through complex relationships. Complex structural information and rich semantic information are included by multi-relationship feature information networks, thereby the real network can be restored more realistically. However, one type of relational feature of the network is merely considered by most of the existing GNNs, and the information of multi-relationship features and the hidden information between multi-relationship features are ignored in the network. In order to deal with these restrictions, a novel graph convolutional network (TCGCN) based on an attention mechanism is proposed, which aims to extract different relational features in multi-relationship feature networks and the hidden information between them more comprehensively, thereby improving the performance of GNNs in downstream tasks. In this paper, an improved attention mechanism is used to aggregate different types of single relational features, and the aggregated multiple single relational feature network information is fused with the mixed relational feature network information into the final node representation. The proposed TCGCN outperforms most other methods in extensive experiments conducted on three real-world datasets. © 2022 IEEE.

关键词： Graph neural networks

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Optimal respiratory waveform selection based on range-multiple beams using a MIMO radar 13

Optimal respiratory waveform selection based on range-multip...

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13th International Conference on Signal processing Systems, ICSPS 2021

作者： Wu, Wanhua Yang, Zhaocheng Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

In this paper, we propose an optimal respiratory waveform selection algorithm based on range-multiple beams using a 77GHz frequency modulated continuous wave (FMCW) multiple-input-multiple-output (MIMO) radar. Generally speaking, the human chest is a multi-scattering target and the optimal monitoring position changes in a small range during breathing movement. According to this motion feature, we roughly locate the target in the range-angle candidate box based on range fast Fourier transform (FFT) and Capon direction of arrival (DOA) algorithm, respectively. Additionally, the fixed beamforming is utilized to algin the detected target site which can reduce the interference of clutter and enhance the signal-to-noise ratio (SNR). Then, the extended differential and cross-multiply (DACM) algorithm is further applied for phase unwrapping and the optimal respiratory waveform is extracted based on the features of respiratory periodicity. Ultimately, the respiratory rate is estimated by the frequency-time phase regression (FTPR) algorithm. Experiments with and without interference are conducted and the results show that the proposed algorithm can obtain accurate respiratory rate with mean square errors (MSE) 0.6862 breath2/min compared with the reference vital signs data. © COPYRIGHT SPIE.

关键词： Beamforming

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A Fuzzy Creative Works Generation Algorithm Based on Graph Neural Network 6th

A Fuzzy Creative Works Generation Algorithm Based on Graph N...

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6th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2019

作者： Zhang, Fuquan Wang, Yiou Zhao, Guifen Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Beijing Institute of Science and Technology Information Beijing100044 China

ISBN: (纸本)9789811650352

A fuzzy creative works generation algorithm based on graph neural network is proposed. Firstly, the multi-label fuzzy creative data set is constructed. Secondly, fuzzy logical correlations between creative objects are dynamically calculated by graphical neural networks to capture relevant digital objects and their relationships. Thirdly, the projectiles, boundary markers and location words of the creative scene objects are generated by analyzing related attributes of each entity based on graph neural network. Fuzzy creative works are automatically generated by adjusting the appropriate location. Finally, the experiments are deployed, and experimental results show that the accuracy rate of the proposed algorithm is improved by 9.6% on average compared with the manual selection, which indicates the feasibility of the proposed algorithm replacing the manual selection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy neural networks

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A Survey on Audio-Driven Talking Face Generation

A Survey on Audio-Driven Talking Face Generation

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Artificial Intelligence, Networking and information Technology (AINIT), International Seminar on

作者： Xue Bai Xiangzhen He Mengdi Ma Xiang Wang Wenhao Jiang Tingting Du Zijian Huang Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Key Laboratory of Linguistic and Cultural Computing Ministry of Education Gansu Province (Northwest Minzu University) Lanzhou China

ISBN: (数字)9798331522285

ISBN: (纸本)9798331522292

In recent years, advancements in deep learning have driven the development of audio-driven talking face generation. This technology has widespread applications in virtual avatars, intelligent assistants, film and television special effects, remote education, digital human live streaming, and more. Audio-driven talking face generation methods synthesize synchronized talking videos based on input speech signals, ensuring that the mouth movements, facial expressions, and head poses of virtual characters naturally align with the speech content. This paper provides an overview of the major research progress in this field and analyzes the advantages and limitations of different techniques. Additionally, it introduces evaluation metrics used to assess model performance in detail. Finally, the paper summarizes current challenges and potential future research directions.

关键词： Surveys Seminars Measurement TV Education Mouth Synchronization information technology Faces Videos

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Soft-Labeled Contrastive Pre-training for Function-level Code Representation

Soft-Labeled Contrastive Pre-training for Function-level Cod...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Li, Xiaonan Guo, Daya Gong, Yeyun Lin, Yun Shen, Yelong Qiu, Xipeng Jiang, Daxin Chen, Weizhu Duan, Nan Shanghai Key Laboratory of Intelligent Information Processing Fudan University China School of Computer Science Fudan University China Microsoft National University of Singapore Singapore

Code contrastive pre-training has recently achieved significant progress on code-related tasks. In this paper, we present SCodeR, a Soft-labeled contrastive pre-training framework with two positive sample construction methods to learn functional-level Code Representation. Considering the relevance between codes in a large-scale code corpus, the soft-labeled contrastive pre-training can obtain fine-grained soft-labels through an iterative adversarial manner and use them to learn better code representation. The positive sample construction is another key for contrastive pre-training. Previous works use transformation-based methods like variable renaming to generate semantically equal positive codes. However, they usually result in the generated code with a highly similar surface form, and thus mislead the model to focus on superficial code structure instead of code semantics. To encourage SCodeR to capture semantic information from the code, we utilize code comments and abstract syntax sub-trees of the code to build positive samples. We conduct experiments on four code-related tasks over seven datasets. Extensive experimental results show that SCodeR achieves new state-of-the-art performance on all of them, which illustrates the effectiveness of the proposed pretraining method. © 2022 Association for Computational Linguistics.

关键词： Iterative methods

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Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs

Unveiling and Consulting Core Experts in Retrieval-Augmented...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Zhou, Xin Nie, Ping Guo, Yiwen Wei, Haojie Zhang, Zhanqiu Minervini, Pasquale Ma, Ruotian Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University Shanghai China LightSpeed Studios Tencent China Institute of Modern Languages and Linguistics Fudan University Shanghai China Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Informatics and ELLIS University of Edinburgh United Kingdom

ISBN: (纸本)9798891761643

Retrieval-Augmented Generation (RAG) significantly improved the ability of Large Language Models (LLMs) to solve knowledge-intensive tasks. While existing research seeks to enhance RAG performance by retrieving higher-quality documents or designing RAG-specific LLMs, the internal mechanisms within LLMs that contribute to the effectiveness of RAG systems remain underexplored. In this paper, we aim to investigate these internal mechanisms within the popular Mixture-of-Expert (MoE)based LLMs and demonstrate how to improve RAG by examining expert activations in these LLMs. Our controlled experiments reveal that several core groups of experts are primarily responsible for RAG-related behaviors. The activation of these core experts can signify the model's inclination towards external/internal knowledge and adjust its behavior. For instance, we identify core experts that can (1) indicate the sufficiency of the model's internal knowledge, (2) assess the quality of retrieved documents, and (3) enhance the model's ability to utilize context. Based on these findings, we propose several strategies to enhance RAG's efficiency and effectiveness through expert activation. Experimental results across various datasets and MoE-based LLMs show the effectiveness of our method. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2504-2514页

作者： Ma, Chenglong Li, Zilong Li, Yuanlin Han, Jing Zhang, Junping Zhang, Yi Liu, Jiannan Shan, Hongming Fudan University Institute of Science and Technology for Brain-inspired Intelligence Moe Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai200433 China Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Shanghai200433 China Shanghai Ninth People's Hospital Department of Oral Maxillofacial Head and Neck Oncology China Shanghai Jiao Tong University School of Medicine College of Stomatology China Shanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Shanghai200011 China Sichuan University School of Cyber Science and Engineering Chengdu Sichuan610065 China

Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel radiologist-in-the-loop self-training framework for MAR, termed RISE-MAR, which can integrate radiologists' feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo groundtruths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. © 2025 IEEE.

关键词： Computerized tomography

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Anonymous Verifiable Sealed Quotation Auction Based on Blockchain

Anonymous Verifiable Sealed Quotation Auction Based on Block...

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作者： Ke Wei Yulong Dang The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University

With the development of blockchain technology, the existing online auction technology is developing from the trusted third party as auction intermediary to the disintermediation to the third party. Aiming at the problems existing in the existing sealed bid auction scheme based on blockchain, such as the leakage of bidders' privacy information, illegal acquisition or tampering of bidders' bids in sealed auction, etc. Based on blockchain as the bottom layer, an auction scheme with decentralization, bid privacy,anonymous verifiability, tamper-proof and collusion-proof is constructed. ASVChain uses encryption mechanism to encrypt each bidder's offer to ensure the privacy of the bid,and uses ring signature technology to sign the encrypted offer, providing safe and verifiable steps while ensuring that each bidder's personal information is not leaked, and preventing illegal tampering while resisting collusion. The security analysis shows the advantages of this scheme compared with the existing scheme. The experiment analyzes and tests the influence of the number of bidders on the bidding time, and the results show that the time consumption of the proposed auction scheme meets the daily auction demand.

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