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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

Neural computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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A Structured Meshing Approach for FDTD Method 7

A Structured Meshing Approach for FDTD Method

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Zhong, Bo Feng, Jian Fang, Ming Huang, Zhixiang Anhui University The Information Materials and Intelligent Sensing Laboratory of Anhui Province Center for Big Data and Population Health of IHM China

ISBN: (纸本)9798350384437

The Finite Difference Time Domain (FDTD) algorithm has found extensive applications in computational electromagnetics due to its simplicity, low computational cost, and divergence-free properties. However, the Yee grid-based FDTD algorithm may introduce inaccuracies when modeling irregular objects. Hence, this paper proposes a novel FDTD method based on two-dimensional triangular grids to address certain spatial modeling challenges. In the triangular FDTD algorithm, the spatial domain is initially discretized using triangular grids. Subsequently, starting from Maxwell's equations, updated equations for the electromagnetic and magnetic fields within the triangular grid are formulated. Concurrently, periodic boundary conditions for hexagonal simulation regions are established, and the spatial propagation patterns of the magnetic field are monitored. A comparative analysis is conducted between the temporal propagation patterns of Gaussian sources in triangular grids and Yee grids. Furthermore, a comparison is made regarding the resonant frequencies excited by Gaussian sources within Perfect Electric Conductor (PEC) boundary conditions to validate the feasibility of the triangular grid FDTD method. © 2024 IEEE.

关键词： Maxwell equations

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Influencing Factors of healthy Aging Risk Assessed Using Biomarkers:A Life Course Perspective

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China CDC weekly 2024年第11期6卷 219-224,I0014-I0017页

作者： Cedric Zhang Bo Lua Yajie Gao Jinming Li Xingqi Cao Xinwei Lyu Yinuo Tu Shuyi Jin Zuyun Liu Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Healththe Key Laboratory of Intelligent Preventive Medicine of Zhejiang ProvinceZhejiang University School of MedicineHangzhou CityZhejiang ProvinceChina Institute of Epidemiology and Health Care University College LondonLondonUK College of Chemical and Biological Engineering Zhejiang UniversityHangzhou CityZhejiang ProvinceChina

Assessing individual risks of healthy aging using biomarkers and identifying associated factors have become important areas of *** this study,we conducted a literature review of relevant publications between 2018 and 2023 in both Chinese and English *** studies have predominantly used single biomarkers,such as C-reactive protein,or focused on specific life course stages and factors such as socioeconomic status,mental health,educational levels,and unhealthy *** summarizing the progress in this field,our study provides valuable insights and future directions for promoting healthy aging from a life course perspective.

关键词： such insight directions

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AlphaFold2 and its applications in the fields of biology and medicine

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Signal Transduction and Targeted Therapy 2023年第4期8卷 1630-1643页

作者： Zhenyu Yang Xiaoxi Zeng Yi Zhao Runsheng Chen West China Biomedical Big Data Center West China HospitalSichuan UniversityChengdu 610041China Key Laboratory of Intelligent Information Processing Advanced Computer Research CenterInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Key Laboratory of RNA Biology Center for Big Data Research in HealthInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China Pingshan Translational Medicine Center Shenzhen Bay LaboratoryShenzhen 518118China

AlphaFold2(AF2)is an artificial intelligence(AI)system developed by DeepMind that can predict three-dimensional(3D)structures of proteins from amino acid sequences with atomic-level *** structure prediction is one of the most challenging problems in computational biology and chemistry,and has puzzled scientists for 50 *** advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much *** release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community,especially in the fields of biology and ***2 is thought to have a significant impact on structural biology and research areas that need protein structure information,such as drug discovery,protein design,prediction of protein function,et *** the time is not long since AF2 was developed,there are already quite a few application studies of AF2 in the fields of biology and medicine,with many of them having preliminarily proved the potential of *** better understand AF2 and promote its applications,we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success,and particularly focus on reviewing its applications in the fields of biology and *** of current AF2 prediction will also be discussed.

关键词： system fields Fold

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Synergetic Focal Loss for Imbalanced Classification in Federated XGBoost

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 647-660页

作者： Tian, Jiao Tsai, Pei-Wei Zhang, Kai Cai, Xinyi Xiao, Hongwang Yu, Ke Zhao, Wenyu Chen, Jinjun Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia Center for Health Computing Research Beijing Academy of Artificial Intelligence Beijing100084 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China

Applying sparsity- and overfitting-aware eXtreme Gradient Boosting (XGBoost) for classification in federated learning allows many participants to train a series of trees collaboratively. Since various local multiclass distributions and global aggregation diversity, model performance plummets as convergence slowly and accuracy decreases. Worse still, neither the participants nor the server can detect this problem and make timely adjustments. In this article, we provide a new local-global class imbalance inconsistency quantification and utilize softmax as the activation and focal loss, a dynamically scaled cross-entropy loss, in federated XGBoost to mitigate local class imbalance. Moreover, we propose a simple but effective hyperparameter determination strategy based on local data distribution to adjust the sample weights among noncommunicating participants, synergetic focal loss, to solve the inconsistency of local and global class imbalance, a unique characteristic of federated learning. This strategy is perfectly integrated into the original classification algorithm. It requires no additional detectors or information transmission. Furthermore, a dynamical for loop is designed to capture an optimum hyperparameter combination. Finally, we conduct comprehensive tabular- and image-based experiments to show that synergetic focal loss used in federated XGBoost achieves faster convergency and significant accuracy improvement. Simulation results prove the effectiveness of the proposed principle of configuring sample weights. © 2023 IEEE.

关键词： Heuristic algorithms

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IGED:Towards intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Task Separation and Knowledge Sharing for Class Incremental Learning 5

Task Separation and Knowledge Sharing for Class Incremental ...

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5th International Conference on intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

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IF-Font: Ideographic Description Sequence-Following Font Generation 38

IF-Font: Ideographic Description Sequence-Following Font Gen...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xinping Ke, Xiao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Few-shot font generation (FFG) aims to learn the target style from a limited number of reference glyphs and generate the remaining glyphs in the target font. Previous works focus on disentangling the content and style features of glyphs, combining the content features of the source glyph with the style features of the reference glyph to generate new glyphs. However, the disentanglement is challenging due to the complexity of glyphs, often resulting in glyphs that are influenced by the style of the source glyph and prone to artifacts. We propose IF-Font, a novel paradigm which incorporates Ideographic Description Sequence (IDS) instead of the source glyph to control the semantics of generated glyphs. To achieve this, we quantize the reference glyphs into tokens, and model the token distribution of target glyphs using corresponding IDS and reference tokens. The proposed method excels in synthesizing glyphs with neat and correct strokes, and enables the creation of new glyphs based on provided IDS. Extensive experiments demonstrate that our method greatly outperforms state-of-the-art methods in both one-shot and few-shot settings, particularly when the target styles differ significantly from the training font styles. The code is available at https://***/Stareven233/IF-Font. © 2024 Neural information processing systems foundation. All rights reserved.

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Image Denoising Method Based on Channel Compression and Split-Attention Mechanism 5

Image Denoising Method Based on Channel Compression and Spli...

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5th International Conference on Computer Engineering and intelligent Control, ICCEIC 2024

作者： Sun, Hao Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Shandong Yantai China

ISBN: (纸本)9798331507992

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel Compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks. © 2024 IEEE.

关键词： Restoration

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Research on Blockchain-based Decentralized Federated Learning

Research on Blockchain-based Decentralized Federated Learnin...

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2023 International Conference on Computer Applications Technology, CCAT 2023

作者： Han, Boyuan Liang, Zhihong Qin, Mingming Jiang, Rongxin Dai, Wei Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Southwest Forestry University Big Data and Cloud Computing Center Kunming China Yunnan Pu'er Technical School Yunnan Pu'er Technical School Kunming China

ISBN: (纸本)9798350301540

The integration of blockchain and federated learning exhibits a highly synergistic relationship. Blockchain, with its assurance of trustworthiness in computational processes and data, combined with federated learning's ability to make data available yet invisible, mutually complement each other and have shown initial advancements across various domains. Consequently, research and analysis of blockchain-based federated learning technology have garnered considerable *** blockchain enhances the federated learning process, it also poses a number of problems, such as increased communication overhead and lack of computational *** article aims to provide a comprehensive examination of the subject. Firstly, it introduces the fundamental concept of federated learning technology, along with privacy protection techniques and the existing threat landscape. Secondly, it analyzes the core principles of blockchain-based federated learning technology, delving into current research status, progress, and outlining the various research directions involved. Furthermore, it conducts an in-depth analysis and summarization of the integration between federated learning and blockchain technology, identifying numerous challenges that need to be addressed within the relevant domains. Finally, it recognizes and analyzes the future development of this combination, emphasizing the need to overcome existing *** summary, this article highlights the amalgamation of federated learning and blockchain technology. It identifies and analyzes the many challenges that require resolution within the pertinent fields, while also offering insights into the future development of this integration. © 2023 IEEE.

关键词： Blockchain

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