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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： Liu, Wenjun Wang, Hailan Wang, Xiping Hou, Mengshu Chengdu611731 China School of Computer and Software Engineering Xihua University Chengdu610039 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China

Word Sense Disambiguation is a process to determine the best meaning of an ambiguous word according to its contextual semantic information. Many methods of Word Sense Disambiguation cannot deal with polysemous words well because they only consider the meaning of the adjacent words before and after ambiguous words, and cannot consider the meaning of all words in the sentence globally. In order to solve the above problems, this paper proposes a word sense disambiguation method based on Multiple Sense Graph. This method applies the BERT model to generate word sense vectors, and globally considers the feature relationship between the ambiguous word and all words in the context. In addition, this method applies the PageRank algorithm to score the importance of each sense vector of the word, and the scoring results are sorted to obtain the best sense of the ambiguous word. The experimental results indicate that the proposed BERT-PageRank method improves the evaluation index compared with the other two semantic disambiguation methods. In summary, the proposed method improves the accuracy of word sense disambiguation to obtain the best word sense. © 2024 W. Liu, H. Wang, X. Wang & M. Hou.

关键词： BERT Model PageRank Algorithm Word Sense Disambiguation Word Sense Disambiguation Graph

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Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

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Research in Astronomy and Astrophysics 2024年第3期24卷 283-296页

作者： Zi-Yi You Yun-Rong Pan Zhi Ma Li Zhang Shuo Xiao Dan-Dan Zhang Shi-Jun Dang Ru-Shuang Zhao Pei Wang Ai-Jun Dong Jia-Tao Jiang Ji-Bing Leng Wei-An Li Si-Yao Li School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Key Laboratory of Information and Computing Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Big data and Computer Science Guizhou Normal UniversityGuiyang 550025China Guizhou Software Engineering Research Center Guiyang 550000China

Pulsar search is always the basis of pulsar navigation,gravitational wave detection and other research ***,the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope(FAST)shows an explosive growth rate that has brought challenges for its pulsar candidate filtering ***,the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification *** this study,a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented,which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for *** on both High Time Resolution Universe SurveyⅡ(HTRU2)and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars:On HTRU2,the precision and recall rates of its parallel mode reach0.981 and 0.988 *** FAST data,those of its parallel mode reach 0.891 and 0.961,meanwhile,the running time also significantly decreases with the increment of parallel nodes within ***,we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.

关键词： methods data analysis-surveys-methods numerical

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Time-Decay Dynamic Graph Neural Network for Multivariate Time Series Forecasting

Time-Decay Dynamic Graph Neural Network for Multivariate Tim...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yi Luo Baoyi Wang Yutong Luo Ruijuan Yin Yueyang Wang Qingyu Xiong School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Multivariate Time Series (MTS) forecasting is to accurately predict future trends through in-depth analysis of historical time series data, and to provide valuable reference to support for decision-making. Because of its complex time variations, some deep learning methods are applied to model time series, where Graph Neural Networks (GNNs) are included as their powerful capability to model variables’ relationship. However, these methods ignored to either construct the graph structure by considering the dual features of periodic and aperiodic characteristics in time series or dynamically capture the time variations patterns by short- and long-term dependencies. In this paper, we propose a model named Time-Decay Dynamic Graph Neural Network (TDDGNN), which combines period-aware mechanism with a dual-attention time-decay dynamic graph structure. To consider the dual features in time series, Feature Extraction (FE) module is designed to mine the periodic features and aperiodic features. Otherwise, Time-Decay Dual attetion dynamic graph (TDD) module is designed for exploring both short- and long-time intervals. The experimental results show that the TDDGNN model outperforms baseline models on the three datasets. The results of this study will provide a valuable reference scheme for solving the problem of periodic characteristics and correlationship between variables in MTS forecasting.

关键词： Deep learning Time series analysis Decision making Benchmark testing Feature extraction Market research Graph neural networks

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Boundary-Enhanced Network for Semantic Segmentation of Remote Sensing Images 5

Boundary-Enhanced Network for Semantic Segmentation of Remot...

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5th International Conference on Control, Robotics, and Intelligent System, CCRIS 2024

作者： You, Qian Lyu, Xin Li, Chunhua Li, Xin Chen, Shangjing Fang, Yiwei Jiang, Wenxuan College of Computer Science and Software Engineering Hohai University Nanjing211100 China Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing211100 China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

Improving the accuracy of semantic segmentation for remote sensing images (RSIs) is crucial for the geoscientific research and applications. However, existing models tend to focus too much on the subject information for semantic objects, but less on the global-local boundary information of images. To tackle this issue, we introduce a novel approach named the Boundary Enhanced Network (BENet), which applies synchronized attention to both global and local aspects of images. Specifically, the proposed method utilizes the Global-Local Boundary Fusion module (GLBF) to aggregate the boundary information of the objects from a high-dimensional feature perspective. To adapt to the multi-scale characteristics of RSIs, GLBF is embed hierarchically to maintain boundary details across different stages. Besides, a Feature Enhancement Module (FEM) is devised with sequentially DEConv and Squeeze-and-Excitation (SE) blocks to refine the features from decoder. The experiments on the ISPRS Vaihingen and Potsdam datasets showed that BENet outperforms mainstream methodology, OA, mIoU and mF1 metrics were all significantly elevated. © 2024 SPIE.

关键词： Semantic Segmentation

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CIFD: A Distance for Complex Intuitionistic Fuzzy Set 4th

CIFD: A Distance for Complex Intuitionistic Fuzzy Set

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zhao, Yangyang Xiao, Fuyuan School of Computer and Information Science Southwest University Chongqing400715 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China

ISBN: (纸本)9783031200953

Intuitionistic fuzzy set (IFS) has attracted much attention because it can deal with fuzziness and uncertainty more flexibly than traditional fuzzy set. Complex intuitionistic fuzzy set (CIFS) extends intuitionistic fuzzy to the complex plane, which can better express and process uncertain information. In this paper, a novel distance measure complex intuitionistic fuzzy distance (CIFD) is proposed for CIFSs. Firstly, inspired by Tanimoto coefficient, a new similarity measure between CIFSs is proposed. Then, based on the similarity measure, the CIFD is proposed and its non-negativity, non-degeneracy, and symmetry are analyzed. Moreover, when CIFS degenerates into classical IFS, CIFD is also applicable to measure the differences between IFSs. Finally, to illustrate the effectiveness of CIFD, an example is given at the end. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy sets

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A Comparative Study on Node Classification Methods For Undirected Social Networks

A Comparative Study on Node Classification Methods For Undir...

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Ay, Tolga Gulcu, Ayla Bahçeşehir University Big Data Analytics and Management İstanbul Turkey Bahçeşehir University Software Engineering Dept İstanbul Turkey

ISBN: (纸本)9798350306590

Traditional Neural Networks can solve problems with normal 1-dimensional and 2-dimensional Euclidean data such as image and text classification. However, most real-life problems are relationship-based and the corresponding real-world data has a non-Euclidean nature. This paper presents a comparative study between Graph Neural Networks (GNNs) and traditional approaches (K-Nearest Neighbour(KNN)) for solving real-life problems involving non-Euclidean data with complex relationships. The results demonstrate that GNN models outperform KNN in terms of accuracy while maintaining low runtime on graph structured data. The study highlights the strengths and limitations of Graph Convolutional Networks (GCNs) and GraphSAGE, with GraphSAGE offering flexibility and scalability but potentially introducing aggregation bias. Ongoing experiments include investigating the sensitivity of the algorithms to hyperparameter values. Overall, this research contributes to the understanding and practical application of GNN methodologies in various domains. © 2023 IEEE.

关键词： Classification (of information)

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Cellular Network Optimization Using Unfolding-Based Graph Neural Networks 24

Cellular Network Optimization Using Unfolding-Based Graph Ne...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Software Engineering Shanghai China Peng Cheng Laboratory Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9781665496261

The rapid development of wireless communication networks has driven the need for careful optimization of network parameters to achieve optimal performance. Traditional optimization approaches, however, face challenges in managing a vast number of tunable parameters and scalability across different scenarios. In this paper, we propose a novel bipartite graph neural network (Bi-GNN) approach for scalable cellular network optimization, focusing on network parameters including the azimuth angle and tilt angle of the antenna array, as well as the transmission power of the base station (BS). Our approach leverages the bipartite structure of cellular network topology and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, offering performance guarantees while maintaining a low computational overhead. Numerical results demonstrate that the Bi-GNN approach not only achieves a higher coverage ratio, but also significantly reduces the computational cost compared to existing methods. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. The proposed Bi-GNN approach has the potential to revolutionize cellular network optimization, paving the way for more efficient and scalable network solutions. © 2023 IEEE.

关键词： Optimization

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ZO-DARTS: Differentiable Architecture Search with Zeroth-Order Approximation 48

ZO-DARTS: Differentiable Architecture Search with Zeroth-Ord...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xie, Lunchen Huang, Kaiyu Xu, Fan Shi, Qingjiang Tongji University School of Software Engineering Shanghai China Peng Cheng Laboratory Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9781728163277

Neural Architecture Search (NAS) is a silver bullet in alleviating time consumption and human effort for deep neural network design. It is however challenging to search for good architectures with low consumption. In this paper, we propose a novel NAS framework to address the differentiable neural architecture search problem by inspecting the bi-level problem formulation from scratch. Combined with the Zeroth-Order (ZO) gradient descent technique and implicit gradients, the proposed algorithm can not only reduce search time for suitable architectures than existing works but maintain the final accuracy simultaneously. Experimental results show the efficacy of our proposed ZO-based NAS approach. © 2023 IEEE.

关键词： Deep neural networks

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Research on Improving the Quality of Life of Students Using Machine Learning and Developing a Digital Health Profile 12

Research on Improving the Quality of Life of Students Using ...

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12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Sarsembayeva, Talshyn Mansurova, Madina Sarsembayev, Magzhan Ibrayeva, Anar Al-Farabi Kazakh National University Department of Artificial Intelligence & Big Data Almaty Kazakhstan International Engineering Technological University Department of Software Engineering Almaty Kazakhstan Institute of Endocrinology Department of Endocrinology Almaty Kazakhstan

ISBN: (纸本)9798350350982

In modern society, the health of students is one of the priorities, which is receiving more and more attention. Deviations or any negative changes in health status can negatively affect their academic performance, as well as their quality of life. Therefore, the development of an information and analytical system for monitoring the health of students is a hot topic not only in Kazakhstan. The article presents research as a component for solving this problem. Today, society is paying more and more attention to caring for the health of students, realizing that their well-being is directly reflected in academic and personal indicators. This article describes an approach to creating an information and analytical system based on machine learning and data analysis methods to assess and identify key factors affecting the quality of life of students. © 2024 IEEE.

关键词： Electronic health record

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Dual Interaction and Kernel-Diverse Network for Accurate Drug-Target Binding Affinity Prediction

Dual Interaction and Kernel-Diverse Network for Accurate Dru...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wu, Yulong Xie, Jin Nie, Jing Hu, Jian Zeng, Yuansong Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University School of School of Microelectronics and Communication Engineering Chongqing400044 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

ISBN: (纸本)9798350386226

Drug-target binding affinities measure the binding strength between drugs and targets, serving as the foundation for computer-aided drug design. The existing methods based on neural network rarely incorporate knowledge from biochemistry, which limits their performance. In this paper, we propose a dual interaction and kernel-diverse network for drug-target binding affinity prediction, termed DTANet, drawing inspiration from the biochemical properties of drugs and targets. Specifically, since functional groups and peptide chains are pivotal in determining the properties of drugs and their targets, and their lengths vary significantly. Drawing inspiration from this, we propose a Kernel-diverse Two-stream Network (KTN) and a Cross-Scale Interaction Module (CSIM) to extract the features of drugs and targets. The binding site of drug and target is crucial for precise affinity prediction. Inspired by this, we introduce the Drug-Target Interaction Module (DTIM) to explore the relationships between drugs and targets to focus on binding sites. Experiments have been conducted on two popular benchmarks: KIBA and Davis. Our DTANet obtains superior performance compared to existing methods on both datasets. Source codes are available at https://***/202324131016T/DTANet. © 2024 IEEE.

关键词： Convolutional neural networks

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