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7th International Conference on Computer Information Science and Application Technology, CISAT 2024

作者： Su, Ruilin Zhang, Wanshan Yu, Dunhui Hubei University Wuhan China Wuhan430062 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China Hubei University Hubei Key Laboratory of Big Data Intelligent Analysis and Application Wuhan430062 China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan430062 China

ISBN: (纸本)9798350375107

Multi-hop Knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. Aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model's memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model's own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing. © 2024 IEEE.

关键词： Iterative decoding

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Steel Surface Defect Detection Based on Improved YOLOv8 20

Steel Surface Defect Detection Based on Improved YOLOv8

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Zhang, Jichen Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

There are some defects on the surface of steel that are difficult to detect, so we propose an improved algorithm based on YOLOv8 for detecting defects that are difficult to be detected. This improvement includes the introduction of an SPD module to minimize the problem of information loss due to downsampling;the addition of a small object detection head combined with EMA attention to further enhance the detection capabilities for small targets;the use of BiFPN to replace the original PANet, applying different weights to different features to strengthen feature fusion. Experiments were conducted on the NEU-DET dataset, and the algorithm improved mAP to 80.2%, an improvement of 3.9% over the baseline model. © 2024 IEEE.

关键词： Change detection

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Analysis of Bamboo Resource Survey Application Based on Remote Sensing Images 6

Analysis of Bamboo Resource Survey Application Based on Remo...

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6th International Conference on Computer Information Science and Application Technology, CISAT 2023

作者： Liu, Xiaohong Yan, Kai Sun, Maosheng College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650000 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Forestry Southwest Forestry University Kunming650224 China

ISBN: (纸本)9781510668546

Understanding the distribution range of bamboo resources in Yiliang county is important for the ecological services and economic benefits of the county. In this paper, we investigated the distribution of bamboo resources by using 3S (GPS, RS, GIS) technology with remote sensing images and field survey. The survey results show that: the total area of bamboo forest in the county is 65863.72×10-2 km2;the origin of bamboo resources is mainly plantation and natural forest;the types of bamboo forest are pure bamboo forest, tree forest and mixed bamboo forest;the applications are mainly nature reserve bamboo forest, protective bamboo forest, timber bamboo forest and bamboo forest for bamboo shoots;There are 9 kinds of bamboo species, including Jinfo Mountain Fang, Xiaochaoba Fang, Yushan Bamboo, Cixi Bamboo, Mao Bamboo, Bambusa Bamboo, Shui Bamboo, Yongsan Fang and Siban Bamboo;the protection level is Ⅰ, Ⅱ, Ⅲ and Ⅳ respectively. © 2023 SPIE.

关键词： Bamboo

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Homomorphic Encryption based stem cell assay data sharing study

Homomorphic Encryption based stem cell assay data sharing st...

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Information Technology,big data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Hengjiang Xiao Zhihong Liang Yuxiang Huang Mingming Qin Fei Xiong Mengxin Liu School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data Southwest Forestry University School of Economics and Management Southwest Forestry University Kunming China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

With the rapid development of the biomedical field, stem cell therapy has received widespread attention as an innovative therapy with great potential. However, privacy protection and secure sharing of stem cell testing data are still major challenges. In this paper, we propose a homomorphic encryption-based data sharing scheme for stem cell testing, which achieves efficient and secure data sharing with data privacy by designing a system architecture covering data acquisition, encryption, transmission, decryption and analysis. Experimental results show that the scheme can not only effectively protect data privacy, but also support efficient data processing with less impact on system performance. The main contributions of this paper include designing and implementing a stem cell detection data sharing system based on homomorphic encryption, proposing a homomorphic encryption algorithm optimised for the high-dimensional characteristics of stem cell data, and experimentally verifying the security and effectiveness of the system. Future research directions include optimising homomorphic encryption algorithms, expanding application scenarios, integrating multiple privacy protection techniques, and optimising system performance.

关键词： data privacy Privacy System performance Medical treatment Systems architecture Stem cells Homomorphic encryption Protection Information technology Testing

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FELSDiff: Image Super-Resolution Based on Diffusion Model Fusing Feature Extraction with Latent Space 20

FELSDiff: Image Super-Resolution Based on Diffusion Model Fu...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wu, Cancan Zhang, Zhi Wuhan University of Science and Technology College of Computer Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

In addressing issues like an excessive number of network parameters and the loss of fine texture details in image super-resolution reconstruction approaches based on diffusion models, this passage introduces a novel image super-resolution model (FELSDiff). This model relies on the fusion of diffusion models, feature extraction, and latent space. FELSDiff utilizes a Pre-trained CNN for initial super-resolution to extract the primary low-band features and part high-band features of the image. The preliminary super-resolved image is then concatenated as a condition to this model. We introduce a cross-attention mechanism guided by the high-band features of the preliminary super-resolved image to enhance the network's feature extraction capability and obtain more effective features. In the U-Net structure, we incorporate a latent space strategy, enabling the model's training and inference processes to be conducted in the latent space, thereby reducing the number of network parameters. Experimental results on multiple standard datasets indicate that this method outperforms other advanced methods overall. © 2024 IEEE.

关键词： Convolutional neural networks

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Event Argument Extraction with Two-Dimensional Enhanced Fusion Mechanism

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Computer Engineering and Applications 2025年第10期61卷 111-119页

作者： Wang, Luxiang Chen, Yanping Huang, Hui Huang, Ruizhang Qin, Yongbin Engineering Research Center of Ministry of Education for Text Computing and Cognitive Intelligence School of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

Addressing the lack of interaction between trigger and arguments, and the lack of interaction within channels in existing studies of event argument extraction, this paper proposes a two-dimensional enhanced fusion mechanism for event argument extraction (W2-ARG) that incorporates a 2D enhanced fusion method. It inserts identifiers on both sides of a trigger word in a sentence, which encodes information about the event type. In the learning process, it is effective to enhance the interaction between trigger and argument, and encode the trigger separately to highlight its information in the sentence. The event argument extraction is implemented as a two-dimensional representation of label prediction. It has the advantage to capture semantic interactions of words at different distances through dilated convolution. Subsequently, a channel attention module is applied to enhance the interactions within the channel to strengthen the information transfer within the channel. Finally, the Laplace operator is utilized to highlight the positional features of event arguments in the semantic space to improve the performance. The proposed model is experimented on the ACE05-EN and ERE-EN datasets. Experimental results show clear performance improvement of the proposed model compared with other event argument extraction methods. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： BERT channel attention event argument extraction Laplace operator sentence planarization representation

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A Hybrid Prediction Method Based on MODWT-EMD for Time Series in Wireless Sensor Networks 16

A Hybrid Prediction Method Based on MODWT-EMD for Time Serie...

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16th IEEE International Conference on intelligent Systems and Knowledge Engineering, ISKE 2021

作者： Wang, Zhongmin Jia, Jingwen Gao, Cong Chen, Yanping Xia, Hong Xi'an University of Posts and Telecommunications Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an China Xi'an University of Posts and Telecommunications School of Computer Science Technology Xi'an China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China

ISBN: (纸本)9781665405539

Time series is the main form of sensor data. Time series prediction is conducive to reduce the energy consumption of sensor nodes and increase the service life. As time cost and computation cost of the existing hybrid prediction methods for time series are not good, this paper proposes a hybrid prediction method based on MODWT-EMD for time series. Firstly, the original time series is decomposed into multiple frequency components by Maximal Overlapping Discrete Wavelet Transform (MODWT). Secondly, the multiple frequency components are decomposed successively by Empirical Mode Decomposition (EMD) to obtain IMF components and residuals. IMFs and residuals contain the feature information of time series. Then, the importance of the extracted feature information is scored and sorted by random forest. We select feature information with a higher score. Finally, the selected feature information is input into Bi-GRU for training. We predicted the original time series by the trained Bi-GRU and obtained the prediction results. The experimental results show that the method proposed in this paper can predict the time series effectively. © 2021 IEEE.

关键词： Empirical mode decomposition

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LP-Gaussians: Learnable Parametric Gaussian Splatting for Efficient Dynamic Reconstruction of Single-View Scenes

LP-Gaussians: Learnable Parametric Gaussian Splatting for Ef...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wu, Shaoqi Xie, Weixing Peng, Youhong Li, Jinwen Yao, Jiawei Yao, Junfeng Center for Digital Media Computing School of Film School of Informatics Xiamen University China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Taiwan

ISBN: (纸本)9798350368741

With the popularity of short video platforms, the number of single-view videos has increased significantly. Existing NeRF-based methods can reconstruct dynamic scenes in a single-view setting, but slow rendering speed and low rendering quality limit their practical applications. To address these challenges, we propose a fast single-view scenes reconstruction framework based on 3D Gaussian Splatting. Our method uses point clouds obtained with depth priors as the Gaussian initialization and introduces learnable parametric functions to model the time-dependent deformation of Gaussians. The explicit deformation modeling for Gaussians significantly reduces training and rendering time. Furthermore, to improve the rendering quality of challenging areas, we adopt an adaptive sampling strategy to densify Gaussians. For occlusion problems from single-view videos, we design a smooth loss function to restore the color of the occluded areas. Experimental results demonstrate that our method significantly reduces training time, enhances rendering quality, and accelerates rendering speed. Project page: https://***/LPGaussians. © 2025 IEEE.

关键词： 3D Gaussian Splatting Dynamic Scene Reconstruction Single-view Reconstruction

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Interpretable Disease Progression Path for Next Admission Diagnosis Event Prediction in Healthcare data via Hybrid Rule-Transformer Network 11th

Interpretable Disease Progression Path for Next Admission D...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Xu, Fanxin Yu, Hong Su, Zuqiang Zhang, Ping Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing401331 China Sichuan Huhui Software Co. LTD Mianyang China

ISBN: (纸本)9783031927409

Next admission diagnosis event prediction is one of the core tasks based on electronic health records. Existing researches on this task mainly focus on learning accurate disease representations and then fusing these disease representations into deep learning models for learning and prediction. This approach implicitly models the disease progressing path between diseases in the model, while ignoring the explicit disease progression path between diseases that can be established by the data itself. Therefore, it is impossible to accurately and explicitly represent the disease progression path to achieve the purpose of interpreting the disease progression path. For this problem, this paper propose the Hybrid Rule-Transformer Network, it first represent the interpretable disease progression paths as positive region-based decision rules by Pawlak Rough Set, then fuse the original disease embedding and the rule embedding by a dynamic gating fusion strategy, and finally realize the extraction of patient time series modeling information through the Transformer layer. Extensive experiments on two real EHR datasets show that the model this paper established has achieved state-of-the-art in terms of F1-score and Recall, and provides process interpretability of the disease progression paths between visits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Li, Hangying Xu, Changlin Shen, Juhong School of Mathematics and Information Science North Minzu University Ningxia Yinchuan750021 China Key Laboratory of Intelligent Information and Big Data Processing Ningxia Yinchuan750021 China

ISBN: (纸本)9783031927461

To improve the recommendation accuracy of collaborative filtering algorithms, this paper proposes a similarity measurement method based on the concept of normal cloud. First, starting from the characteristic curve of the normal cloud, combining the expected entropy curve and the outer envelope curve of the normal cloud, the paper uses the Bhattacharyya distance to characterize the similarity of probability distributions, and proposes a Bhattacharyya distance-based normal cloud similarity measurement method. Then, numerical simulation experiments are conducted to compare and analyze the performance of the proposed method with existing methods. The experimental results show that the proposed method has a better similarity differentiation capability. Finally, the method is applied to a collaborative filter recommendation system, and experiments are carried out on the MovieLens 100k movie review dataset. The results demonstrate that the proposed method is effective and feasible in collaborative filtering applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Normal distribution

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