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2022 International Joint Conference on Information and Communication engineering, JCICE 2022

作者： Zhu, Hongli Jin, Meiqi Wen, Yumeng Lu, Ling Liaoning University of Science and Technology School of Computer and Software Engineering Department of Software Engineering Liaoning Anshan China Liaoning University of Science and Technology School of Computer and Software Engineering Department of Network Engineering Liaoning Anshan China Liaoning University of Science and Technology School of Computer and Software Engineering Liaoning Anshan China

ISBN: (数字)9781665460675

ISBN: (纸本)9781665460675

The height of Infiltration line of tailings dam is a very important parameter for the safety of tailings reservoir. Infiltration line is the intersection line between the free water surface and the cross section of the dam body, that the free water surface is formed by the infiltration of water from the earth dam to the downstream. When the buried depth of the pressure sensors at each monitoring point could be obtained and the groundwater seepage pressure of the tailings dam could be measured too, the height of the free water surface can be calculated then. The commonly used pressure sensor is vibrating wire sensor. Among the many seepage pressure monitoring points, because of the lines, power supply or some other reasons, it often occurs that one or some points do not collect groundwater seepage pressure data for a period of time. For the missing data, BP neural network can be used to mend the missed data. Of course, the patched data should be marked to distinguish it from the real collected data. The mending method of missing seepage pressure data using BP neural network model is analyzed. The influence of anti-normalization parameters on mending data is analyzed. In the process of anti-normalization, the maximum and minimum values of the output data of the training data set are not put to use. Instead, a sequences is formed with the existing output data of some range before and after the mission data segment. The maximum and minimum values of the sequence are selected to use as the anti-normalization parameters. This will make the patched data values closer to the actual collected values. © 2022 IEEE.

关键词： Groundwater

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Analyzing the Trade-Off Between Complexity Measures, Ambiguity in Insertion System and Its Application in Natural Languages 11th

Analyzing the Trade-Off Between Complexity Measures, Ambigu...

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Mahendran, Anand Kannan, Kumar Hamada, Mohammed Laboratory of Theoretical Computer Science Higher School of Economics Moscow Russia School of Computer Science and Engineering Vellore Institute of Technology Vellore India Software Engineering Lab The University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9789811988240

Insertion is one of the basic operations in DNA computing. Based on this basic operation, an evolutionary computation model, the insertion system, was defined. For the evolutionary computation model defined above, varying levels of ambiguity and basic descriptional complexity measures have been defined. In this paper, we define twelve new (descriptional) complexity measures based on the integral parts of the derivation, such as axioms, strings to be inserted, and contexts used in the insertion rules. Later, we analyze the trade-off among the complexity measures and the existing ambiguity levels. Finally, we examine the application of the analyzed trade-off in natural languages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Economic and social effects

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MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

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computers, Materials & Continua 2023年第6期75卷 5573-5595页

作者： Zhanyang Xu Jianchun Cheng Luofei Cheng Xiaolong Xu Muhammad Bilal School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory Novel Software Technology Nanjing UniversityNanjing210023China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign StudiesYongin-siGyeonggi-do17035Korea

Federated learning has been used extensively in business inno-vation scenarios in various *** research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment ***,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise *** proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model ***,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and *** addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global *** results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify ***,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

关键词： Federated learning feature selection credit risk assessment MSEs

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The SOC Prediction of Li-ion Batteries Based on Neural Network and Random Forest 2nd

The SOC Prediction of Li-ion Batteries Based on Neural Netwo...

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2nd International Conference on Image, Vision and Intelligent Systems, ICIVIS 2022

作者： Yu, Mengqi Wang, Shuai Wei, Yining Zhong, Zhenkun Peng, Hui School of Computer Science and Engineering Central South University Changsha410083 China School of Software Engineering Xinjiang University Xinjiang830008 China

ISBN: (纸本)9789819909223

The state of charge (SOC) of lithium-ion batteries is predicted by using neural network and random forest. According to the battery discharge state in different periods, the entire discharge process is divided into platform stage and non-platform stage, and the above two methods are used to predict each stage separately. On the basis of fully analyzing the charge and discharge mechanism of lithium-ion batteries, the back propagation (BP) neural network model of lithium-ion batteries is established by genetic algorithm. The result proves that the SOC prediction method based on neural network and random forest has high accuracy, so this method can be applied in predicting the battery SOC. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithium-ion batteries

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Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5026-5039页

作者： Li, Mengke Cheung, Yiu-Ming Lu, Yang Hu, Zhikai Lan, Weichao Huang, Hui Shenzhen518132 China Hong Kong Baptist University Department of Computer Science 999077 Hong Kong Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen361005 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This article therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. © 2020 IEEE.

关键词： Benchmarking

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A novel observation points‐based positive‐unlabeled learning algorithm

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1425-1443页

作者： Yulin He Xu Li Manjing Zhang Philippe Fournier‐Viger Joshua Zhexue Huang Salman Salloum Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) ShenzhenChina College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina School of Computing National University of SingaporeSingaporeSingapore

In this study,an observation points‐based positive‐unlabeled learning algorithm(hence called OP‐PUL)is proposed to deal with positive‐unlabeled learning(PUL)tasks by judiciously assigning highly credible labels to unlabeled *** proposed OP‐PUL algorithm has three ***,an observation point classifier ensemble(OPCE)algorithm is constructed to divide unlabeled samples into two categories,which are temporary positive and permanent negative ***,a temporary OPC(TOPC)is trained based on the combination of original positive samples and permanent negative samples and then the permanent positive samples that are correctly classified with TOPC are retained from the temporary positive ***,a permanent OPC(POPC)is finally trained based on the combination of original positive samples,permanent positive samples and permanent negative *** exhaustive experimental evaluation is conducted to validate the feasibility,rationality and effectiveness of the OP‐PUL algorithm,using 30 benchmark PU data *** show that(1)the OP‐PUL algorithm is stable and robust as unlabeled samples and positive samples are increased in unlabeled data sets and(2)the permanent positive samples have a consistent probability distribution with the original positive ***,a statistical analysis reveals that POPC in the OP‐PUL algorithm can yield better PUL performances on the 30 data sets in comparison with four well‐known PUL *** demonstrates that OP‐PUL is a viable algorithm to deal with PUL tasks.

关键词： artificial intelligence datamining,machine learning

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Two-stage image inpainting method based on transformer and Fast Fourier convolution 2

Two-stage image inpainting method based on transformer and F...

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Li, Changxing Chen, Zhuang Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing China Chongqing University of Technology School of Computer Science and Engineering Chongqing China

ISBN: (纸本)9798350331417

In view of the insufficient ability of the currently existing deep learning-based methods to repair image high-frequency information and the small sensory field of the traditional convolutional methods. A two-stage image repair model is proposed. First, Transformer is used to repair the image initially;second, Fourier convolution is used to construct a generative network to refine the high-frequency details of the image;finally, the repair results are input into the discriminator for training to obtain the final image repair model. The experimental results on street view datasets show that the model in the paper has better restoration results in high-frequency details as well as semantic information;compared with mainstream methods, It achieves better results in peak signal-to-noise ratio and structural similarity. © 2023 IEEE.

关键词： Generative adversarial network image inpainting multiple attention Receptive field

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ASIMSA: Advanced Semantic Information Guided Multi-Scale Alignment Framework for Medical Vision-Language Pretraining 9

ASIMSA: Advanced Semantic Information Guided Multi-Scale Ali...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Xiao, Shuai Zhang, Yangyang Jiang, Liming Wang, Zhengxia School of Computer Science and Technology Hainan University China School of Information and Communication Engineering Hainan University China Software Co. Ltd. China

ISBN: (纸本)9798350352214

Medical Visual Language Pretraining (MVLP) utilizes textual reports for weak supervision to improve the learning of medical visual representations, showing promise in various medical image analysis tasks. While previous studies have focused on image and report correspondences within the same instances, they have overlooked high-level semantic associations, such as disease-level semantic correspondences, across different instances. To address this issue, we propose an Advanced Semantic Information Guided Multi-Scale Alignment Framework (ASIMSA) with two main modules: 1) Image-Report Alignment (IRA) module for learning shared representations between image and report of the same instance, and 2) Disease-Entity Alignment (DEA) module for learning disease entity correspondences across different instances. Extensive experiments on zero-shot classification, fine-tune classification, and semantic segmentation tasks validate the effectiveness of our proposed method, demonstrating its stable and superior performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua science and Technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework 24

NNVISR: Bring Neural Network Video Interpolation and Super R...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Tong, Yuan Hu, Mengshun Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

We present NNVISR - an open-source filter plugin for the VapourSynth video processing framework, which facilitates the application of neural networks for various kinds of video enhancing tasks, including denoising, super resolution, interpolation, and spatio-temporal super-resolution. NNVISR fills the gap between video enhancement neural networks and video processing pipelines, by accepting any network that enhances a group of frames, and handling all other network agnostic details during video processing. NNVISR is publicly released at https://***/tongyuantongyu/vs-NNVISR. © 2024 ACM.

关键词： Open systems

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