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

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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An Error-Guided Correction Model for Chinese Spelling Error Correction

An Error-Guided Correction Model for Chinese Spelling Error ...

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2022 Findings of the Association for computational Linguistics: EMNLP 2022

作者： Sun, Rui Wu, Xiuyu Wu, Yunfang MOE Key Laboratory of Computational Linguistics Peking University Beijing China School of Software and Microelectronics Peking University Beijing China School of Computer Science Peking University Beijing China

Although existing neural network approaches have achieved great success on Chinese spelling correction, there is still room to improve. The model is required to avoid over-correction and to distinguish a correct token from its phonological and visually similar ones. In this paper, we propose an error-guided correction model (EGCM) to improve Chinese spelling correction. By borrowing the powerful ability of BERT, we propose a novel zero-shot error detection method to do a preliminary detection, which guides our model to attend more on the probably wrong tokens in encoding and to avoid modifying the correct tokens in generating. Furthermore, we introduce a new loss function to integrate the error confusion set, which enables our model to distinguish easily misused tokens. Moreover, our model supports highly parallel decoding to meet real application requirements. Experiments are conducted on widely used benchmarks. Our model achieves superior performance against state-of-the-art approaches by a remarkable margin, on both the correction quality and computation speed. © 2022 Association for computational Linguistics.

关键词： Error correction

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RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion 30

RENN: A Rule Embedding Enhanced Neural Network Framework for...

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Joint 30th International Conference on computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zong, Linlin Xie, Zhenrong Ma, Chi Liu, Xinyue Zhang, Xianchao Xu, Bo Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology China School of Computer Science and Technology Dalian University of Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China

ISBN: (纸本)9782493814104

Existing approaches encompass deep neural network-based methods for temporal knowledge graph embedding and rule-based logical symbolic reasoning. However, the former may not adequately account for structural dependencies between ***, the latter methods relies heavily on strict logical rule reasoning and lacks robustness in the face of fuzzy or noisy data. In response to these challenges, we present RENN, a groundbreaking framework that enhances temporal knowledge graph completion through rule embedding. RENN employs a three-step approach. First, it utilizes temporary random walk to extract temporal logic rules. Then, it pre-trains by learning embeddings for each logical rule and its associated relations, thereby enhancing the likelihood of existing quadruples and logical rules. Finally, it incorporates the embeddings of logical rules into the deep neural network. Our methodology has been validated through experiments conducted on various temporal knowledge graph models and datasets, consistently demonstrating its effectiveness and potential in improving temporal knowledge graph completion. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model 22

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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22nd IEEE International Symposium on parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Zhi Zhang, Shiwen Chen, Shuang Liang, Wei Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798331509712

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients' potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning. © 2024 IEEE.

关键词： Federated learning

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A relative coordinate based distributed sparse-preserving matrix factorization approach towards self-stabilizing network location service - Withdrawn

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IEEE Transactions on parallel and Distributed Systems 2023年 1-1页

作者： Fu, Yongquan Wang, Yijie Pei, Xiaoqiang Li, Xiaoyong Science and Technology Laboratory of Parallel and Distributed Processing National University of Defense Technology China

Withdrawn. IEEE

关键词： Linear matrix inequalities

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ScatterX:A software for fast processing of high-throughput small-angle scattering data

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中国物理B（英文版） 2024年第12期33卷 128-141页

作者：谢飞解梅宋宝玉郭桥雨焦学琛 National Synchrotron Radiation Laboratory University of Science and Technology of ChinaHefei 230029China

Scattering experiments become increasingly popular in modem scientific research,including the areas of materials,biology,chemistry,physics,***,various types of scattering facilities have been developed recently,such as lab-based x-ray scattering equipment,national synchrotron facilities and large neutron *** above-mentioned trends bring up fast-increasing data amounts of scattering data,as well as different scattering types(x-ray,neutron,laser and even microwaves).To help researchers process and analyze scattering data more efficiently,we developed a general and model-free scattering data analysis software based on matrix operation,which has the unique advantage of high throughput scattering data processing,analysis and *** maximize generality and efficiency,data processing is performed based on a three-dimensional matrix,where scattering curves are saved as matrices or vectors,rather than the traditional definition of paired *** can not only realize image batch processing,background subtraction and correction,but also analyze data according to scattering theory and model,such as radius of gyration,fractal dimension and other physical *** the aspect of visualization,the software allows the modify the color maps of two-dimensional scattering images and the gradual color variation of one-dimensional curves to suit efficient data *** all,this new software can work as a stand-alone platform for researchers to process,analyze and visualize scattering data from different research facilities without considering different file types or *** codes in this manuscript are open-sourced and can be easily implemented in matrix-based software,such as MATLAB,Python and Igor.

关键词： scatter fractal correlation function high-throughput SVD

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Expression Complementary Disentanglement Network for Facial Expression Recognition

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Chinese Journal of Electronics 2024年第3期33卷 742-752页

作者： Shanmin WANG Hui SHUAI Lei ZHU Qingshan LIU College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics College of Computer and Software Nanjing University of Information Science and Technology School of Information and Control Nanjing University of Information Science and Technology

Disentangling facial expressions from other disturbing facial attributes in face images is an essential topic for facial expression *** methods only care about facial expression disentanglement(FED)itself,ignoring the negative effects of other facial *** to the annotations on limited facial attributes,it is difficult for existing FED solutions to disentangle all disturbance from the input *** solve this issue,we propose an expression complementary disentanglement network(ECDNet).ECDNet proposes to finish the FED task during a face reconstruction process,so as to address all facial attributes during *** from traditional reconstruction models,ECDNet reconstructs face images by progressively generating and combining facial appearance and matching *** designs the expression incentive(EIE) and expression inhibition(EIN) mechanisms,inducing the model to characterize the disentangled expression and complementary parts *** geometry and appearance,generated in the reconstructed process,are dealt with to represent facial expressions and complementary parts,*** combination of distinctive reconstruction model,EIE,and EIN mechanisms ensures the completeness and exactness of the FED *** results on RAF-DB,AffectNet,and CAER-S datasets have proven the effectiveness and superiority of ECDNet.

关键词： Facial expression recognition Facial expression disentanglement Face reconstruction Expression incentive Expression inhibition

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GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing

arXiv

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arXiv 2024年

作者： Wang, Zhichao C. Chen, Xinhai C. Gong, Chunye Y. Yang, Bo Deng, Liang Sun, Yufei F. Pang, Yufei F. Liu, Jie National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment China China Aerodynamics Research and Development Center China Nankai University College of Software China

Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of traditional mesh smoothing process, previous approaches have employed supervised learning and reinforcement learning to train intelligent smoothing models. However, these methods heavily rely on labeled dataset or prior knowledge to guide the models’ learning. Furthermore, their limited capacity to enhance mesh connectivity often restricts the effectiveness of smoothing. In this paper, we first systematically analyze the learning mechanisms of recent intelligent smoothing methods and propose a prior-free reinforcement learning model for intelligent mesh smoothing. Our proposed model integrates graph neural networks with reinforcement learning to implement an intelligent node smoothing agent and introduces, for the first time, a mesh connectivity improvement agent. We formalize mesh optimization as a Markov Decision Process and successfully train both agents using Twin Delayed Deep Deterministic Policy Gradient and Double Dueling Deep Q-Network in the absence of any prior data or knowledge. We verified the proposed model on both 2D and 3D meshes. Experimental results demonstrate that our model achieves feature-preserving smoothing on complex 3D surface meshes. It also achieves state-of-the-art results among intelligent smoothing methods on 2D meshes and is 7.16 times faster than traditional optimization-based smoothing methods. Moreover, the connectivity improvement agent can effectively enhance the quality distribution of the mesh. © 2024, CC BY.

关键词： Deep reinforcement learning

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Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

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Digital Communications and Networks 2025年第2期11卷 473-481页

作者： Qi Li Gaozhan Liu Qianqian Zhang Lidong Han Wei Chen Rui Li Jinbo Xiong School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal UniversityHangzhou 311121China Tongda College of Nanjing University of Posts and Telecommunications Yangzhou 225127China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China School of Computer Science and Technology Xidian UniversityXi'an 710071China State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal UniversityXining 810008China Faculty of Software Fujian Normal UniversityFuzhou 350108China

Ciphertext-Policy Attribute-Based Encryption(CP-ABE)enables fine-grained access control on ciphertexts,making it a promising approach for managing data stored in the cloud-enabled Internet of *** existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute ***,resource-constrained IoT devices,especially those adopting wireless communication,frequently encounter affordability issues regarding decryption *** this paper,we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC).FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them.A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access *** attacks are thwarted by providing unauthorized users with invalid *** heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud ***,users can verify the correctness of multiple outsourced decryption results *** analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

关键词： Access control Policy hiding Verifiable outsourced computation Cloud IoT

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3DGCformer: 3-Dimensional Graph Convolutional transformer for multi-step origin–destination matrix forecasting

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Applied Intelligence 2025年第6期55卷 1-18页

作者： Huang, Yiou Deng, Hao Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Origin–Destination (OD) matrices for a relatively long period in the future. However, multi-step Origin–Destination Matrix Forecasting (ODMF) is a non-trivial problem. First, previous ODMF models only forecast the OD matrix for the next time-step, and they cannot perform well on long-term multi-step forecasts due to error accumulation. Second, many ODMF methods capture spatial and temporal dependencies with separate modules, which is insufficient to model spatio-temporal correlations in the time-varying OD matrix sequence. To address the challenges in multi-step ODMF, we propose 3-Dimensional Graph Convolutional Transformer (3DGCformer). As an enhancement of the original 3DGCN, we propose a novel Origin–Destination Feature Propagation (ODFP) rule between 3DGCN layers and integrate 2 3DGCNs with different spatio-temporal graphs and corresponding feature propagation rules to model the formation of OD flows in a more comprehensive way. For multi-step forecasts, 3DGCformer uses Transformer to capture long-term global temporal dependency, and adapt its decoder using labeled tokens to avoid error accumulation and improve time efficiency. To avoid information loss as the number of regions increases, we propose a patch embedding approach to convert data from 3DGCNs to the Transformer module. We perform extensive experiments on 4 real-world human mobility datasets, and the results show that our proposed model outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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