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Does digital inclusive finance promote industrial global value chain upgrading? evidence from China's advanced manufacturing industry

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Finance Research Letters 2025年 82卷

作者： Cai, Yuanyuan Liang, Le Zhang, Yi Qi, Liangqun Wang, Chengdong School of Economics and Management Harbin University of Science and Technology Harbin 150080 China Heilongjiang Provincial Government Big Data Center Harbin 150080 China

This study examined digital inclusive finance (DIF), green technology innovation (GTI), digitalization, and the upgrading of industrial global value chain (GVC) in the context of a unified research framework to improve the existing research system by considering the theoretical and partial correlations among the constructs. To theoretically reveal the impact of DIF on the upgrading of the industrial GVC as well as the mediating role of GTI and the moderating role of digitalization, an empirical study was conducted with China's advanced manufacturing industry as the object of study. The research revealed three main findings. First, DIF could effectively promote GTI in and the GVC upgrading of advanced manufacturing industries. Second, GTI plays a partial mediating role in the process of DIF's promotion of the GVC upgrading of advanced manufacturing industries. Third, the level of digitalization positively moderates the relation between DIF and the GVC upgrading of advanced manufacturing industries. Based on the results, strategies for promoting the GVC upgrading of China's advanced manufacturing industries are proposed. © 2025

关键词： Advanced manufacturing Digital inclusive finance Digitalization Green technology innovation Industrial GVC upgrading

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QoS-Aware and Routing-Flexible Network Slicing for Service-Oriented Networks

arXiv

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arXiv 2024年

作者： Chen, Wei-Kun Liu, Ya-Feng Dai, Yu-Hong Luo, Zhi-Quan The School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China

In this paper, we consider the network slicing (NS) problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and manage network resources to meet diverse quality of service (QoS) requirements. We propose a mixed-integer nonlinear programming (MINLP) formulation for the considered NS problem that can flexibly route the traffic flow of the services on multiple paths and provide end-to-end delay and reliability guarantees for all services. To overcome the computational difficulty due to the intrinsic nonlinearity in the MINLP formulation, we transform the MINLP formulation into an equivalent mixed-integer linear programming (MILP) formulation and further show that their continuous relaxations are equivalent. In sharp contrast to the continuous relaxation of the MINLP formulation which is a nonconvex nonlinear programming problem, the continuous relaxation of the MILP formulation is a polynomial-time solvable linear programming problem, which significantly facilitates the algorithmic design. Based on the newly proposed MILP formulation, we develop a customized column generation (cCG) algorithm for solving the NS problem. The proposed cCG algorithm is a decomposition-based algorithm and is particularly suitable for solving large-scale NS problems. Numerical results demonstrate the efficacy of the proposed formulations and the proposed cCG algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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A Clustering-Based Routing Protocol Using Path Pattern Discovery Method to Minimize Delay in VANET

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Xie, Xiaoyun Navaei, Yahya Dorostkar Einy, Sajad School of Electronic Information Engineering Gannan University of Science and Technology Jiangxi Ganzhou341000 China Ganzhou Key Laboratory of Cloud Computing and Big Data Jiangxi Ganzhou341000 China Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Istanbul Aydin University Department of Application and Research Center for Advanced Studies Istanbul Turkey

In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communications can link vehicles to each other, and vehicle-to-infrastructure (V2I) messaging and communications can link roadside infrastructure such as routers. The vehicles in these networks act as relays that transmit critical messages in the network. Due to the high-speed movement of vehicles on the road, real-time messaging and minimizing the delay in sending messages is one of the most important objectives of VANET developers. On the other hand, the high mobility of vehicles causes communication interruptions and decreases the data delivery rate in VANET. To overcome this issue, predicting the path of vehicles can play an important role in sending data from the source to the destination. When an accident occurs on the road, the messages that are sensed by the imbedded sensors in the vehicles need to be sent, and if they are sent by the vehicles that change their route, these messages will not be sent to the destination and the performance of the network will be disturbed. Previous methods in the literature for data transmission in intervehicular networks have focused more on reliability and trust, and little attention has been paid to the prediction of vehicle movement paths in these types of networks. Therefore, for fast and reliable data transmission in VANET, accurate prediction of vehicle movement and creation of movement patterns can be effective in message transmission delay and data delivery rate. In this paper, we present an approach using a combination of cluster-based routing protocols and pattern discovery methods to minimize latency in VANETs. The outline of the proposed method has four modules: primary data collection and analysis, primary data preparation and analysis, pattern extraction and vehicle route discovery, and vehicle clustering and data/information transmission routing. The simulation results show that the proposed method with a delivery rate of 88.56% has significantly i

关键词： Vehicular ad hoc networks

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Trend-encoded Probabilistic Multi-Order Model: A Non-Machine Learning Approach for Enhanced Stock Market Forecasts

arXiv

引用

arXiv 2025年

作者： Wang, Peiwan Cui, Chenhao Li, Yong School of Mathematics and Big Data Chaohu University 1 Bantang Road Chaohu Economic Development Zone Anhui Province Hefei City238024 China Technology Co. Ltd. 9 Wangjiang West Road Anhui Province Hefei City230031 China New Finance Research Center International Institute of Finance University of Science and Technology of China 1789 Guangxi Road Anhui Province Hefei City230092 China

In recent years, the dominance of machine learning in stock market forecasting has been evident. While these models have shown decreasing prediction errors, their robustness across different datasets has been a concern. A successful stock market prediction model not only minimizes prediction errors but also showcases robustness across various data sets, indicating superior forecasting performance. This study introduces a novel multiple lag order probabilistic model based on trend encoding (TeMoP) that enhances stock market predictions through a probabilistic approach. Results across different stock indexes from nine countries demonstrate that the TeMoP outperforms the state-of-art machine learning models in terms of predicting accuracy and stabilization. Copyright © 2025, The Authors. All rights reserved.

关键词： Prediction models

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Semantic and syntactic enhanced aspect sentiment triplet extraction

arXiv

引用

arXiv 2021年

作者： Chen, Zhexue Huang, Hong Liu, Bang Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Huazhong University of Science and Technology China RALI & Mila University of Montreal

Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from sentences, where each triplet includes an entity, its associated sentiment, and the opinion span explaining the reason for the sentiment. Most existing research addresses this problem in a multi-stage pipeline manner, which neglects the mutual information between such three elements and has the problem of error propagation. In this paper, we propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model (S3E2) to fully exploit the syntactic and semantic relationships between the triplet elements and jointly extract them. Specifically, we design a Graph-Sequence duel representation and modeling paradigm for the task of ASTE: we represent the semantic and syntactic relationships between word pairs in a sentence by graph and encode it by Graph Neural Networks (GNNs), as well as modeling the original sentence by LSTM to preserve the sequential information. Under this setting, we further apply a more efficient inference strategy for the extraction of triplets. Extensive evaluations on four benchmark datasets show that S3E2 significantly outperforms existing approaches, which proves our S3E2’s superiority and flexibility in an end-to-end fashion. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

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Joint Beam Scheduling and Power Allocation for SWIPT in Mixed Near- and Far-Field Channels

Joint Beam Scheduling and Power Allocation for SWIPT in Mixe...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Yunpu Zhang Changsheng You Weijie Yuan Fan Liu Rui Zhang Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electrical Engineering City University of Hong Hong Hong Kong The Chinese University of Hong Kong (Shenzhen) and Shenzhen Research Institute of Big Data Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore

Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks, leading to a fundamental paradigm shift from conventional far-field communications towards the near-field communications. Different from the existing works that mostly considered simultaneous wireless information and power transfer (SWIPT) in the far field, we consider in this paper a new and practical scenario, called mixed near- and far-field SWIPT, in which energy harvesting (EH) and information decoding (ID) receivers are located in the near- and far-field regions of the XL-array base station (BS), respectively. Specifically, we formulate an optimization problem to maximize the weighted sum-power harvested at all EH receivers by jointly designing the BS beam scheduling and power allocation, under the constraints on the ID sum-rate and BS transmit power. To solve this non-convex optimization problem, an efficient algorithm is proposed to obtain a suboptimal solution by leveraging the binary variable elimination and successive convex approximation methods. Numerical results demonstrate that our proposed joint design achieves substantial performance gain over other benchmark schemes.

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A Knowledge-Enhanced Medical Named Entity Recognition Method that Integrates Pre-Trained Language Models

A Knowledge-Enhanced Medical Named Entity Recognition Method...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Zhiyuan Wang Qiang Zhou Zhao Junfeng Yasha Wang Hongxin Ding Jiahe Song Key Lab of HCST (PKU) MOE SCS Peking University Beijing China School of Software & Microelectronics Peking University Beijing China Shenzhen Emergency Medical Center Shenzhen China Research Center of Big Data Technology Nanhu Laboratory Jiaxing China School of Computer Science Peking University Beijing China

Medical Named Entity Recognition (NER) is a critical task in medical text processing. But medical documents exhibit high variability in terms of language usage, abbreviations, synonyms, misspellings, and typographical errors, so the precise extraction of named entities is challenging. Although large language models (LLMs) have shown good performance in medical knowledge extraction tasks in few-shot settings, their performance is difficult to fully leverage in supervised medical named entity recognition (NER) tasks. This is because NER is a sequence labeling task, while LLMs are more suitable for tasks such as text generation. Furthermore, the structured output of NER tasks leads to a performance loss when LLMs convert it into generative text. Therefore, it is a challenging problem to utilize LLMs to improve the accuracy of medical named entity recognition tasks. On this paper, we propose a method that integrates LLM knowledge to enhance the performance of medical NER models. Firstly, we improve the structure of the LLM model to make it more adaptable to NER tasks. Secondly, we adopt the LoRA method and incorporate Chinese vocabulary information into the model training. Finally, to fully utilize the fine-tuned LLM to enhance the medical NER model, we convert the output of the LLM into a knowledge concentration matrix and inject it into the NER model. We have verify the effectiveness of our new method on the CMeEE dataset. The results demonstrate that our method can efficiently fine-tune the LLM and improve its performance. Moreover, our method can also leverage the prior knowledge of the fine-tuned LLM to enhance the BERT-based medical NER model. In addition, our method demonstrates good generalization and can tackle entity recognition tasks in other domains. We validated the superiority of our approach on the resume-zh dataset.

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Industrial Smoke Image Segmentation Based on a New Algorithm of Cross-Entropy Model 16th

Industrial Smoke Image Segmentation Based on a New Algorithm...

引用

16th International Conference on Intelligent Computing, ICIC 2020

作者： Huang, Qian-Jing Zou, Le Wu, Zhi-Ze Li, Huan-Yi Wang, Xiao-Feng Chen, Yan-Ping Department of Environmental Engineering Hefei University HefeiAnhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University HefeiAnhui230601 China

ISBN: (纸本)9783030607982

Smoke segmentation from the industrial images is a key concern of environmental monitoring. As the similarities between the gray value of the background and the smoke, the existing segmentation algorithms are difficult to accurately segment the target smoke. In this paper, we construct a cross-entropy based industrial smoke image segmentation by integrating the iterative convolution-thresholding. Specially, we use the iterative convolution-thresholding to implicitly represent the interface of each image domain through a characteristic function. We further perform the combination of a regularization term and a fidelity term in the cross-entropy model. In the proposed algorithm, the fidelity term is first converted into the product of the characteristic function and the cross-entropy function. Then the functional of the characteristic function is used to obtain the regularization term by the approach of thermonuclear convolution approximation. The experimental results demonstrate that our proposal has a more accurate segmentation effect and higher segmentation efficiency. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoke Zhu Danyang Li Xiaopan Chen Fumin Qi Fan Zhang Xiao-Yuan Jing School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Department of System Operation National Supercomuting Center in Shenzhen Shenzhen China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

关键词： Pattern matching Similarity learning

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Hyperspectral Classification of Coastal Wetland Based on Multi-objective CNN and Decision Fusion Model

Hyperspectral Classification of Coastal Wetland Based on Mul...

引用

Information Systems and Computing technology (ISCTech), International Conference on

作者： Yabin Hu Junfang Yang Guangbo Ren Yi Ma Jianbu Wang Xiaoheng Zhu Lab of Marine Physics and Remote Sensing First Institute of Oceanography Ministry of Natural Resources Qingdao China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xian China College of Oceanography and Space Informatics China University of Petroleum Qingdao China College of Geodesy and Geomatics ShanDong University of Science and Technology Qingdao China

Coastal wetland play an irreplaceable role in maintaining biodiversity and improving climate. In this paper, we proposed a hyperspectral classification algorithm of coastal wetland based on multi-objective Convolutional Neural Networks (CNN) and decision fusion model. The algorithm is named that the multi-objective spatial-spectral CNN classification with binary morphological strategy decision fusion model algorithm (MB2SCNN-BMSDF). The multi-objective hyperspectral feature band selection model of coastal wetland based on spectral deviation algorithm is developed and constructed to effectively obtain hyperspectral sensitive bands for single target surface object types. On this basis, a CNN hyperspectral classification model that integrates multi-objective spatial spectral information, and a binary morphological decision fusion algorithm that combines spatial position information and fuzzy membership degrees are proposed, which solves the problems of salt and pepper noise and sand eye effect in the classification process, and reduces noise information. The results showed that the CNN classification of multi-objective spatial spectral information and the decision fusion model of binary morphology strategy have better classification effect and performance.

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