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作者： Xiu, Liu Big Data Management and Application Research Institute Nanchang Institute of Technology Jiangxi330044 China

In the context of the "Internet+" era, higher vocational colleges need to constantly strengthen the coordination and linkage among various subjects, establish coordination goals, build new education models, determine scientific education contents, select coordination methods, explore effective education paths, and form a collaborative education pattern of innovation and entrepreneurship according to the coordination requirements. The collaborative education between colleges and families presents the situation of "main" being strong and "auxiliary" being weak, passive family participation, poor communication channels, and imperfect mechanism and system. Based on the analysis of the feasibility of "Internet+" collaborative education between colleges and families, combined with the work practice in the process of deep integration of "Internet+" and education, the concept of collaborative education mechanism between colleges and families under the background of "Internet+" is proposed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： College collaborative education High quality teaching resources Internet+

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Big data and AI in Financial Risk Assessment: The New Scorecard Approach 4

Big Data and AI in Financial Risk Assessment: The New Scorec...

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4th IEEE International Conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Li, Hualin Chen, Kai Yang, Suyi Zhang, Dequan Zhou, Chenchen Li, Kaidong Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center China Shu Yi Xin Credit Management Co Ltd China Chongqing Institute of Engineering School of Big Data and Artificial Intelligence China

ISBN: (纸本)9798350363586

This paper presents a framework for assessing financial risk across industries by integrating rule-based modeling, the Analytic Hierarchy Process (AHP), and artificial intelligence (AI) techniques. The framework utilizes diverse data sources, including financial, tax, and judicial records, as well as financial reports from listed companies, to improve enterprise credit *** process begins with data preparation, which involves collecting and preprocessing data to ensure its quality and consistency. A rule-based preprocessing algorithm is developed to standardize the data for scoring. In the second phase, scoring is done using rule-based modeling informed by expert advice and AHP, which prioritizes and weights factors impacting industrial risk. This generates a customized scorecard for each industry. The third phase focuses on training AI models on the dataset. These models automatically learn to assign weights to indicators and generate industry scores by identifying complex patterns between the data and industry performance. The framework concludes with cross-validation to confirm the accuracy and reliability of the results. This approach provides insights into industry-wide scoring, supports continuous monitoring of industry performance, and enhances financial risk management decision-making. The proposed framework offers a robust and reliable tool for assessing financial risk across various industries. © 2024 IEEE.

关键词： Decentralized finance

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Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

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Chinese Physics B 2023年第1期32卷 380-391页

作者：刘华清姜锐田钧方朱凯旋 Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of TransportBeijing Jiaotong UniversityBeijing 100044China College of Management and Economics Tianjin UniversityTianjin 300072China Suzhou Institute of Technology Jiangsu University of Science and TechnologyZhangjiagang 215600China

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

关键词： traffic flow connected and automated vehicles(CAVs) lane drop optimization-based control algorithm Heuristic rules-based algorithm

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Converging Paths in Divergent Systems: A Comparative Analysis of data Science Education Strategies in China and the United States 54

Converging Paths in Divergent Systems: A Comparative Analysi...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Li, Duo Milonas, Elizabeth Zhang, Qiping Shenyang Institute of Technology Department of Big Data Management and Application Fushun China Brooklyn United States Palmer School of Library and Information Science Long Island University Brookville United States

ISBN: (纸本)9798350351507

This research-to-practice full paper compares data science education strategies in China and the United States, exploring whether different approaches can achieve similar educational outcomes. In the U.S., data science programs are typically developed by individual schools to meet labor market demands, integrating data science as an interdisciplinary subject. In contrast, Chinese universities follow a uniform plan set by the education department, resulting in specialized fields like 'Big data management and application' and 'data Science and Big data Technology.' This research collected data on course offerings and university rankings in China and the U.S., analyzing curriculum content and program characteristics. Major findings indicate that in the U.S., data Science and Business Analytics programs focus on technical skills and are primarily found in top-ranking universities, while in China, data Science and Big data management programs emphasize management and are more widely distributed across different ranking groups. Despite these differences, both countries show similar geographic concentration patterns in data science programs. The study concludes that China and the U.S. adopt different educational strategies but achieve comparable effectiveness in data science education. © 2024 IEEE.

关键词： data Science

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Traceability of Product Supply Chain Based on Hyperledger Fabric 4

Traceability of Product Supply Chain Based on Hyperledger Fa...

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4th International Conference on Advanced Information Science and System, AISS 2022

作者： Song, Shiyang Lu, Jiadong Zhao, Hanxu Wang, Wennan Shi, Chiyu Luo, Ruijie Alibaba Cloud Big Data Application College Anhui University China Academy of Management Anhui University China Institute of Data Science Anhui University China

ISBN: (纸本)9781450397933

At present, more and more consumers and manufacturers are aware of the importance of product traceability, so it is very important to strengthen the traceability in the supply chain. The existing centralized database system has isolated data storage, and many parties involved in data query lack mutual trust. Based on the distributed ledger of the blockchain, this paper attempts to solve this problem by transforming the traceability information of physical goods into the digital of the blockchain, so as to realize the collaborative traceability of entities in the supply chain. At present, the relationship between components and products in manufacturing process cannot be captured, which limits the ability to track the source of products. This paper proposes a supply chain traceability model based on blockchain, which uses smart contracts to complete multi-party agreements on the chain. In the contract, the products produced by the manufacturer are transferred in the specified alliance chain entity, and in each transfer process, the products will be marked with new traceability information in the form of digital authentication. This mechanism retains the possibility of products being traced. The model is tested under the framework of hyperledger fabric. By building a blockchain network, the entity objects participating in the supply chain can be authenticated to ensure the closed-loop circulation of goods. It also indirectly proves the feasibility of commodity traceability application based on blockchain technology. © 2022 ACM.

关键词： Blockchain

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Image super‐resolution via dynamic network

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 837-849页

作者： Chunwei Tian Xuanyu Zhang Qi Zhang Mingming Yang Zhaojie Ju School of Software Northwestern Polytechnical UniversityXi'anChina National Engineering Laboratory for Integrated Aero‐Space‐Ground‐Ocean Big Data Application Technology Xi'anChina Research&Development Institute Northwestern Polytechnical UniversityShenzhenChina School of Economics and Management Harbin Institute of Technology at WeihaiWeihaiChina Tencent AI Lab ShenzhenChina School of Computing University of PortsmouthPortsmouthUK

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐***,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction *** residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐*** enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying *** prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained ***,a residual learning operation is embedded in the refinement block to prevent long‐term dependency ***,a construction block is responsible for reconstructing high‐quality *** heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital *** results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and *** code of DSRNet can be obtained at https://***/hellloxiaotian/DSRNet.

关键词： CNN dynamic network image super‐resolution lightweight network

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application of Convolutional Neural Network in Livestock Target Detection of IoT Images 5

Application of Convolutional Neural Network in Livestock Tar...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Tian, Maohong Zhao, Feiyang Chen, Nengmei Zhang, Dequan Li, Jie Li, Hualin School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Shu Yi Xin Credit Management Co. Ltd Chongqing China School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Intelligent Application of Financial Big Data Chongqing Colleges and Universities Engineering Research Center Chongqing China Chongqing Da Tong Experimental School Chongqing China Shu Yi Xin Credit Management Co. Ltd Chongqing China

ISBN: (纸本)9798350385557

The national rural revitalization strategy has advanced, and new requirements for modern internet-of-things (IoT)-based farm supervision tools have been proposed. This study proposes a 'you-only-look-once' (YOLO) v5 convolutional neural network to optimize the candidate frame's center point algorithm. Five YOLOv5 models are used to identify and classify captive pigs. According to the experimental results, the K-median algorithm has a better effect than using the K-means algorithm, and its average intersection over union is approximately 1-2% higher. In our scenario, the commercial application value of the YOLOv5s model is higher, and the corresponding performance results of F1-score, mAP0.5, mAP0.5:0.95, FPS, and R2 are 92.21%, 95.18%, 64.85%, 75 FPS, and 85.54%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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A New Mathematical Model in Three-dimensional Variational data Assimilation for Air Quality Prediction

A New Mathematical Model in Three-dimensional Variational Da...

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2024 International Conference on Applied Mathematics, Modelling and Statistics application, AMMSA 2024

作者： Zhu, Zhaochen Gao, Yunyun Li, Haihe School of Statistics and Mathematics Zhejiang Gongshang University Hangzhou China Collaborative Innovation Center of Statistical Data Engineering Technology & Application Zhejiang Gongshang University Hangzhou China School of Economics and Management Xinjiang Institute of Technology Aksu China

The three-dimensional variational (3DVar) data assimilation is a critical component of air quality prediction, and the forecast state in air quality prediction is inferred to be approximately low rank and heavily laden with low-frequency noises through the application of tensor decomposition. To reduce the effects of these uncertainties and achieve a more precise analysis state, we establish a new objective function that consists of the weighted sum of three terms: the first term is the norm difference between the gradients of the forecast and analysis states, the second term is the norm difference between the observation and the analysis state in observational space, and the third term is the rank of the analysis state. Numerical results show that our treatment is superior to other methods, such as the classic 3DVar method, the En3DVar method, and the method of 3DVar with forecast gradient. © Published under licence by IOP Publishing Ltd.

关键词： Tensors

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Detection of Financial Fraudulent Activities with Machine Learning:A Case Study of Detecting Potential Tax and Invoice Fraud 7

Detection of Financial Fraudulent Activities with Machine Le...

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7th International Conference on Computer Science and Artificial Intelligence, CSAI 2023

作者： Tian, Maohong Liang, Jian Zhang, Dequan Zhang, Xintong Wang, Zuo Li, Hualin ChongQing Institute of Engineering Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center ChongQing China Shu Yi Xin Credit Management Co LTD ChongQing China

ISBN: (纸本)9798400708688

Financial fraud is a widespread problem that can cause significant economic losses. Traditional fraud detection methods often rely on manual audits and rules-based systems, which can be time-consuming and error-prone. In recent years, machine learning methods have emerged as a promising approach to automating fraud detection by leveraging large-scale data analysis. This article explores the use of machine learning methods to detect financial fraud by using tax, invoice, and big data. We first introduce the challenges and opportunities of using these data sources for fraud detection, and then survey various machine learning techniques that have been applied to this problem. We also discuss the evaluation metrics and case studies of these methods, and highlight the potential benefits and limitations of using machine learning for fraud detection. Finally, we identify some future research directions and challenges in this area. This article aims to provide a comprehensive method of the state-of-the-art in using machine learning methods for financial fraud detection, and to inspire further research and development in this important field. © 2023 ACM.

关键词： Big data

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The application of Big data and AI Analytics in Rating Industrial and Smart Manufacturing Districts: Challenges and Opportunities 3

The Application of Big Data and AI Analytics in Rating Indus...

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3rd International Conference on Big data, Information and Computer Network, BDICN 2024

作者： Zhou, Chenchen Wang, Wei Tian, Maohong Liang, Jian Zhang, Dequan Li, Hualin School of Software and Artificial Intelligence Chongqing Institute of Engineering Chongqing China Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center Chongqing China Shu Yi Xin Credit Management Co LTD Chongqing China

ISBN: (纸本)9798350363821

As an important carrier and component of industrial clusters, industrial districts are an important way for regional economic growth, and the economic effects of industrial districts have attracted more and more attention. Therefore, how to identify high-quality districts with good development trends and obvious competitive advantages from a large amount of district information has become an increasingly important topic. This article takes industrial and intelligent manufacturing districts as an example and proposes an innovative industrial and intelligent manufacturing zone rating method that combines rule-based modeling, Analytic Hierarchy Process (AHP), and Artificial Intelligence (AI) models. Collect structured and unstructured data, including real-Time operational data, historical records, market trends, and expert opinions, from multiple sources through big data technology. Apply rule-based models and Analytic Hierarchy Process (AHP) to prioritize and weight various factors that affect regional performance, and obtain district ratings;And use the Kmeans method for clustering to obtain district level classification. Finally, the paper combines the outputs of the rule-based models, AHP, and AI models to create an overall rating system. This system continuously monitors district performance, providing accurate and comprehensive ratings to support decision-making in industry and government. © 2024 IEEE.

关键词： Smart manufacturing

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