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2021 International Conference on Computer, Blockchain and Financial Development (CBFD)最新文献

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An improved K-Shell-Based Ranking of Node Importance 一种改进的基于k - shell的节点重要性排序
Pub Date : 2021-04-01 DOI: 10.1109/CBFD52659.2021.00059
Wang Jun-di, Zhu Ya-Ling, Wang Juan
Identifying important nodes in complex networks in a fast and effective manner is one of the useful ways to control the network communication process. Degree centrality and K-Shell decomposition are combined to integrate the global and local characteristics of the nodes, without depending on other parameters in the calculation. This effectively improves the shortcomings of poor discrimination by K-Shell decomposition and increases the resolution of node identification.
快速有效地识别复杂网络中的重要节点是控制网络通信过程的有效途径之一。将度中心性和K-Shell分解相结合,综合了节点的全局和局部特征,在计算中不依赖于其他参数。这有效改善了K-Shell分解识别能力差的缺点,提高了节点识别的分辨率。
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引用次数: 0
Real-time detection method for mobile network traffic anomalies considering user behavior security monitoring 考虑用户行为安全监控的移动网络流量异常实时检测方法
Pub Date : 2021-04-01 DOI: 10.1109/CBFD52659.2021.00010
Zhang Huabing, Ye Sisi, C. Xiaoming, Lin Zhida
The traditional network traffic anomaly detection method is based on the principle of feature extraction and matching for a large amount of abnormal traffic data to achieve traffic anomaly detection. Due to the fast changing speed of mobile networks, it is difficult to ensure the real-time and accuracy of the detection method simply by extracting traffic features. To address the above problems, the study considers the real-time detection method of mobile network traffic anomaly for user behavior security monitoring. User behavior data is captured based on the network usage data of users provided by mobile network providers. Protocol parsing and application identify user data packets and extract user behavior features. A Bayesian classifier is constructed and a HAST-NAD model is used to achieve real-time detection of network traffic anomalies. Simulation experimental results show that the highest detection time of the detection method is only 104s, and the detection accuracy of the method is better than the traditional detection method, and the detection effect is better.
传统的网络流量异常检测方法是基于对大量异常流量数据进行特征提取和匹配的原理来实现流量异常检测。由于移动网络的快速变化,单纯通过提取流量特征很难保证检测方法的实时性和准确性。针对上述问题,本研究考虑了移动网络流量异常实时检测方法,用于用户行为安全监控。用户行为数据是根据移动网络提供商提供的用户网络使用数据捕获的。协议解析和应用识别用户数据包,提取用户行为特征。构建贝叶斯分类器,利用ast - nad模型实现网络流量异常的实时检测。仿真实验结果表明,该检测方法的最高检测时间仅为104s,且该方法的检测精度优于传统检测方法,检测效果更好。
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引用次数: 0
Accelerate the innovative development of blockchain technology and industry 加快区块链技术和产业创新发展
Pub Date : 2021-04-01 DOI: 10.1109/CBFD52659.2021.00082
Lin Li
With the continuous improvement of China's macropolicies in the field of blockchain and the increasing industrial investment, the research and application of blockchain technology are changing with each passing day and develop vigorously, and the integrated application of blockchain technology plays an important role in the new technological innovation and industrial reform. The characteristics of blockchain, such as decentralization, tamper proof and traceability, enable it to play a vital role in many areas, such as finance, intelligent manufacturing, Internet of Things, supplychain management, digital asset trading, social governance and the people's livelihood services. Accelerating the innovative development of blockchain technology and industry is conducive to expanding the application fields and development prospects of blockchain technology, helping China to achieve a leading edge in global technological competition and promoting the high-quality development of China's economy and society.
随着中国在区块链领域宏观政策的不断完善和产业投入的不断加大,区块链技术的研究和应用日新月异,蓬勃发展,区块链技术的集成应用在新技术创新和产业变革中发挥着重要作用。区块链的去中心化、防篡改、可追溯等特性,使其在金融、智能制造、物联网、供应链管理、数字资产交易、社会治理、民生服务等诸多领域发挥着至关重要的作用。加快区块链技术和产业的创新发展,有利于拓展区块链技术的应用领域和发展前景,有利于中国在全球技术竞争中取得领先优势,推动中国经济社会高质量发展。
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引用次数: 0
Multi-directional market value analysis of films : Visual data processing based on Python 电影多向市场价值分析:基于Python的可视化数据处理
Pub Date : 2021-04-01 DOI: 10.1109/CBFD52659.2021.00031
Yiwei Hong, Su Zhou, Dejing Niu
This article will be based on the data of Douban and Maoyan platforms, using python to analyze the box office, investment amount, movie types, the popularity of actors and directors and ratings of movies to measure the market value of movies. According to the relationship reflected in the data, we found that the most important driving force influencing the market value of movies is famous actors, followed by famous directors. In addition, the amount of investment is also an important factor for movies, but it is not a necessary factor. In the end, we came to conclusion that in order to improve the market value of movies, we must first work hard on the content. In addition, the participation of famous actors and famous directors, and the increase in investment amount will also greatly increase the market value of the film.
本文将以豆瓣和猫眼平台的数据为基础,使用python分析电影的票房、投资额、电影类型、演员和导演的受欢迎程度以及电影的评分,来衡量电影的市场价值。根据数据反映的关系,我们发现影响电影市场价值的最重要驱动力是著名演员,其次是著名导演。此外,投资金额对电影来说也是一个重要的因素,但不是必要的因素。最后,我们得出结论,要想提高电影的市场价值,首先要在内容上下功夫。此外,著名演员和著名导演的参与,以及投资金额的增加也将大大增加电影的市场价值。
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引用次数: 0
Short-term load forecasting based on ELM combined model 基于ELM组合模型的短期负荷预测
Pub Date : 2021-04-01 DOI: 10.1109/CBFD52659.2021.00008
Yang Kunqiao, Jiang Jiandong
In order to accurately predict the short-term load, a combination forecasting model based on extreme learning machine is proposed. First, variational modal technology is used to decompose the original load sequence, and the appropriate number of modal components is obtained; secondly, according to the different performance characteristics of each modal, the time series and extreme learning machine model is used for prediction, and the improved bat algorithm is used to optimize the selection of parameters in the extreme learning machine; finally, the output value of the model built by each sub-sequence is reconstructed to obtain the final prediction result. Through the measured data, the effectiveness and accuracy of the combined forecasting model proposed in this paper are verified in load forecasting.
为了准确预测短期负荷,提出了一种基于极限学习机的组合预测模型。首先,采用变分模态技术对原始载荷序列进行分解,得到相应的模态分量数;其次,根据各模态的不同性能特点,采用时间序列和极限学习机模型进行预测,并采用改进的bat算法对极限学习机中的参数选择进行优化;最后,对各子序列构建的模型的输出值进行重构,得到最终的预测结果。通过实测数据,验证了本文提出的组合预测模型在负荷预测中的有效性和准确性。
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引用次数: 0
Customer churn model based on complementarity measure and random forest 基于互补度量和随机森林的客户流失模型
Pub Date : 2021-04-01 DOI: 10.1109/CBFD52659.2021.00026
Chen Zhang, Hong Li, Guangde Xu, Xuhui Zhu
How to prevent the loss of bank customers, especially the loss of high-quality customers, is a great concern of banks, for which an accurate churn prediction model is of great importance. The accuracy of the integrated classifier is better than that of a single classifier. Random forest is a kind of ensemble learning. Traditional random forest uses all decision trees for voting. Some poor decision trees will reduce the overall performance of random forests. To improve the performance of traditional random forest, the random forest based on complementarity measure is proposed. The decision trees in the forest are pruned using complementarity measure. We use the proposed method to predict bank customer churn. Firstly, affinity propagation clustering (AP clustering) algorithm is used for attribute selection. Then the improved random forest method is used to establish an early warning model of customer churn. Compared with the general churn prediction model, this model has higher accuracy.
如何防止银行客户的流失,特别是优质客户的流失,是银行非常关注的问题,一个准确的客户流失预测模型对银行客户流失预测具有重要意义。综合分类器的准确率优于单一分类器。随机森林是一种集成学习。传统的随机森林使用所有决策树进行投票。一些糟糕的决策树会降低随机森林的整体性能。为了提高传统随机森林的性能,提出了基于互补性测度的随机森林算法。利用互补性度量对森林中的决策树进行剪枝。我们使用所提出的方法来预测银行客户流失。首先,采用亲和传播聚类(AP聚类)算法进行属性选择;然后利用改进的随机森林方法建立客户流失预警模型。与一般的客户流失预测模型相比,该模型具有较高的预测精度。
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引用次数: 0
Game Analysis of "Blockchain+Supply Chain Finance" Mode in Empowering Small and Micro Enterprises’ Financing “区块链+供应链金融”模式赋能小微企业融资的博弈分析
Pub Date : 2021-04-01 DOI: 10.1109/CBFD52659.2021.00086
Lei Zhou, Xiao Ya Zhong, J. Liu, M. Xia
The integration of blockchain and supply chain provides new possibilities for solving the financing difficulties of small and micro enterprises (SMEs). This paper constructs dynamic evolutionary game models between financial institutions and SMEs as well as core firms and SMEs. Furthermore, the following conclusions were drawn by using MATLAB software for numerical simulation based on models combined with the example. Docking with blockchain platform is the dominant strategy of financial institutions. Blockchain can help SMEs make trustworthy decisions by promoting credit split circulation, improving financing efficiency, increasing default cost and reducing financing rate. In addition, through "network cooperation and credit incentive", "joint punishment for breach of trust" and "reasonable revenue sharing", the game equilibrium evolves toward the ideal state that financial institutions dare to lend, core firms and SMEs are "Double Trustworthy". Thus, the financing of SMEs is empowered by blockchain. Finally, according to the results of game analysis, suggestions that blockchain should be used to develop digital supply chain to meet the financing needs of SMEs are proposed.
区块链与供应链的融合,为解决小微企业融资难提供了新的可能。本文构建了金融机构与中小企业、核心企业与中小企业的动态演化博弈模型。基于模型结合实例,利用MATLAB软件进行数值模拟,得出以下结论:与区块链平台对接是金融机构的主导战略。区块链可以促进信用分割流通,提高融资效率,增加违约成本,降低融资率,帮助中小企业做出值得信赖的决策。此外,通过“网络合作与信用激励”、“失信联合惩戒”和“合理收益分享”,博弈均衡向金融机构敢于借贷、核心企业和中小企业“双重可信”的理想状态演化。因此,区块链赋予了中小企业融资权力。最后,根据博弈分析结果,提出利用区块链发展数字供应链以满足中小企业融资需求的建议。
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引用次数: 1
Minimizing Task Completion Time in the Cloud based on Random Neural Network 基于随机神经网络的云中任务完成时间最小化
Pub Date : 2021-04-01 DOI: 10.1109/CBFD52659.2021.00019
Yu Wang, Wu Tongtong
With the development of IoT and 5G, the number of devices accessing the Internet is increasing every day. While mobile edge computing effectively reduces the pressure on cloud centers, cloud centers still face the challenge of task scheduling and resource allocation for a large amount of SaaS applications. In this paper, the conditions for minimizing the average task completion time are derived by a simplified queuing model and an adaptive dynamic scheduling algorithm for minimizing the average task completion time is proposed in combination with stochastic neural networks, which is based on online measurements and takes up very little resources and computation. A diverse range of algorithms are tested in many different environments as a way to analyze algorithm performance. The simulation results show that our proposed algorithm is effective in reducing the average task completion time in a variety of environments.
随着物联网和5G的发展,接入互联网的设备数量每天都在增加。虽然移动边缘计算有效地减轻了云中心的压力,但云中心仍然面临着大量SaaS应用的任务调度和资源分配的挑战。本文通过简化的排队模型推导了任务平均完成时间最小化的条件,并结合随机神经网络提出了一种基于在线测量的任务平均完成时间最小化的自适应动态调度算法,该算法占用的资源和计算量很小。作为分析算法性能的一种方式,在许多不同的环境中测试了各种各样的算法。仿真结果表明,该算法能有效地缩短各种环境下的平均任务完成时间。
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引用次数: 0
Financial Application based on Virtual Reality 基于虚拟现实的金融应用
Pub Date : 2021-04-01 DOI: 10.1109/CBFD52659.2021.00084
Wencheng Bao
With the increasing influence of virtual reality and more and more devices supported, virtual reality has been widely applied in different areas. In this paper, we give a comprehensive introduction of the development of virtual reality as well as the prototype financial applications based on virtual reality. We also discuss the challenges when deploying these applications in the future.
随着虚拟现实的影响越来越大,支持的设备越来越多,虚拟现实在不同的领域得到了广泛的应用。本文全面介绍了虚拟现实的发展以及基于虚拟现实的金融应用原型。我们还讨论了将来部署这些应用程序时面临的挑战。
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引用次数: 0
An Empirical Analysis of the Motivation of Commercial Banks Holding Financial Derivatives to Hedging Risks 商业银行持有金融衍生品对冲风险动机实证分析
Pub Date : 2021-04-01 DOI: 10.1109/CBFD52659.2021.00094
Yu Chen
As a physical asset that financial derivatives can smooth the interest rate and foreign exchange risk of commercial banks through hedging transactions and provide services for their risk management. However, due to the last start of this application in China, the identification and supervision of risk types and holding motives in the market are still not perfect. Therefore, this paper studied this situation.The paper first analyzes several major development trends and development backgrounds of the financial derivatives market. Then, by summarizing the literature, it makes several relevant hypotheses in the empirical part, aiming at several factors that affect the holding of financial derivatives of commercial banks, and carries out multiple linear regression analysis on the hypotheses. Finally, through the hypothetical conclusions verified by collating the regression results, the paper provides some suggestions for the development of related fields in China.
金融衍生品作为一种实物资产,可以通过对冲交易平滑商业银行的利率和外汇风险,为商业银行的风险管理提供服务。但由于这一应用在国内起步较晚,市场对风险类型和持有动机的识别和监管尚不完善。因此,本文对这一情况进行了研究。本文首先分析了金融衍生品市场的几个主要发展趋势和发展背景。然后,在总结文献的基础上,在实证部分针对影响商业银行金融衍生品持有量的几个因素,提出了几个相关假设,并对假设进行了多元线性回归分析。最后,通过整理回归结果验证假设结论,对中国相关领域的发展提出建议。
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引用次数: 0
期刊
2021 International Conference on Computer, Blockchain and Financial Development (CBFD)
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