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Proceedings of the 16th Economics & Finance Conference, Prague最新文献

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EVALUATING THE IMPACT OF GOVERNMENT SOCIAL PROTECTION ON HOUSEHOLDS’ WELFARE DURING THE COVID-19 PANDEMIC IN SOUTH AFRICA 评估2019冠状病毒病大流行期间政府社会保护对南非家庭福利的影响
Pub Date : 1900-01-01 DOI: 10.20472/efc.2022.016.018
Sheunesu Zhou, A. Ayandibu, T. Chimucheka, M. Masuku
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引用次数: 0
PATENTING - A COST MANAGEMENT PERSPECTIVE 专利——成本管理的视角
Pub Date : 1900-01-01 DOI: 10.20472/efc.2022.016.010
Ulrike Michel-Schneider
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引用次数: 0
"THE MONTAGE OF ATTRACTIONS" IN CRYPTO ECOSYSTEM: FROM ZERO INTRINSIC VALUE TO A BUBBLE PRICE 加密生态系统中的“吸引力蒙太奇”:从零内在价值到泡沫价格
Pub Date : 1900-01-01 DOI: 10.20472/efc.2022.016.008
O. Khon
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引用次数: 0
A REVIEW OF KNOWLEDGE GRAPH AND GRAPH NEURAL NETWORK APPLICATION 知识图与图神经网络的应用综述
Pub Date : 1900-01-01 DOI: 10.20472/efc.2022.016.015
E. Xhumari, Suela Maxhelaku, Endri Xhina
: Many learning activities include working with graph data, which offers a wealth of relational information between parts. Modeling physical systems, learning molecular fingerprints, predicting protein interfaces, and diagnosing illnesses all need the use of a model that can learn from graph inputs. In other fields, such as learning from non-structural data such as texts and images, reasoning on extracted structures (such as phrase dependency trees and image scene graphs) is a major topic that requires graph reasoning models. Graph neural networks (GNNs) are neural models that use message transmission between graph nodes to represent graph dependency. Variants of GNNs have recently showed ground-breaking performance on a variety of deep learning tasks. This paper represents a review of the literature on Knowledge Graphs and Graph Neural Networks, with a particular focus on Graph Embeddings and Graph Neural Networks applications as a powerful tool for organizing structured data and making sense of unstructured data, which can be applied to a variety of real-world problems.
许多学习活动包括使用图形数据,它提供了零件之间丰富的关系信息。建模物理系统、学习分子指纹、预测蛋白质界面和诊断疾病都需要使用可以从图形输入中学习的模型。在其他领域,例如从非结构化数据(如文本和图像)中学习,对提取的结构(如短语依赖树和图像场景图)进行推理是需要图推理模型的主要主题。图神经网络(gnn)是利用图节点之间的消息传递来表示图依赖关系的神经模型。gnn的变体最近在各种深度学习任务上显示出突破性的性能。本文对知识图和图神经网络的文献进行了回顾,特别关注图嵌入和图神经网络作为组织结构化数据和理解非结构化数据的强大工具的应用,可以应用于各种现实世界的问题。
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引用次数: 0
THE ECB’S FINANCIAL STABILITY IMPACT ON CREDIT DEFAULT SWAPS MARKET 欧洲央行的金融稳定对信用违约掉期市场的影响
Pub Date : 1900-01-01 DOI: 10.20472/efc.2022.016.001
Georgios Alexopoulos
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引用次数: 0
FORECASTING AS A FUNCTION OF SECURITY MANAGEMENT 预测作为安全管理的一个功能
Pub Date : 1900-01-01 DOI: 10.20472/efc.2022.016.003
Oliver Bakreski, Sergej Cvetkovski, Leta Bardjieva Miovska
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引用次数: 0
FACTORS OF FOREIGN DIRECT INVESTMENT INWARDS: THE CASE OF SAUDI ARABIA 外来直接投资因素:以沙特阿拉伯为例
Pub Date : 1900-01-01 DOI: 10.20472/efc.2022.016.002
M. Alharthi
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引用次数: 0
THE INTEGRATION OF LIFE AND NON-LIFE INSURANCE IN FINANCIAL INCLUSION INDEX 金融普惠指数中寿险与非寿险的整合
Pub Date : 1900-01-01 DOI: 10.20472/efc.2022.016.016
Shen Yap, H. Lee, Ping-Xin Liew
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引用次数: 0
TEACHING TRICKS & TOOLS: A CASE OF HIGHER EDUCATION DURING COVID-19 PANDEMIC 教学技巧与工具:以COVID-19大流行期间的高等教育为例
Pub Date : 1900-01-01 DOI: 10.20472/efc.2022.016.007
S. Khahro, Yasir Javed, Z. Memon, Imran Hussain
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引用次数: 0
HIGH SCHOOL PRINCIPAL AS PROCESS MANAGER 高中校长担任流程经理
Pub Date : 1900-01-01 DOI: 10.20472/efc.2022.016.012
E. Urbanová, J. Šafránková
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引用次数: 0
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Proceedings of the 16th Economics & Finance Conference, Prague
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