A Detection Method for Abnormal Transactions in E-Commerce Based on Extended Data Flow Conformance Checking

With the development of smart devices and mobile communication technologies, e-commerce has spread over all aspects of life. Abnormal transaction detection is important in e-commerce since abnormal transactions can result in large losses. Additionally, integrating data flow and control flow is important in the research of process modeling and data analysis since it plays an important role in the correctness and security of business processes. This paper proposes a novel method of detecting abnormal transactions via an integration model of data and control flows. Our model, called Extended Data Petri net (DPNE), integrates the data interaction and behavior of the whole process from the user logging into the e-commerce platform to the end of the payment, which also covers the mobile transaction process. We analyse the structure of the model, design the anomaly detection algorithm of relevant data, and illustrate the rationality and effectiveness of the whole system model. Through a case study, it is proved that each part of the system can respond well, and the system can judge each activity of every mobile transaction. Finally, the anomaly detection results are obtained by some comprehensive analysis.

Volunteer Love Education Learning and Self-Management Model

The “FU Chuan Charity Foundation” uses the statutory curriculum of the Department of Social Sciences to implement education on volunteer love and self-management for volunteer students. The statutory courses of the Department of Social Sciences belong to the “Interdisciplinary Curriculum Integration Model”, the “FU Chuan Charity Foundation” opened the “FU Chuan Charity Bachelor Class”, which has been operated by volunteers for 8 years, invisibly in the original “education and learning philosophy”. The outlines of 7 groups of education and learning models gradually emerged, so they were named “Education and Learning Models for the Bachelor of Evangelical Compassion”: including (1) the integration model of old and new students, (2) the cross-age and multi-group co-learning model, (3) Sharing mode in different places and different industries, (4) Social welfare resource co-study mode, (5) Potential mode of voluntary service, (6) Intervention response effect mode, (7) No-handling property mode. The operation of this model has changed from “Originally run a school for the Foundation” to “Run a school for the Friends Association”, and at least assist students in 5 changes: (1) Attitude to study, from “wait and see trial” to “specialized reading” “, then change to “Determined to Grad.” (2) The learning factor changes from “convenient time” to “professional needs”, then to “equal attention to time and professionalism”, and then to “desire to graduate”. (3) The average number of courses taken has changed from “inconsistent courses” to “concentrated courses” and then to “intensive courses”. (4) Student volunteer habits have changed from “adjustment to ups and downs” to “balanced and stable”. (5) The willingness of students to volunteer has changed from “rare concepts” to “stable learning” and then to “dare to face the test of the epidemic” [1].

Big Data Spatio-Temporal Correlation Analysis and LRIM Model Based Targeted Poverty Alleviation through Education

There are many factors affecting poverty, among which education is an important one. Firstly, from the perspective of digital statistics, this research quantitatively analyzes the correlation between average education years (AEY) and Gross Domestic Product per capita (GDP/C), and finds that there is a significant positive correlation between AEY and GDP/C in provinces of China. Furthermore, from the perspective of spatial distribution and geostatistics, this research analyzes the correlation between AEY and the distribution of poor counties, revealing the inherent connection between education and poverty. Based on the data processing of nighttime light remote sensing images, this research adopts the machine learning method of random forest to extract the distribution status of spatio-temporal sequences for poor counties. Through the analysis, it is found that poor counties are characterized by centralized distribution and spatial autocorrelation spatially, and the number of poor counties decreases year by year in temporal evolution. On this basis, we analyze the correlation between education levels and the distribution of poor counties. It is found that, on the spatial scale, AEY in poor counties is relatively low, while AEY in non-poor counties is relatively high, showing a significant negative correlation between the two. On the temporal scale, the number of poor counties gradually decreased from 2000 to 2010, and at the same time, the education levels of poor counties also gradually improved. Finally, from the perspective of improving education levels to promote poverty elimination, we analyze the main factors affecting education using Principal Component Analysis (PCA) and other methods and obtain a regression model. This research proposes the Linear and Residual Integration Model (LRIM) to more accurately predict AEY in each province in 2020 based on historical data, and identifies the regions with low AEY as key regions for targeted poverty alleviation through education (TPAE) in the future. This research provides a decision-making basis to achieve TPAE means, helping to achieve the victory of the national education poverty elimination battle.

Proposal for a Standard Architecture for the Integration of Clinical Information Systems in a Complex Hospital Environment

The evolution of technology in clinical environments increases the level of precision in patient care, as well as optimizes the management of healthcare centers. However, the need to have information systems that are more sophisticated and require interoperability between them means that a great deal of effort has to be made to assume the maintenance and scalability of the systems. Therefore, a proposal for a standard information model for the integration of clinical systems in a healthcare environment is presented. In order to elaborate the model, an analysis of the functional needs of the different clinical areas of a clinical environment is made based on the information systems that make up the system and application map. An evaluation of the technical requirements and the technological solutions that can satisfy these requirements is also carried out, delving into the different technical alternatives that allow the exchange of information. From the analysis carried out, an integration model capable of covering the needs that arise in clinical environments with a high level of complexity is obtained, also allowing the continuous evolution of the systems that make up the model, along with the incorporation of new systems. Although the model presented may fully cover the expectations raised, the rapid evolution in terms of both functional needs and technical aspects makes it necessary to continuously monitor and evaluate the model, in order to adapt it to the needs that arise.

Integration of proprioceptive and visual feedback during online control of reaching

Visual and proprioceptive feedback both contribute to perceptual decisions, but it remains unknown how these feedback signals are integrated together or consider factors such as delays and variance during online control. We investigated this question by having participants reach to a target with randomly applied mechanical and/or visual disturbances. We observed that the presence of visual feedback during a mechanical disturbance did not increase the size of the muscle response significantly but did decrease variance, consistent with a dynamic Bayesian integration model. In a control experiment we verified that vision had a potent influence when mechanical and visual disturbances were both present but opposite in sign. These results highlight a complex process for multi-sensory integration, where visual feedback has a relatively modest influence when the limb is mechanically disturbed, but a substantial influence when visual feedback becomes misaligned with the limb.

Evolution and Construction Differentiation Pattern of African Railway Network

Since the new century, countries in Africa have started a new round of rail network planning and construction which brings the completed different features together with the spatial organization of the railway network during the colonial period. Along with the strategic layout of “going out” with China’s railways, the organizational structure of the African railway network will make a tremendous change for the construction market, network organization, and gauge structure of the African railways. Based on the literature reviews, we analyzed and forecasted the evolution of railway network in Africa and discussed the spatial differentiation of the future construction market of the railways from the view of country and enterprise. The results show that the development of the African railway networks will experience three stages: 1850–1960, 1960–2010 and 2010–2050, and that the organization pattern of the African railway network has evolved from the “Hinterland-Port” model to the “Continental Integration” model. The development of railway technical standards tends to be integrated, the gauge type is changed from complicated to single, the gauge distribution is changed from broken to uniform. The contractor countries of the railway changed from English-French dominated to China dominated. The application of railway technical standards is influenced by technology dependence and path dependence and is mainly reflected in the two characteristics of “Chinese standard implantation” and “local standard retention”. The contractor enterprises of railway have a monopoly on the market of a country, CCECC and CRCC are leading, and the contractor enterprises are spatially characterized by four spatial distribution modes: single, continuous, jumping and comprehensive.

Self-Expansion or Internalization as the Two Processes of Vertical Integration: What Informs the Decision?

In vertical integration literature, the two processes leading to vertical integration, namely, (1) self-expansion of the scope of activities based on internal capabilities and (2) internalization of activities with external capabilities have not been distinguished. However, using internal capabilities or incorporating external capabilities is an alternative decision for managers and distinguishing them is crucial in practice. The purpose of this study is to distinguish self-expansion separated from internalization and to explain systematically when they likely occur. This study develops a unique vertical integration model by integrating transaction cost economics and the capability approach. With the model, we systematically analyzed the occurrence of (1) self-expansion and (2) internalization. Results reveal that the firm prefers self-expansion to internalization if it is easy to build the capabilities internally or difficult to procure them from outside the firm and if the costs of acquiring a firm or business with the required capabilities or the governance costs of the activities with external capabilities are high and vice versa. Our model leads to more understanding of vertical integration.

Morphology Decoder to Predict Heterogeneous Rock Permeability with Machine Learning Guided 3D Vision

<p><a></a><a>Permeability has a dominant influence on the flow behavior of a natural fluid, and without proper quantification, biological fluids (Hydrocarbons) and water resources become waste. During the first decades of the 21^st century, permeability quantification from nano-micro porous media images emerged, aided by 3D pore network flow simulation, primarily using the Lattice Boltzmann simulator. Earth scientists realized that the simulation process holds millions of flow dynamics calculations with accumulated errors and high computing power consumption. Therefore, accuracy and efficiency challenges obstruct planetary exploration. To effic­­­iently, consistently predict permeability with high quality, we propose the Morphology Decoder. It is a parallel and serial flow reconstruction of machine learning-driven semantically segmented heterogeneous rock texture images of 3D X-Ray Micro Computerized Tomography (μCT) and Nuclear Magnetic Resonance (MRI). For 3D vision, we introduce controllable-measurable-volume as new supervised semantic segmentation, in which a unique set of voxel intensity corresponds to grain and pore throat sizes. The morphology decoder demarks and aggregates the morphologies' boundaries in a novel way to quantify permeability. The morphology decoder method consists of five novel processes, which we describe in this paper, these novel processes are (1) Geometrical: 3D Permeability Governing Equation, (2) Machine Learning: Guided 3D Properties Recognition of Rock Morphology, (3) Analytical: 3D Image Properties Integration Model for Permeability, (4) Experimental: MRI Permeability Imager, and (5) Morphology Decoder (the process that integrates the other four novel processes).</a></p>

Morphology Decoder to Predict Heterogeneous Rock Permeability with Machine Learning Guided 3D Vision

<p><a></a><a>Permeability has a dominant influence on the flow behavior of a natural fluid, and without proper quantification, biological fluids (Hydrocarbons) and water resources become waste. During the first decades of the 21^st century, permeability quantification from nano-micro porous media images emerged, aided by 3D pore network flow simulation, primarily using the Lattice Boltzmann simulator. Earth scientists realized that the simulation process holds millions of flow dynamics calculations with accumulated errors and high computing power consumption. Therefore, accuracy and efficiency challenges obstruct planetary exploration. To effic­­­iently, consistently predict permeability with high quality, we propose the Morphology Decoder. It is a parallel and serial flow reconstruction of machine learning-driven semantically segmented heterogeneous rock texture images of 3D X-Ray Micro Computerized Tomography (μCT) and Nuclear Magnetic Resonance (MRI). For 3D vision, we introduce controllable-measurable-volume as new supervised semantic segmentation, in which a unique set of voxel intensity corresponds to grain and pore throat sizes. The morphology decoder demarks and aggregates the morphologies' boundaries in a novel way to quantify permeability. The morphology decoder method consists of five novel processes, which we describe in this paper, these novel processes are (1) Geometrical: 3D Permeability Governing Equation, (2) Machine Learning: Guided 3D Properties Recognition of Rock Morphology, (3) Analytical: 3D Image Properties Integration Model for Permeability, (4) Experimental: MRI Permeability Imager, and (5) Morphology Decoder (the process that integrates the other four novel processes).</a></p>

An Integration Model for Text Classification using Graph Convolutional Network and BERT

Recently, Graph Convolutional Neural Network (GCN) is widely used in text classification tasks, and has effectively completed tasks that are considered to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make full use of context-dependent information in text classification, and cannot capture local information. The Bidirectional Encoder Representation from Transformers (BERT) has been shown to have the ability to capture the contextual information in a sentence or document, but its ability to capture global information about the vocabulary of a language is relatively limited. The latter is the advantage of GCN. Therefore, in this paper, Mutual Graph Convolution Networks (MGCN) is proposed to solve the above problems. It introduces semantic dictionary (WordNet), dependency and BERT. MGCN uses dependency to solve the problem of context dependence and WordNet to obtain more semantic information. Then the local information generated by BERT and the global information generated by GCN are interacted through the attention mechanism, so that they can influence each other and improve the classification effect of the model. The experimental results show that our model is more effective than previous research reports on three text classification data sets.