A Tax Evasion Detection Method Based on Positive and Unlabeled Learning with Network Embedding Features

2020 ◽  
pp. 140-151
Author(s):  
Lingyun Mi ◽  
Bo Dong ◽  
Bin Shi ◽  
Qinghua Zheng
Keyword(s):  
Tax Evasion ◽  
Detection Method ◽  
Network Embedding ◽  
Positive And Unlabeled Learning

TEDM-PU: A Tax Evasion Detection Method Based on Positive and Unlabeled Learning

2019 ◽  
Author(s):  
Yingchao Wu ◽  
Qinghua Zheng ◽  
Yuda Gao ◽  
Bo Dong ◽  
Rongzhe Wei ◽  
...  
Keyword(s):  
Tax Evasion ◽  
Detection Method ◽  
Positive And Unlabeled Learning

scholarly journals ATTENet: Detecting and Explaining Suspicious Tax Evasion Groups

2019 ◽  
Author(s):  
Qinghua Zheng ◽  
Yating Lin ◽  
Huan He ◽  
Jianfei Ruan ◽  
Bo Dong
Keyword(s):  
Random Forest ◽  
Social Relationships ◽  
Tax Evasion ◽  
Network Embedding ◽  
Random Forest Algorithm ◽  
Embedding Method ◽  
Interactive Tools ◽  
Basic Features ◽  
Economic Behaviors ◽  
Affiliated Transaction

In this demonstration, we present ATTENet, a novel visual analytic system for detecting and explaining suspicious affiliated-transaction-based tax evasion (ATTE) groups. First, the system constructs a taxpayer interest interacted network, which contains economic behaviors and social relationships between taxpayers. Then, the system combines basic features and structure features of each group in the network with network embedding method structure2Vec, and then detects suspicious ATTE groups with random forest algorithm. Last, to explore and explain the detection results, the system provides an ATTENet visualization with three coordinated views and interactive tools. We demonstrate ATTENet on a non-confidential dataset which contains two years of real tax data obtained by our cooperative tax authorities to verify the usefulness of our system.

scholarly journals Tax evasion risk management using a Hybrid Unsupervised Outlier Detection method

2022 ◽  
pp. 116409
Author(s):  
Miloš Savić ◽  
Jasna Atanasijević ◽  
Dušan Jakovetić ◽  
Nataša Krejić
Keyword(s):  
Risk Management ◽  
Outlier Detection ◽  
Tax Evasion ◽  
Detection Method ◽  
Unsupervised Outlier Detection

Solid-phase immunoelectron microscopy for rapid diagnosis of enteroviruses

1984 ◽  
Vol 42 ◽  
pp. 226-227 ◽  
Author(s):  
K. Pegg-Feige ◽  
F. W. Doane
Keyword(s):  
Electron Microscopy ◽  
Cell Culture ◽  
Immunoelectron Microscopy ◽  
Detection Method ◽  
Solid Phase ◽  
Protein A ◽  
Plant Viruses ◽  
Rapid Diagnosis ◽  
Virus Diagnosis ◽  
Antiviral Antibody

Immunoelectron microscopy (IEM) applied to rapid virus diagnosis offers a more sensitive detection method than direct electron microscopy (DEM), and can also be used to serotype viruses. One of several IEM techniques is that introduced by Derrick in 1972, in which antiviral antibody is attached to the support film of an EM specimen grid. Originally developed for plant viruses, it has recently been applied to several animal viruses, especially rotaviruses. We have investigated the use of this solid phase IEM technique (SPIEM) in detecting and identifying enteroviruses (in the form of crude cell culture isolates), and have compared it with a modified “SPIEM-SPA” method in which grids are coated with protein A from Staphylococcus aureus prior to exposure to antiserum.

Pengaruh Konvergensi International Financial Reporting Standards (Ifrs) Terhadap Penghindaran Pajak(Tax Avoidance)

2018 ◽  
Vol 26 (2) ◽  
pp. 158-169
Author(s):  
Umi Wahidah ◽  
Sri Ayem
Keyword(s):  
Financial Reporting ◽  
Tax Evasion ◽  
Stock Exchange ◽  
Control Variable ◽  
Tax Avoidance ◽  
Sampling Technique ◽  
International Financial Reporting Standards ◽  
Reporting Standards ◽  
Control Variables ◽  
Financial Reporting Standards

This research aimed to examine the effect of the convergence of International Financial Reporting Standards (IFRS) on tax avoidance on companies listed in Indonesia Stock Exchange. Tax avoidance that used in this research was Cash Efective Tax Rate (CETR). This research is also use the control variable to get other different influence that different such as CSR, size, and earning management (EM. This research used populations sector of transport service companies that listed in Indonesia Stock Exchange. The data of this research taken from secondary data that was from the Indonesia Stock Exchange in the form of Indonesian Capital Market Directory (ICMD) and the annual report of the company 2011-2015. The method of collecting sample was purposive sampling technique, the population that to be sampling in this research was populations that has the criteria of a particular sample. Companies that has the criteria of the research sample as many as 78 companies. The method of analysis used in this research is multiple regression analysis. Based on regression testing shows that the convergence of International Financial Reporting Standards (IFRS) has a positiveand significant impact on tax evasion. This shows that IFRS convergence actually improves tax evasion practices. The control variables of firm size and earnings management also significantly influence the application of IFRS in improving tax avoidance practices, while CSR control variables have no role in convergence IFRS in improving tax evasion practice.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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