Proposal of a guide for the interpretation, simplification of the regulatory process and good tax compliance, case of digital taxpayers, influencers.

Influencers generate opinions in individuals through multiple virtual platforms, this phenomenon implies social influence that induces consumers to buy and direct these activities to the sponsorship of brands, which means monetary income for the influencer. Many of these incomes are not reported to the tax system, which causes evasion due to misinformation or lack of knowledge. Therefore, the need for a correct adaptation and interpretation of the Peruvian tax regulations for the payment of taxes on income received by this segment of taxpayers was observed. The purpose of this research was a guide that interprets, simplifies the processes and provides a regulatory framework for tax compliance for domiciled and non-domiciled influencers. The tax guide was designed thanks to the adaptation of the Scribber methodology and consisted of 4 steps: Familiarization, coding, theme generation, defining themes. The guide was organized in level 01, describing how to achieve the tax obligation in the sector of digital taxpayers influencers, level 02, where the activities described by the regulations are mentioned and level 3, tax procedures carried out by the tax administration to influencers. This guide is an aid to define the category that attributes the taxpayer's tax payment method. By identifying the tax categorization code according to the type of activity. It identifies the key factors to be able to interpret and adapt the law to the influencer's activities.

Discrimination of foreign bodies in quinoa (Chenopodium quinoa Willd.) grains using convolutional neural networks with a transfer learning approach.

The rising interest in quinoa (Chenopodium quinoa Willd.) is due to its high protein content and gluten-free condition; nonetheless, the presence of foreign bodies in quinoa processing facilities is an issue that must be addressed. As a result, convolutional neural networks have been adopted, mostly because of their data extraction capabilities, which had not been utilized before for this purpose. Consequently, the main objective of this work is to evaluate convolutional neural networks with a learning transfer for foreign bodies identification in quinoa samples. For experimentation, quinoa samples were collected and manually split into 17 classes: quinoa grains and 16 foreign bodies. Then, one thousand images were obtained from each class in RGB space and transformed into four different color spaces (L*a*b*, HSV, YCbCr, and Gray). Three convolutional neural networks (AlexNet, MobileNetv2, and DenseNet-201) were trained using the five color spaces, and the evaluation results were expressed in terms of accuracy and F-score. All the CNN approaches compared showed an F-score ranging from 98% to 99%; both color space and CNN structure were found to have significant effects on the F-score. Also, DenseNet-201 was the most robust architecture and, at the same time, the most time-consuming. These results evidence the capacity of CNN architectures to be used for the discrimination of foreign bodies in quinoa processing facilities.

Competency analysis based on accounting career anchors using clustering techniques.

This research work aims to identify the prevalent anchors in the professional accounting career using the Schein scale and to describe the prevalent anchors by defining the values, attitudes, aptitudes, skills, and interests. Career anchors are defined by the competence, motivation, and values a person has to perform a particular job in an organization and are present throughout their working life. When determining the soft and hard competencies of the professional profile, universities must consider the career anchors essential for graduates' work performance. To determine which anchors dominate the competencies of the graduate profile, two universities in Latin America with a degree in accounting were selected. The study was organized in two stages: first, the operationalization of the research was conducted, including the description of the instrument through the application of 40 questions divided into Schein's eight anchors. Samples were selected based on the convenience of the authors: one university in Peru and another in Colombia. The sample includes all students enrolled in the accounting major, and the data were coded and processed. In the second stage, data analysis was performed by grouping parameters, analysis of variance, explanatory analysis using a test for the best clustering algorithm, statistical testing, and discussion of the findings. The predominant anchors in the two universities are creativity, entrepreneurship, and lifestyle. The selected universities placed considerable emphasis on training future accountants with an innovative spirit, integrity, and social commitment without neglecting the professional requirements. This approach allows students to undertake challenges and new businesses in their field of work.