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In this paper, we report the experience of a mathematics teacher educator, who is a mathematician, while teaching the Euclidean Division Algorithm Theorem in a Number Theory course for prospective mathematics teachers. Considering that the knowledge of a Mathematics teacher is specialized, from the perspective of the Mathematics Teachers' Specialised Knowledge model, we intended to identify which knowledge is mobilized when the teacher educator addresses this algebraic result. Some activities were conducted by the professor in an attempt to understand how the prospective teachers performed the division of integers before, during, and after knowing the theorem; our analysis focuses on these different moments. Regarding the prospective teachers' knowledge, it was possible to observe, primarily, knowledge related to procedures involving the algorithm. However, throughout the conducted activities, they were able to establish different connections concerning the Euclidean Division Algorithm Theorem. About the teacher educator, we emphasize that his mathematical and pedagogical knowledge, combined with the goal of effectively prepare future Mathematics teachers, has the potential to promote specialized knowledge in the prospective teachers regarding the subject matter. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has become increasingly present in human life, pointing to an integrated future with the real and the virtual walking side by side. Inserted as an AI field, Machine Leaning operates with a set of tools, called algorithms, that allow performing activities in almost every area of knowledge, whether in Biology, Math and Science or Humanities. An application of the algorithms is in the area of credit card fraud detection, which causes financial institutions and society to lose billions of dollars every year. In this sense, this work aimed to identify how Machine Learning has contributed to this financial and social problem. For this proposal, it was used the Systematic Bibliographic Review, from which it was obtained primary diagnoses on the areas of Machine Learning, Fraud and Credit Card Fraud, in order to arrive at a thorough portrait of the subject. As a source of research, it was used in the Web of Science portal, with searches centered on the period from 2008 to 2018. The results show that, both in the subject of Machine Learning and fraud, most of the works refer to areas of Medicine & Biomedicine. In the integration between “Machine Learning” and credit card fraud, five papers were found that, when fully evaluated, proved to have good fraud detection power. There is, however, a way to go that allows comparing the methods employed from a common database. [ABSTRACT FROM AUTHOR]

This paper presents an exploratory study of image banks, specifically Getty Images and Shutterstock, analyzing whether their tagging processes reveal algorithmic forms of reproducing colonial mentalities about black women. From Patricia Hill Collins' concept of "controlling images," Jezebel, Mammy, and Sapphire images were examined, understanding that these stereotypes of black female subjectivity underscore narratives of subordination, exploitation, and animalization in the service of coloniality. While the results show specificities, primarily due to the COVID-19 pandemic, we found that algorithmic coloniality becomes apparent in these banks, which frequently tag the images of black women under colonial representation standards of labor, sexuality, and aggressiveness. [ABSTRACT FROM AUTHOR]

In this theoretical articulation based on bibliographic research, the culture of intolerance, driven by the political context of hyperneoliberalism, is linked to the imaginary present on algorithmic platforms, where a fundamental mode of governance is practiced today. While not ignoring past manifestations of intolerance on the Internet, this paper intends to demonstrate how these platforms are environments even more favorable for such manifestations, given the peculiarities of their technology and their business model. To this end, certain aspects of their operation, classified as arena of attention, uneven omnimediation, calibrated exposure and flexible veridiction, are related to dispositions that Lacan associates with the imaginary and aggressiveness - respectively, narcissism, narcissistic identification with leaders, segregation and paranoia. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the relationship between data practice and algorithmic actions on digital platforms. For this purpose, we do a descriptive analysis of Instagram documents that are addressed to its users, identifying what suggests action and practical consequence of digital materialities in terms of production, sharing and collection. Through them we identify the centrality of experience in this kind of informational construct developed in relation to datafication processes - which we call algorithmic experience. From a pragmatist and neomaterialist perspective, we suggest following this experience to understand the practical differences produced in the use of digital platforms and identifying relevant interactions in situated practices. [ABSTRACT FROM AUTHOR]

Algorithms have become the focus of discussions on technology whether involving the role of social networks in our everyday life or the promise of a generalized coding of various occupations. This paper intends to unravel the importance of algorithms for capitalism from the advent of the Third Industrial Revolution in three functions: (1) to expand the everyday life colonization by the market and by the State, a task formerly exercised only by the cultural industry, (2) increase the limits of the subsumption of labor by capital, and (3) improving existing forms of precarious work. Finally, we present possible solutions for working class organization of the working in the contemporary scenario of precariousness and withdrawal of labor rights. [ABSTRACT FROM AUTHOR]