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E-commerce has grown considerably in Latin America in recent years due to the COVID-19 pandemic. E-commerce users in English-speaking and Chinese-speaking countries have web-based tools to compare the prices of products offered by various retailers. The task of product comparison is known as entity matching in the data-science domain. This paper proposes the first entity-matching system for product comparison in Spanish-speaking e-commerce. Given the lack of uniformity of e-commerce sites in Mexico, we opted for a bimodal entity-matching system that uses the image and textual description of products from two of the largest e-commerce stores in Mexico. State-of-the-art techniques in natural language processing and machine learning were used to develop this research. The resulting system achieves F1 values of approximately 80%, representing a significant step towards consolidating a product-matching system in Spanish-speaking e-commerce. [ABSTRACT FROM AUTHOR]

Measures of teaching are used internationally to understand and improve quality in early education with little consideration for ecological validity. In this study, we analyze videos gathered in 58 K-1 classrooms in Central Mexico to evaluate the validity and reliability of scores from an observational tool developed in the U.S. and used internationally: the Classroom Assessment Scoring System (CLASS). We use mixed methods to examine ecological validity to propose a revised, better-fitting three-factor solution of the CLASS in Mexico: Emotional Support (Positive Climate, Negative Climate), Social Relationships for Teaching (Teacher Sensitivity, Instructional Learning Formats, Behavior Management, Productivity), and Instructional Interactions (Regard for Student Perspectives, Concept Development, Quality of Feedback, Language Modeling). Generalizability study findings demonstrate moderate reliability across three sources of measurement error: raters, days, and video segments. These findings support the need for a 'unified approach to validity' to develop, adapt, and refine measures of teaching as a basis for enhancing teacher development and teaching quality in early education in Mexico and throughout Latin America.

The problem of gender-based violence in Mexico has been increased considerably. Many social associations and governmental institutions have addressed this problem in different ways. In the context of computer science, some effort has been developed to deal with this problem through the use of machine learning approaches to strengthen the strategic decision making. In this work, a deep learning neural network application to identify gender-based violence on Twitter messages is presented. A total of 1,857,450 messages (generated in Mexico) were downloaded from Twitter: 61,604 of them were manually tagged by human volunteers as negative, positive or neutral messages, to serve as training and test data sets. Results presented in this paper show the effectiveness of deep neural network (about 80% of the area under the receiver operating characteristic) in detection of gender violence on Twitter messages. The main contribution of this investigation is that the data set was minimally pre-processed (as a difference versus most state-of-the-art approaches). Thus, the original messages were converted into a numerical vector in accordance to the frequency of word's appearance and only adverbs, conjunctions and prepositions were deleted (which occur very frequently in text and we think that these words do not contribute to discriminatory messages on Twitter). Finally, this work contributes to dealing with gender violence in Mexico, which is an issue that needs to be faced immediately. [ABSTRACT FROM AUTHOR]