Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

A pesar de ser Julia Ferrer una figura significativa en la intelectualidad peruana del medio siglo, su obra ha caído en el olvido. El objetivo de esta investigación ha sido reivindicar la obra de Ferrer desde el estudio de sus dos únicos poemarios, Imágenes porque sí (1958) y La olvidada lección de las cosas olvidadas (1966). En primer lugar, se estudia Imágenes porque sí a la luz del contexto político y poético del medio siglo y las influencias literarias presentes en el libro. Después, se analiza La olvi­dada lección de las cosas olvidadas como un ejemplo de consolidación de la voz poética de la autora. Se presta especial atención a temas recurren­tes en su obra, como el tiempo, el olvido y el cuestionamiento de un lenguaje establecido. A través de este estudio, se ha buscado no solo recuperar la relevancia de Julia Ferrer en la literatura peruana, sino también abrir posibles líneas de investigación sobre su obra.

En este trabajo analizamos el proceso de heroización del lingüista alemán Rodolfo Lenz (1863-1938) y su relación con la constitución del campo de los estudios del lenguaje en Chile. El material analizado corresponde a un conjunto de discursos biográficos datados entre 1920 y 1938. Como marco interpretativo, recurrimos al enfoque glotopolítico, a los estudios de historia cultural de la ciencia y a la sociología de los campos disciplinares. Proponemos entender este proceso como una operación colectiva que sirve para consolidar el campo de los estudios científicos del lenguaje en Chile, que venía emergiendo desde la última década del siglo xix y que, con la heroización de Lenz, consigue un centro o punto de referencia en torno al cual se establecen valores y posiciones, así como cobran sentido estrategias e intereses.

In an increasingly digital world, protecting personal data has become a pressing global issue, prompting the creation of complex regulations aimed at safeguarding individuals' privacy. However, achieving compliance with these frameworks poses significant challenges for organizations, requiring a methodical and well-structured approach. In this context, OntoPriv was developed as a legal ontology tailored to support compliance with Ecuador's Organic Law on Personal Data Protection (LOPDP). OntoPriv serves as a systematic framework that not only clarifies legal obligations but also provides tools to strengthen privacy practices and encourage accountability. This extended work goes a step further by aligning OntoPriv with the Data Privacy Vocabulary (DPV), an internationally recognized W3C standard. This alignment significantly enhances OntoPriv's modularity, semantic depth, and interoperability, effectively connecting local and global data privacy frameworks. By integrating DPV, OntoPriv incorporates standardized concepts such as legal bases, purposes, and risk mitigation strategies. This ensures cross-jurisdictional compatibility and addresses prior challenges like limited interoperability and insufficient semantic connections. This integration positions OntoPriv as a powerful tool that facilitates automated compliance, comprehensive risk assessments, and improved data governance while fostering a shared understanding of privacy standards across different regions. This article discusses OntoPriv's development, the methodology for its alignment with DPV, and its implications for advancing data privacy compliance in Ecuador and beyond.

A diverse workforce is one of the most important assets for modern organizations. In this sense, the integration of workers with disabilities is both an opportunity in terms of staff diversity and of corporate social responsibility, and, in many cases, a legal duty. Successfully achieving this integration can nevertheless be challenging, and requires taking into account different aspects, one of them is achieving a fair and efficient task distribution. In the literature, many task assignment models have been proposed for distributing and managing tasks within a work team, usually aiming to optimize productivity and efficiency. These models take into account workers abilities, experience and work charge, as well as the characteristics of the tasks, in order to assign each worker the most appropriate task. When the workforce includes people with disabilities, the assignment models must be adaptable enough to guarantee the full integration of all the team members. In this paper we develop a mathematical programming model for task assignment in the context of hiring people with disabilities in service organizations. We discuss a practical case study at the Intendencia de Montevideo (IdeM), as part of a project for improving the integration of people with disabilities in its staff (currently the IdeM staff only includes about 1.5% of employees with disabilities; while applicable laws state that this percentage should raise to at least 4%). The mathematical programming model developed includes four alternative objective functions, taking into account the goals of different stakeholders. We analyze the solutions found by applying the model, comparing the results against manual assignments; we discuss them when the objective functions are integrated using a weighted sum method, and the sensitivity with regard to the coefficients; and how the results vary when the number of available positions is changed. The main conclusion is that mathematical programming models are an effective tool to support decision making and improve the integration of workers with disabilities in a service organization.

La rápida evolución de la industria de TI ha atraído a muchos profesionales sin experiencia previa en TI, generando oportunidades y una necesidad crítica de capacitación efectiva y educación continua para mantener una fuerza laboral capacitada y adaptable. A pesar de esta demanda, solo alrededor de un tercio de los profesionales no informáticos que se incorporan al campo permanecen a largo plazo, a menudo debido a una capacitación insuficiente. Este problema se vuelve particularmente desafiante cuando se abordan aspectos transversales de la disciplina, como la programación, la detección de errores y la especificación de requisitos. Considerando lo anterior, este documento propone estrategias de enseñanza adaptadas al estilo de aprendizaje más representativo dentro de la industria para mejorar el desarrollo y la retención profesional en Ingeniería de Software. Para abordar esto, se administró la Prueba de Estilos de Aprendizaje de Kolb a 112 profesionales del desarrollo de software para identificar el estilo de aprendizaje predominante. Las respuestas se analizaron para determinar patrones y conocimientos relevantes para la capacitación. El análisis reveló que el Estilo de Aprendizaje Pensante es el más representativo entre los profesionales de la industria. Con base en este hallazgo, presentamos estrategias de enseñanza personalizadas para abordar los desafíos clave en la capacitación en Ingeniería de Software.

Autonomous driving, decades ago relegated to the realm of science fiction, emerged as a tangible reality that is rapidly transforming the automotive industry, redefining our relationship with vehicles, and placing them in the spotlight of both the industry and the general public. Through the study and analysis of modern and efficient interpolation techniques, we aim to reduce the current costs and processing requirements associated with the LiDAR sensor, which is one of the main information sources. Our approach explores the fusion of lower-cost LiDAR sensors with advanced interpolation techniques, with a particular focus on achieving performance parity with pricier 64-channel LiDAR setups. This work is based on 3 main axes: firstly, the analysis of available LiDAR data and its representation; secondly, the development and implementation of an interpolation technique based on 1D convolutional layers integrated with fully connected layers, in order to analyse data coming from a sliding window; and finally, the comparative evaluation of the results between different state-of-the-art interpolation techniques, using object detection networks in point clouds. Furthermore, a basic analysis regarding power consumption and a potential hardware implementation is presented. By interpolating the point clouds with the proposed technique, improvements between 1.92% and 30.98% in detection and classification tasks were achieved, depending on the object and the type of detection (3D or bird's eye view). Furthermore, computational efficiency was not left aside by reducing the inference times necessary for interpolation, compared to other techniques used as contrast. This highlights the viability and scalability of our approach in realizing cost-effective yet high-performance autonomous driving systems.

The problem of migrating monolithic applications to microservices has become popular both in industry and academia, particularly when using automated tools to assist developers in the decomposition. However, deciding which is the most appropriate decomposition for a given monolith is challenging because the selected technique can return alternative decompositions depending on tool parameter configurations. This issue, often overlooked in the literature, makes developers have to resort to their intuition or use default parameters, leading to uncertain or opaque results. Based on a prior study of the parameters and variability of the decompositions generated an existing tool (MicroMiner), we propose an approach that leverages data-driven techniques to analyze the space of possible decompositions. These analytics are wrapped as predefined analysis mechanisms. Furthermore, we introduce a semantic layer based on an LLM, which connects developers' questions about the decompositions with predefined analyses, delivering textual answers and graphical charts. This enables user-friendly interactions between developers and decomposition tools' data. Our results demonstrate that our approach effectively identifies key parameters influencing decompositions and that the semantic layer provided relevant answers to 74% of possible practitioners' questions about the decomposition landscape, bringing insights into the tool parametrizations and also improving the interpretability of results by humans.