Ingeniería Civil Informática y Telecomunicaciones

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https://repositorio.uft.cl/handle/20.500.12254/2435

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Mostrando 1 - 7 de 7

A new dynamic, secondary-memory metric index
(Red de Universidades Nacionales con carreras en Informática (RedUNCI); Universidad Nacional de la Plata, 2024-10-07) Paredes, Rodrigo
Metric space searching addresses the problem of efficient similarity searching in many applications. Although promising, the metric space approach is still immature in several aspects that are well established in traditional databases. Particularly, most indexing schemes are not dynamic, that is, few of them tolerate insertion of elements at reasonable cost over an existing index with none or mild performance degrading; and even less of them work efficiently in secondary memory. The List of Clusters (LC) is a competitive index in main memory. We introduce a new dynamic, secondary-memory variant of the LC. Our new index handles well the secondary memory scenario and is competitive with the state of the art, becoming a useful alternative in a wide range of database applications. Also, our ideas are applicable to other secondary-memory indexes, where it is possible to control the disk page occupation.
A study of software architects’ cognitive approaches: kolb’s learning styles inventory in action
(Springer, 2024-10-19) Hidalgo Barrientos, Mauricio Fernando
The multidisciplinary nature of software architects demands a diverse set of skills, ranging from technical expertise to interpersonal abilities. Within this domain, software architects are responsible for designing systems that adhere to quality standards, meet functional requirements, and align with organizational goals. However, educating or training software architects presents a challenge due to the complexity of their roles and responsibilities. To address this challenge, this paper proposes an approach to understanding the learning styles of software architects using Kolb’s Learning Style Inventory (KLSI). It aims to provide a characterization of their teaching and learning preferences, thus facilitating the design and execution of educational strategies tailored to their specific needs. In conducting our research, we utilized LinkedIn as a platform to distribute the KLSI Test, ultimately gathering a sample comprising 18 Senior and Mid-Senior Software Architects. Through trend analysis of their responses, we consistently observed a discernible pattern. This led us to identify the Deciding Learning Style as the primary approach among the sample of software architects regarding their learning preferences. This finding offers initial insights into the predominant learning style within this profession, providing valuable guidance for educational practitioners and institutions aiming to optimize their training programs for software architects.
Análisis de canasta de mercado en supermercados mediante mapas auto-organizados
(Universidade Federal do Paraná, 2021-11-08) Cordero, Joaquín
Introducción: Una cadena importante de supermercados de la zona poniente de la capital de Chile, necesita obtener información clave para tomar decisiones. Esta información se encuentra disponible en las bases de datos, pero necesita ser procesada debido a la complejidad y cantidad de información, lo que genera una dificultad a la hora de visualizar. Método: Para este propósito, se ha desarrollado un algoritmo que utiliza redes neuronales artificiales, aplicando el método SOM de Kohonen. Para llevarlo a cabo, se han debido seguir ciertos procedimientos claves, como preparar la información, para luego utilizar solo los datos relevantes a las canastas de compra de la investigación. Luego de efectuado el filtrado, se tiene que preparar el ambiente de programación en Python para adaptarlo a los datos de la muestra, y luego proceder a entrenar el SOM con sus parámetros fijados luego de resultados de pruebas. Resultado: El resultado del SOM obtiene la relación entre los productos que más se compraron, posicionándolos topológicamente cerca, para conformar promociones y bundles, para que el retail mánager tome en consideración. Conclusión: En base a esto, se han hecho recomendaciones sobre canastas de compra frecuentes a la cadena de supermercados que ha proporcionado los datos utilizados en la investigación.
BOLDSC: A New Dynamic, Secondary-Memory Metric Index
(Springer Nature, 2025-10-01) Paredes, Rodrigo
Metric space searching addresses the problem of efficient similarity searching across diverse applications, in particular for non-structured objects, for instance, natural language or images. Although promising, this approach is still immature in several aspects that are well-established in traditional databases. Particularly, most indexing schemes are not dynamic, as they cannot efficiently handle insertions over an ongoing index without significant performance degradation. Moreover, very few of them work efficiently in secondary memory. The List of Clusters (LC) has proven to be a competitive index in main memory due to its simplicity and good search performance in high dimensional metric spaces. We introduce a new dynamic, secondary-memory LC variant. Our new index efficiently handles the secondary memory scenario and achieves competitive search and insertion times compared to the state-of-the-art, making it a practical alternative for large-scale database applications. Also, our ideas are applicable to other secondary-memory indexes, where it is possible to control the disk page occupation.
Negative sampling for triplet-based loss: improving representation in self-supervised representation learning
(Springer, 2024-11-17) Goyo, Manuel Alejandro
Significant strides have been made in artificial neural networks across various fields, necessitating extensive labeled data for effective training. However, the acquisition of such annotated data is both costly and labor-intensive. To address this challenge, Self-Supervised Representation Learning (SSRL) has emerged as a promising solution. One prominent SSRL method, Contrastive Self-Supervised Learning (CSL), enhances feature representations by discerning similarities and differences among samples in the feature space. Yet, accurately identifying dissimilar samples remains a persistent issue, limiting CSL’s effectiveness. In response, an innovative enhancement to CSL is proposed in this paper. Explicit negative sampling strategies using a binary classification algorithm within the feature space are introduced to distinguish between similar and dissimilar features precisely. Additionally, Triplet Loss, originally designed for tasks such as person re-identification and face recognition, is incorporated to further refine feature learning. Experimental evaluations on the CIFAR-10 and SVHN datasets validate the proposed method’s superiority in content-based image retrieval (CBIR) and classification tasks. Significant improvements are demonstrated in metrics such as mean average precision (MAP), accuracy, recall, precision, and F1-score compared to existing techniques. This framework contributes to the advancement of SSRL by enabling scalable neural network training on large datasets with minimal annotation, effectively bridging the gap between supervised and unsupervised learning paradigms.
Protocolo para ciberseguridad utilizando la aplicación "Azure" de Microsoft
(Universidad Finis Terrae (Chile) Facultad de Ingeniería, 2025) Huenchún López, Jorge Luis; Urrutia Sepúlveda, Angélica prof. guía
Este trabajo desarrolló un protocolo de ciberseguridad en la nube implementado en Microsoft Azure, con el propósito de establecer un marco adaptable y metódico que fortalezca la protección de datos en entornos propios de nube. La propuesta surgió ante la creciente acogida de los servicios en la nube y las dificultades observadas en la gestión de accesos, la clasificación de información, trazabilidad de incidentes y el cumplimiento normativo. El protocolo se diseñó conforme a los estándares internacionales ISO/IEC 27001, NIST SP 800-53, GDPR y junto a la legislación chilena sobre la protección de datos personales. La estructura se modeló mediante BPMN, organizando el flujo en seis fases que integraron controles técnicos basados en herramientas nativas de Azure como Microsoft Entra ID, Key Vault, Defender for Cloud y Log Analytics. Durante el desarrollo, se aplicó una metodología combinada entre Kanban y Lean que permitió gestionar las tareas de forma visual, eliminando redundancias y priorizando actividades de mayor valor. La implementación práctica se ejecutó utilizando una suscripción gratuita de Azure, lo que permitió validar la operatividad del modelo e identificar limitaciones asociadas a funciones avanzadas. Aun así, mostraron resultados de un 83% de cumplimiento respecto de las configuraciones planificadas, demostrando su factibilidad técnica dentro del entorno disponible. En otras palabras, el protocolo demostró que es un modelo modular, replicable y alineado con buenas prácticas internacionales, capaz de fortalecer la postura de ciberseguridad de las organizaciones que operen en la nube y sentó bases claras para futuras ampliaciones bajo suscripciones empresariales completas.
What Is the Process? A Metamodel of the Requirements Elicitation Process Derived from a Systematic Literature Review
(MDPI, 2024-12-25) Hidalgo Barrientos, Mauricio Fernando
Requirements elicitation is a fundamental process in software engineering, essential for aligning software products with user needs and project objectives. As software projects become more complex, effective elicitation methods are vital for capturing accurate and comprehensive requirements. Despite the variety of available elicitation methods, practitioners face persistent challenges such as capturing tacit knowledge, managing diverse stakeholder needs, and addressing ambiguities in requirements. Moreover, although elicitation is recognized as a core process for gathering and analyzing system objectives, there is a lack of a unified and systematic framework to guide practitioners—especially newcomers—through the activity. To address these challenges, we provide a comprehensive analysis of existing elicitation methods, aiming to contribute to better alignment between software products and project objectives, ultimately improving software engineering practices. We do so by performing a systematic literature review identifying crosscutting steps, common techniques, tools, and approaches that define the core activities of the elicitation process. We synthesize our findings into a metamodel that structures software elicitation processes. This review uncovers various elicitation methods—such as collaborative workshops, interviews, and prototyping—each demonstrating unique strengths in different project contexts. It also highlights significant limitations, including stakeholder misalignment and incomplete requirements capture, which continue to reduce the effectiveness of elicitation processes. Finally, our study seeks to contribute to understanding requirements elicitation methods by providing a comprehensive view of their current strengths and limitations through a metamodel enabling the structuring and optimization of elicitation processes.

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Examinando Ingeniería Civil Informática y Telecomunicaciones por Materia "1.2.1 - Ciencias de la Computación"

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