http://sedici.unlp.edu.ar:80/handle/10915/103582 Fri, 13 Nov 2026 14:34:12 GMT 2026-11-13T14:34:12Z http://sedici.unlp.edu.ar:80/handle/10915/197632 Objeto de conferencia XIV Jornadas de Cloud Computing, Big Data y Emerging Topics (La Plata, 23 al 25 de junio de 2026); https://sedici.unlp.edu.ar/handle/10915/197309; 14th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2026) Early detection of vehicular faults remains a major challenge in the development of predictive maintenance strategies. Traditional diagnostic systems based on OBD2 (On-Board Diagnostics II) data are commonly used in a reactive manner, identifying failures only after they have already affected vehicle performance. To address this limitation, a predictive model based on percolation theory, OBD2 data, and Machine Learning is proposed. Real-world data were collected from an internal combustion vehicle using an ELM327 OBD2 scanner under different operating conditions. Next, a functional network was constructed in which the sensors were represented as nodes and their statistical relationships as edges. Percolation theory was applied to evaluate network robustness and estimate the critical connectivity threshold associated with system stability. In parallel, unsupervised learning techniques, particularly DBSCAN and Isolation Forest, were used to identify patterns and detect anomalous observations in the sensor data. The results indicate that the integration of network-based structural analysis and Machine Learning provides a valuable framework for the early detection of incipient vehicular faults and supports the design of data-driven predictive maintenance strategies. Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197632 2026-01-01T00:00:00Z Early detection of vehicular faults remains a major challenge in the development of predictive maintenance strategies. Traditional diagnostic systems based on OBD2 (On-Board Diagnostics II) data are commonly used in a reactive manner, identifying failures only after they have already affected vehicle performance. To address this limitation, a predictive model based on percolation theory, OBD2 data, and Machine Learning is proposed. Real-world data were collected from an internal combustion vehicle using an ELM327 OBD2 scanner under different operating conditions. Next, a functional network was constructed in which the sensors were represented as nodes and their statistical relationships as edges. Percolation theory was applied to evaluate network robustness and estimate the critical connectivity threshold associated with system stability. In parallel, unsupervised learning techniques, particularly DBSCAN and Isolation Forest, were used to identify patterns and detect anomalous observations in the sensor data. The results indicate that the integration of network-based structural analysis and Machine Learning provides a valuable framework for the early detection of incipient vehicular faults and supports the design of data-driven predictive maintenance strategies. http://sedici.unlp.edu.ar:80/handle/10915/197631 Objeto de conferencia XIV Jornadas de Cloud Computing, Big Data y Emerging Topics (La Plata, 23 al 25 de junio de 2026); https://sedici.unlp.edu.ar/handle/10915/197309; 14th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2026) Despite the growing interest in LLMs for clinical decision support, few studies have evaluated their ability to perform ESI-based triage using real clinical data from Latin American healthcare settings. This study systematically evaluates the capacity of LLMs to predict emergency triage levels from clinical narratives. Two models — Qwen3- 30B and MedGemma-1.5-4B — were assessed across three progressive strategies: zero-shot prompting, Chain-of-Thought prompt engineering with ESI v4 logic, and LoRA fine-tuning on a synthetically constructed instruction-response dataset of 2,750 cases from Talca Regional Hospital, Chile. Fine-tuning yielded the most substantial performance gains, achieving accuracy above 0.55 and F1 scores up to 0.585, with the lightweight MedGemma model reaching performance comparable to the larger Qwen3- 30B. These findings suggest that domain-specific models can be effectively adapted for triage classification tasks, supporting their potential as clinical decision-support tools and motivating future work on knowledgegraph-augmented approaches tailored to regional emergency protocols. Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197631 2026-01-01T00:00:00Z Despite the growing interest in LLMs for clinical decision support, few studies have evaluated their ability to perform ESI-based triage using real clinical data from Latin American healthcare settings. This study systematically evaluates the capacity of LLMs to predict emergency triage levels from clinical narratives. Two models — Qwen3- 30B and MedGemma-1.5-4B — were assessed across three progressive strategies: zero-shot prompting, Chain-of-Thought prompt engineering with ESI v4 logic, and LoRA fine-tuning on a synthetically constructed instruction-response dataset of 2,750 cases from Talca Regional Hospital, Chile. Fine-tuning yielded the most substantial performance gains, achieving accuracy above 0.55 and F1 scores up to 0.585, with the lightweight MedGemma model reaching performance comparable to the larger Qwen3- 30B. These findings suggest that domain-specific models can be effectively adapted for triage classification tasks, supporting their potential as clinical decision-support tools and motivating future work on knowledgegraph-augmented approaches tailored to regional emergency protocols. http://sedici.unlp.edu.ar:80/handle/10915/197630 Objeto de conferencia XIV Jornadas de Cloud Computing, Big Data y Emerging Topics (La Plata, 23 al 25 de junio de 2026); https://sedici.unlp.edu.ar/handle/10915/197309; 14th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2026) Cannabis cultivation is a process that, although it follows a well-defined life cycle, always encounters unique details in each crop. Throughout the different stages, from germination to harvest, it is essential to adjust the light, water, and nutrient conditions to suit the specific needs of each plant. Establishing secure, transparent, and structured access to operational and medicinal information about the final product promotes not only consumer confidence but also regulatory compliance and the optimization of production processes. Currently, support systems exist at the national and local levels with a variety of services; however, very few cover all phases of cultivation. Besides, the most comprehensive systems reflect practices adopted from large-scale companies, making it difficult to implement traceability in small and medium-sized organizations. Our system attempts to address some of these shortcomings by providing complete but easy-to-adopt coverage of the phases. This paper discusses lessons learned during its development and implementation, including design decisions that might be considered when blockchain technology is deployed in practice. Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197630 2026-01-01T00:00:00Z Cannabis cultivation is a process that, although it follows a well-defined life cycle, always encounters unique details in each crop. Throughout the different stages, from germination to harvest, it is essential to adjust the light, water, and nutrient conditions to suit the specific needs of each plant. Establishing secure, transparent, and structured access to operational and medicinal information about the final product promotes not only consumer confidence but also regulatory compliance and the optimization of production processes. Currently, support systems exist at the national and local levels with a variety of services; however, very few cover all phases of cultivation. Besides, the most comprehensive systems reflect practices adopted from large-scale companies, making it difficult to implement traceability in small and medium-sized organizations. Our system attempts to address some of these shortcomings by providing complete but easy-to-adopt coverage of the phases. This paper discusses lessons learned during its development and implementation, including design decisions that might be considered when blockchain technology is deployed in practice. http://sedici.unlp.edu.ar:80/handle/10915/197629 Objeto de conferencia XIV Jornadas de Cloud Computing, Big Data y Emerging Topics (La Plata, 23 al 25 de junio de 2026); https://sedici.unlp.edu.ar/handle/10915/197309; 14th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2026) This paper examines UX design patterns in non-custodial cryptocurrency wallets and their implications for usability, safety, and adoption. A hands-on evaluation of widely used mobile wallets (e.g., MetaMask, Trust Wallet, and Exodus) identifies recurring solutions across critical flows: Initial Setup and Seed-phrase Generation, Seed Import, Portfolio Visualization, Recipient Address, and Transaction Execution. We identify five UX Patterns which are described using a uniform pattern template that explains each one of them in terms of what is it, when to use it, and why it works. The resulting catalog provides actionable guidance for designers and developers of multi-chain, mobile-first wallets and, suggests directions for standardization. Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197629 2026-01-01T00:00:00Z This paper examines UX design patterns in non-custodial cryptocurrency wallets and their implications for usability, safety, and adoption. A hands-on evaluation of widely used mobile wallets (e.g., MetaMask, Trust Wallet, and Exodus) identifies recurring solutions across critical flows: Initial Setup and Seed-phrase Generation, Seed Import, Portfolio Visualization, Recipient Address, and Transaction Execution. We identify five UX Patterns which are described using a uniform pattern template that explains each one of them in terms of what is it, when to use it, and why it works. The resulting catalog provides actionable guidance for designers and developers of multi-chain, mobile-first wallets and, suggests directions for standardization. http://sedici.unlp.edu.ar:80/handle/10915/197628 Objeto de conferencia XIV Jornadas de Cloud Computing, Big Data y Emerging Topics (La Plata, 23 al 25 de junio de 2026); https://sedici.unlp.edu.ar/handle/10915/197309; 14th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2026) Remote monitoring has evolved from reactive systems to complex Internet of Medical Things (IoMT) ecosystems. However, centralized architectures face critical vulnerabilities: high latency, absolute dependence on connectivity and its state, and a high rate of false positives in detections due to the system’s own conditions, such as fall detection. This paper presents a distributed multi-agent framework designed to be executed at the edge using ESP32/Single Board Computer (SBC) hardware and MicroPython. The architecture replaces the monolithic logic of a system with a society of autonomous agents (ECG, Falls, Environmental, etc.) that collaborate through the Micro Agent Communication Protocol (μACP). The novelty lies in cooperative reasoning; the system emulates clinical judgment by correlating biological and environmental dimensions through Bayesian fusion. The results project the validation of an accuracy close to 100% in the discrimination of activities of daily living (ADL) and a latency reduction compared to cloud-based models. Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197628 2026-01-01T00:00:00Z Remote monitoring has evolved from reactive systems to complex Internet of Medical Things (IoMT) ecosystems. However, centralized architectures face critical vulnerabilities: high latency, absolute dependence on connectivity and its state, and a high rate of false positives in detections due to the system’s own conditions, such as fall detection. This paper presents a distributed multi-agent framework designed to be executed at the edge using ESP32/Single Board Computer (SBC) hardware and MicroPython. The architecture replaces the monolithic logic of a system with a society of autonomous agents (ECG, Falls, Environmental, etc.) that collaborate through the Micro Agent Communication Protocol (μACP). The novelty lies in cooperative reasoning; the system emulates clinical judgment by correlating biological and environmental dimensions through Bayesian fusion. The results project the validation of an accuracy close to 100% in the discrimination of activities of daily living (ADL) and a latency reduction compared to cloud-based models. http://sedici.unlp.edu.ar:80/handle/10915/197627 Objeto de conferencia XIV Jornadas de Cloud Computing, Big Data y Emerging Topics (La Plata, 23 al 25 de junio de 2026); https://sedici.unlp.edu.ar/handle/10915/197309; 14th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2026) Authentication processes are fundamental for service providers and users alike. Currently, many users rely on centralized identity providers that verify identities to grant access to services. While these providers offer convenience, they require the disclosure of personal data, often leading to privacy concerns and third-party data access. Advancements in blockchain technology and smart contracts have enabled decentralized alternatives. Specifically, Sign-In with Ethereum (SIWE) leverages asymmetric cryptography, allowing users to authenticate via their public addresses and wallets. However, this approach introduces a significant risk: the loss or theft of private keys can result in the permanent loss of account control. This document proposes a multi-factor authentication (MFA) scheme that utilizes a smart contract to link a primary blockchain address with a secondary factor. This system provides an additional security layer against attackers without requiring personal information, effectively balancing account recovery resilience with user privacy. Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197627 2026-01-01T00:00:00Z Authentication processes are fundamental for service providers and users alike. Currently, many users rely on centralized identity providers that verify identities to grant access to services. While these providers offer convenience, they require the disclosure of personal data, often leading to privacy concerns and third-party data access. Advancements in blockchain technology and smart contracts have enabled decentralized alternatives. Specifically, Sign-In with Ethereum (SIWE) leverages asymmetric cryptography, allowing users to authenticate via their public addresses and wallets. However, this approach introduces a significant risk: the loss or theft of private keys can result in the permanent loss of account control. This document proposes a multi-factor authentication (MFA) scheme that utilizes a smart contract to link a primary blockchain address with a secondary factor. This system provides an additional security layer against attackers without requiring personal information, effectively balancing account recovery resilience with user privacy. http://sedici.unlp.edu.ar:80/handle/10915/197626 Objeto de conferencia XIV Jornadas de Cloud Computing, Big Data y Emerging Topics (La Plata, 23 al 25 de junio de 2026); https://sedici.unlp.edu.ar/handle/10915/197309; 14th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2026) The complexity of stochastic optimization and large-scale benchmarking has elevated Research Data Management (RDM) from a peripheral task to a core methodological requirement. While the FAIR principles, that ensures data is Findable, Accessible, Interoperable, and Reusable, provide high-level guidance, their practical implementation in doctoral-level computational research remains insufficiently documented. In fact, existing guidelines are often difficult to apply as they do not immediately adapt to the diverse and specific scenarios of high-dimensional research. This paper presents a case study on the RDM lifecycle of a heuristic framework, documenting the transition from non-standardized experimental logs to FAIR-compliant datasets in the CORA.RDR repository. We identify critical challenges such as data heterogeneity, volume, and the technical debt generated by stochastic variability across thousands of independent runs. In response, we propose a lightweight, practical framework based on hierarchical structures, self-documenting naming conventions, and multi-level metadata schemas. The contribution of this work lies in bridging the gap between theoretical RDM and the non-linear reality of doctoral research. By providing actionable lessons on retrospective curation and interpretable transparency, we offer a roadmap for researchers to ensure the long-term reusability and integrity of complex computational artifacts. Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197626 2026-01-01T00:00:00Z The complexity of stochastic optimization and large-scale benchmarking has elevated Research Data Management (RDM) from a peripheral task to a core methodological requirement. While the FAIR principles, that ensures data is Findable, Accessible, Interoperable, and Reusable, provide high-level guidance, their practical implementation in doctoral-level computational research remains insufficiently documented. In fact, existing guidelines are often difficult to apply as they do not immediately adapt to the diverse and specific scenarios of high-dimensional research. This paper presents a case study on the RDM lifecycle of a heuristic framework, documenting the transition from non-standardized experimental logs to FAIR-compliant datasets in the CORA.RDR repository. We identify critical challenges such as data heterogeneity, volume, and the technical debt generated by stochastic variability across thousands of independent runs. In response, we propose a lightweight, practical framework based on hierarchical structures, self-documenting naming conventions, and multi-level metadata schemas. The contribution of this work lies in bridging the gap between theoretical RDM and the non-linear reality of doctoral research. By providing actionable lessons on retrospective curation and interpretable transparency, we offer a roadmap for researchers to ensure the long-term reusability and integrity of complex computational artifacts. http://sedici.unlp.edu.ar:80/handle/10915/197625 Objeto de conferencia XIV Jornadas de Cloud Computing, Big Data y Emerging Topics (La Plata, 23 al 25 de junio de 2026); https://sedici.unlp.edu.ar/handle/10915/197309; 14th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2026) Insider threats remain a critical and frequently underestimated challenge in cybersecurity, particularly due to their origin from authorized users and the limitations of traditional detection approaches. Addressing this problem requires not only detection mechanisms, but also integrated approaches for security event analysis and decision support. This study presents the design and implementation of a modular framework for the detection and analysis of insider threats in organizational environments. The proposed system integrates a configurable event simulator, a data ingestion module, an analytical processing API, and an interactive dashboard, forming an end-to-end pipeline for security event analysis. This architecture enables the systematic processing and interpretation of security events, supporting monitoring and decisionmaking processes. The main contribution of this work lies in the development of an integrated and interpretable analytical framework, combining event classification, weighted risk scoring, and normalization to provide transparent and actionable insights for insider threat management. The system was evaluated in controlled environments using synthetic data derived from the CERT dataset, enabling systematic experimentation in the absence of real-world data. The results demonstrate the system’s capability to detect anomalous behaviors, identify risk patterns, and operate efficiently under high-volume scenarios. These findings highlight the feasibility of the proposed approach as a practical foundation for modular insider threat analysis systems in organizational contexts. Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197625 2026-01-01T00:00:00Z Insider threats remain a critical and frequently underestimated challenge in cybersecurity, particularly due to their origin from authorized users and the limitations of traditional detection approaches. Addressing this problem requires not only detection mechanisms, but also integrated approaches for security event analysis and decision support. This study presents the design and implementation of a modular framework for the detection and analysis of insider threats in organizational environments. The proposed system integrates a configurable event simulator, a data ingestion module, an analytical processing API, and an interactive dashboard, forming an end-to-end pipeline for security event analysis. This architecture enables the systematic processing and interpretation of security events, supporting monitoring and decisionmaking processes. The main contribution of this work lies in the development of an integrated and interpretable analytical framework, combining event classification, weighted risk scoring, and normalization to provide transparent and actionable insights for insider threat management. The system was evaluated in controlled environments using synthetic data derived from the CERT dataset, enabling systematic experimentation in the absence of real-world data. The results demonstrate the system’s capability to detect anomalous behaviors, identify risk patterns, and operate efficiently under high-volume scenarios. These findings highlight the feasibility of the proposed approach as a practical foundation for modular insider threat analysis systems in organizational contexts. http://sedici.unlp.edu.ar:80/handle/10915/197621 Objeto de conferencia XIV Jornadas de Cloud Computing, Big Data y Emerging Topics (La Plata, 23 al 25 de junio de 2026); https://sedici.unlp.edu.ar/handle/10915/197309; 14th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2026) Public health surveillance in Argentina is constrained by both vertical and horizontal fragmentation of health information systems. Existing platforms, such as the National Health Surveillance System (SNVS) and the Integrated Health Record (Historia de Salud Integrada, HSI), operate as loosely coupled systems with limited interoperability, often relying on manual data exchange. This paper proposes a cloud-native architecture for real-time public health vulnerability assessment that integrates heterogeneous data sources, including clinical records, epidemiological data, and geospatial social determinants of health. The main contribution is a stream-centric modular monolith architecture that extends Hexagonal Architecture with an internal Kappa-style streaming layer, enabling continuous processing of domain events and computation of contextual indicators. These indicators are reintegrated into the domain model, establishing a controlled feedback loop between analytical processing and vulnerability assessment. The architecture combines Command Query Responsibility Segregation (CQRS) with the Transactional Outbox pattern to ensure consistent and reliable propagation of state changes throughout the system while preserving a unified deployment model. The proposed approach enables near-real-time vulnerability assessment at both individual and population levels, supporting context-aware decision-making in fragmented public health environments. The design is evaluated through a scenario-based architectural validation grounded in representative public health workflows. Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197621 2026-01-01T00:00:00Z Public health surveillance in Argentina is constrained by both vertical and horizontal fragmentation of health information systems. Existing platforms, such as the National Health Surveillance System (SNVS) and the Integrated Health Record (Historia de Salud Integrada, HSI), operate as loosely coupled systems with limited interoperability, often relying on manual data exchange. This paper proposes a cloud-native architecture for real-time public health vulnerability assessment that integrates heterogeneous data sources, including clinical records, epidemiological data, and geospatial social determinants of health. The main contribution is a stream-centric modular monolith architecture that extends Hexagonal Architecture with an internal Kappa-style streaming layer, enabling continuous processing of domain events and computation of contextual indicators. These indicators are reintegrated into the domain model, establishing a controlled feedback loop between analytical processing and vulnerability assessment. The architecture combines Command Query Responsibility Segregation (CQRS) with the Transactional Outbox pattern to ensure consistent and reliable propagation of state changes throughout the system while preserving a unified deployment model. The proposed approach enables near-real-time vulnerability assessment at both individual and population levels, supporting context-aware decision-making in fragmented public health environments. The design is evaluated through a scenario-based architectural validation grounded in representative public health workflows. http://sedici.unlp.edu.ar:80/handle/10915/197620 Objeto de conferencia XIV Jornadas de Cloud Computing, Big Data y Emerging Topics (La Plata, 23 al 25 de junio de 2026); https://sedici.unlp.edu.ar/handle/10915/197309; 14th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2026) Emerging technologies continue to evolve. As a result, institutions like Universidad del Azuay (UDA) are expanding support services beyond traditional omnichannel platforms. There is now a need to offer support through extended reality and voice-based environments, enabling immersive, natural user interactions. This study presents the building and evaluation of an Intelligent Immersive Assistant (IIA) that provides accurate information on multiple topics through immersive and voice interaction. The Design thinking methodology was used. A survey helped identify user needs and define the problem. An IIA prototype, UDAbot, was built using Unity 3D, XR Plugin Management, the Meta All-in-One SDK, and the Voice SDK with Wit.ai for voice interaction. Generative AI and Retrieval-Augmented Generation (RAG) enhanced response accuracy. Finally, acceptance of the IIA was evaluated via a survey that assessed 21 factors related to technology acceptance, usability, and immersive environments. The study identified 16 key factors influencing user adoption. Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197620 2026-01-01T00:00:00Z Emerging technologies continue to evolve. As a result, institutions like Universidad del Azuay (UDA) are expanding support services beyond traditional omnichannel platforms. There is now a need to offer support through extended reality and voice-based environments, enabling immersive, natural user interactions. This study presents the building and evaluation of an Intelligent Immersive Assistant (IIA) that provides accurate information on multiple topics through immersive and voice interaction. The Design thinking methodology was used. A survey helped identify user needs and define the problem. An IIA prototype, UDAbot, was built using Unity 3D, XR Plugin Management, the Meta All-in-One SDK, and the Voice SDK with Wit.ai for voice interaction. Generative AI and Retrieval-Augmented Generation (RAG) enhanced response accuracy. Finally, acceptance of the IIA was evaluated via a survey that assessed 21 factors related to technology acceptance, usability, and immersive environments. The study identified 16 key factors influencing user adoption. http://sedici.unlp.edu.ar:80/handle/10915/197619 Objeto de conferencia XIV Jornadas de Cloud Computing, Big Data y Emerging Topics (La Plata, 23 al 25 de junio de 2026); https://sedici.unlp.edu.ar/handle/10915/197309; 14th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2026) Cloud computing has established itself as a pillar of organizational digital transformation, offering flexible and scalable pay-per-use models. However, this paradigm introduces significant challenges in the financial management of technological resources, particularly in small and medium-sized enterprises (SMEs), where a lack of visibility and governance of spending can limit the expected benefits. In this context, FinOps (Cloud Financial Operations) practices are emerging as an organizational framework aimed at maximizing the economic value of the cloud through coordination between the areas of technology, finance, and business. This work evaluates the feasibility of implementing FinOps practices in SMEs in the province of Misiones, considering the characteristics of the regional productive fabric and the availability of cloud computing infrastructure and services provided by local actors, especially the state-owned company Marandú Comunicaciones S.E. The methodology combines a systematic review of recent literature on cloud computing, FinOps, and technology adoption in SMEs with semi-structured interviews with key actors in the provincial ecosystem. As a main contribution, it proposes a Matrix for Evaluating the Feasibility of Implementing FinOps Practices, structured in six dimensions that integrate technical, economic, organizational, and institutional aspects, under a triple helix approach that articulates business, university, and government. The results indicate that, although SMEs in Misiones have low to intermediate levels of digital maturity, there is a favorable environment for the progressive and contextualized adoption of FinOps, aimed at improving the efficiency, predictability, and sustainability of cloud use in regional economies.; La computación en la nube se ha consolidado como un pilar de la transformación digital organizacional, al ofrecer modelos flexibles y escalables de pago por uso. Sin embargo, este paradigma introduce desafíos significativos en la gestión financiera de los recursos tecnológicos, especialmente en las pequeñas y medianas empresas (PyMEs), donde la falta de visibilidad y gobernanza del gasto puede limitar los beneficios esperados. En este contexto, las prácticas FinOps (Operaciones Financieras en la Nube) emergen como un marco organizacional orientado a maximizar el valor económico de la nube mediante la coordinación entre las áreas de tecnología, finanzas y negocio. Este trabajo evalúa la factibilidad de implementar prácticas FinOps en PyMEs de la provincia de Misiones, considerando las características del entramado productivo regional y la disponibilidad de infraestructura y servicios de computación en la nube provistos por actores locales, en particular la empresa estatal Marandú Comunicaciones S.E. La metodología combina una revisión sistemática de literatura reciente sobre computación en la nube, FinOps y adopción tecnológica en PyMEs, con entrevistas semiestructuradas a actores clave del ecosistema provincial. Como principal aporte, se propone una Matriz de Evaluación de Factibilidad para la Implementación de Prácticas FinOps, estructurada en seis dimensiones que integran aspectos técnicos, económicos, organizacionales e institucionales, bajo un enfoque de triple hélice que articula empresa, universidad y gobierno. Los resultados indican que, si bien las PyMEs de Misiones presentan niveles de madurez digital bajos a intermedios, existe un entorno favorable para la adopción progresiva y contextualizada de FinOps, orientada a mejorar la eficiencia, previsibilidad y sostenibilidad en el uso de la nube en las economías regionales Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197619 2026-01-01T00:00:00Z Cloud computing has established itself as a pillar of organizational digital transformation, offering flexible and scalable pay-per-use models. However, this paradigm introduces significant challenges in the financial management of technological resources, particularly in small and medium-sized enterprises (SMEs), where a lack of visibility and governance of spending can limit the expected benefits. In this context, FinOps (Cloud Financial Operations) practices are emerging as an organizational framework aimed at maximizing the economic value of the cloud through coordination between the areas of technology, finance, and business. This work evaluates the feasibility of implementing FinOps practices in SMEs in the province of Misiones, considering the characteristics of the regional productive fabric and the availability of cloud computing infrastructure and services provided by local actors, especially the state-owned company Marandú Comunicaciones S.E. The methodology combines a systematic review of recent literature on cloud computing, FinOps, and technology adoption in SMEs with semi-structured interviews with key actors in the provincial ecosystem. As a main contribution, it proposes a Matrix for Evaluating the Feasibility of Implementing FinOps Practices, structured in six dimensions that integrate technical, economic, organizational, and institutional aspects, under a triple helix approach that articulates business, university, and government. The results indicate that, although SMEs in Misiones have low to intermediate levels of digital maturity, there is a favorable environment for the progressive and contextualized adoption of FinOps, aimed at improving the efficiency, predictability, and sustainability of cloud use in regional economies. La computación en la nube se ha consolidado como un pilar de la transformación digital organizacional, al ofrecer modelos flexibles y escalables de pago por uso. Sin embargo, este paradigma introduce desafíos significativos en la gestión financiera de los recursos tecnológicos, especialmente en las pequeñas y medianas empresas (PyMEs), donde la falta de visibilidad y gobernanza del gasto puede limitar los beneficios esperados. En este contexto, las prácticas FinOps (Operaciones Financieras en la Nube) emergen como un marco organizacional orientado a maximizar el valor económico de la nube mediante la coordinación entre las áreas de tecnología, finanzas y negocio. Este trabajo evalúa la factibilidad de implementar prácticas FinOps en PyMEs de la provincia de Misiones, considerando las características del entramado productivo regional y la disponibilidad de infraestructura y servicios de computación en la nube provistos por actores locales, en particular la empresa estatal Marandú Comunicaciones S.E. La metodología combina una revisión sistemática de literatura reciente sobre computación en la nube, FinOps y adopción tecnológica en PyMEs, con entrevistas semiestructuradas a actores clave del ecosistema provincial. Como principal aporte, se propone una Matriz de Evaluación de Factibilidad para la Implementación de Prácticas FinOps, estructurada en seis dimensiones que integran aspectos técnicos, económicos, organizacionales e institucionales, bajo un enfoque de triple hélice que articula empresa, universidad y gobierno. Los resultados indican que, si bien las PyMEs de Misiones presentan niveles de madurez digital bajos a intermedios, existe un entorno favorable para la adopción progresiva y contextualizada de FinOps, orientada a mejorar la eficiencia, previsibilidad y sostenibilidad en el uso de la nube en las economías regionales http://sedici.unlp.edu.ar:80/handle/10915/197309 Libro Naiouf, Marcelo Ricardo; Chichizola, Franco; De Giusti, Laura Cristina; Libutti, Leandro Ariel Proceedings of the 14th Conference on Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2026), held in a hybrid modality (both on-site and live online settings were allowed). JCC-BD&ET 2026 was organized by the III-LIDI and the Postgraduate Office, both from School of Computer Science of the National University of La Plata.JCC-BD&ET 2026 covered the following topics: high-performance, edge and fog computing; internet of things; modelling and simulation; big and open data; machine and deep learning; smart cities; e-government; human-computer interaction; visualization; and special topics related to emerging technologies. Facultad de Informática (UNLP) Thu, 01 Jan 2026 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/197309 2026-01-01T00:00:00Z Proceedings of the 14th Conference on Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2026), held in a hybrid modality (both on-site and live online settings were allowed). JCC-BD&ET 2026 was organized by the III-LIDI and the Postgraduate Office, both from School of Computer Science of the National University of La Plata.JCC-BD&ET 2026 covered the following topics: high-performance, edge and fog computing; internet of things; modelling and simulation; big and open data; machine and deep learning; smart cities; e-government; human-computer interaction; visualization; and special topics related to emerging technologies. http://sedici.unlp.edu.ar:80/handle/10915/189271 Objeto de conferencia XIII Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 24 al 26 de junio de 2025) In recent years, the economic and ecological impact of energy consumption in public and private institutions has increased, making it necessary to have computer and electronic systems that allow monitoring energy expenditure in the different areas of an institution. Currently, smart buildings have automated systems that include sensor nodes that allow the monitoring and control of environmental parameters, facilitating the goal of achieving energy-efficient, comfortable, and cost-effective environments. In this context, this work presents the design and development of a smart energy consumption meter that can be integrated into any control and monitoring system without the need for significant changes, thanks to its modular implementation. The hardware architecture is presented, focusing on the main functionality of each module and the interconnection and communication between them. In addition, the calibration tests performed to adjust the measurement parameters and obtain acceptable relative measurement errors are described. Tests performed with the measuring node on a heterogeneous architecture computer, comprising CPU and GPU cores and running a computationally intensive algorithm, show that the accumulated power decreases as the amount of resources used increases, validating the measuring capability of the developed system. Wed, 01 Jan 2025 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/189271 2025-01-01T00:00:00Z In recent years, the economic and ecological impact of energy consumption in public and private institutions has increased, making it necessary to have computer and electronic systems that allow monitoring energy expenditure in the different areas of an institution. Currently, smart buildings have automated systems that include sensor nodes that allow the monitoring and control of environmental parameters, facilitating the goal of achieving energy-efficient, comfortable, and cost-effective environments. In this context, this work presents the design and development of a smart energy consumption meter that can be integrated into any control and monitoring system without the need for significant changes, thanks to its modular implementation. The hardware architecture is presented, focusing on the main functionality of each module and the interconnection and communication between them. In addition, the calibration tests performed to adjust the measurement parameters and obtain acceptable relative measurement errors are described. Tests performed with the measuring node on a heterogeneous architecture computer, comprising CPU and GPU cores and running a computationally intensive algorithm, show that the accumulated power decreases as the amount of resources used increases, validating the measuring capability of the developed system. http://sedici.unlp.edu.ar:80/handle/10915/182615 Objeto de conferencia XIII Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 24 al 26 de junio de 2025); 13th Conference on Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2025) The implementation of Internet of Things (IoT) technologies in precision agriculture has been revolutionizing crop resource management. Soil sensors continuously monitor moisture, temperature, and salinity, providing crucial data for precise irrigation and soil management, while weather sensors track atmospheric conditions including air temperature, humidity, precipitation, and wind speed. This data enables prediction and management of pests and diseases, while also informing harvest decisions. Continuous monitoring and advanced data analysis allow for identification of trends and anomalies, facilitating rapid and precise adjustments to agricultural operations. In this article we propose an approach to deal with the problem of latency in the use of IoT in remote areas. Wed, 01 Jan 2025 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/182615 2025-01-01T00:00:00Z The implementation of Internet of Things (IoT) technologies in precision agriculture has been revolutionizing crop resource management. Soil sensors continuously monitor moisture, temperature, and salinity, providing crucial data for precise irrigation and soil management, while weather sensors track atmospheric conditions including air temperature, humidity, precipitation, and wind speed. This data enables prediction and management of pests and diseases, while also informing harvest decisions. Continuous monitoring and advanced data analysis allow for identification of trends and anomalies, facilitating rapid and precise adjustments to agricultural operations. In this article we propose an approach to deal with the problem of latency in the use of IoT in remote areas. http://sedici.unlp.edu.ar:80/handle/10915/182614 Objeto de conferencia XIII Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 24 al 26 de junio de 2025); 13th Conference on Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2025) This study explores residential property valuation in Seville, Spain, using interpretable machine learning techniques on a small dataset of 1701 sales ads of apartments collected online. Unlike conventional approaches that rely on large datasets, our research addresses the unique challenges of small data samples while maintaining model interpretability. We compare traditional hedonic linear regression with Random Forest algorithms. The results provide actionable insights for real estate stakeholders in medium-sized urban markets, bridging the gap between econometric tradition and machine learning innovation. Wed, 01 Jan 2025 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/182614 2025-01-01T00:00:00Z This study explores residential property valuation in Seville, Spain, using interpretable machine learning techniques on a small dataset of 1701 sales ads of apartments collected online. Unlike conventional approaches that rely on large datasets, our research addresses the unique challenges of small data samples while maintaining model interpretability. We compare traditional hedonic linear regression with Random Forest algorithms. The results provide actionable insights for real estate stakeholders in medium-sized urban markets, bridging the gap between econometric tradition and machine learning innovation. http://sedici.unlp.edu.ar:80/handle/10915/182613 Objeto de conferencia XIII Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 24 al 26 de junio de 2025); 13th Conference on Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2025) The detection of cybersecurity attacks through the collection and analysis of information is a challenge that focuses, in this work, on the use of honeypots, which are decoys that allow attacks to be studied in a controlled environment. The data collected can be used as a source of information in Cyber Threat Intelligence (CTI). Cyber Deception is a form of deception that exploits digital tools to deceive, manipulate or confuse a target, where the value lies in being attacked and investigated. Thus, honeypots constitute a Cyber Deception mechanism. Cybersecurity frameworks provide a reference model for the analysis of attacks, favoring their classification and understanding in order to mitigate them. Furthermore, these frameworks help to understand the stages of attacks linked to Cyber Deception mechanisms, including honeypots. The aim of the work is to analyze the communication mechanisms between honeypots and CTI platforms, with the aim of improving the cybersecurity strategies of organizations. Wed, 01 Jan 2025 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/182613 2025-01-01T00:00:00Z The detection of cybersecurity attacks through the collection and analysis of information is a challenge that focuses, in this work, on the use of honeypots, which are decoys that allow attacks to be studied in a controlled environment. The data collected can be used as a source of information in Cyber Threat Intelligence (CTI). Cyber Deception is a form of deception that exploits digital tools to deceive, manipulate or confuse a target, where the value lies in being attacked and investigated. Thus, honeypots constitute a Cyber Deception mechanism. Cybersecurity frameworks provide a reference model for the analysis of attacks, favoring their classification and understanding in order to mitigate them. Furthermore, these frameworks help to understand the stages of attacks linked to Cyber Deception mechanisms, including honeypots. The aim of the work is to analyze the communication mechanisms between honeypots and CTI platforms, with the aim of improving the cybersecurity strategies of organizations. http://sedici.unlp.edu.ar:80/handle/10915/182612 Objeto de conferencia XIII Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 24 al 26 de junio de 2025); 13th Conference on Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2025) The integration of artificial intelligence (AI) technologies within organizations presents both significant opportunities and complex challenges. To manage this complexity, ISO/IEC standards provide a structured framework for the adoption and management of AI systems throughout their lifecycle. This article explores the role of ISO/IEC standards in ensuring the quality, security, and ethical alignment of AI systems, based on the lifecycle framework defined by the Organisation for Economic Co-operation and Development (OECD). The paper outlines how these standards support AI system development, from planning and design through deployment and monitoring, addressing critical issues such as governance, data quality, bias detection, and system reliability. A comprehensive quality model is proposed, drawing on ISO/IEC 25058 and 25059 standards, to assess the effectiveness and transparency of AI systems in real-world environments. The adoption of these standards is shown to enhance corporate reputation, improve regulatory compliance, and mitigate risks, positioning organizations to leverage AI technologies responsibly and efficiently. Wed, 01 Jan 2025 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/182612 2025-01-01T00:00:00Z The integration of artificial intelligence (AI) technologies within organizations presents both significant opportunities and complex challenges. To manage this complexity, ISO/IEC standards provide a structured framework for the adoption and management of AI systems throughout their lifecycle. This article explores the role of ISO/IEC standards in ensuring the quality, security, and ethical alignment of AI systems, based on the lifecycle framework defined by the Organisation for Economic Co-operation and Development (OECD). The paper outlines how these standards support AI system development, from planning and design through deployment and monitoring, addressing critical issues such as governance, data quality, bias detection, and system reliability. A comprehensive quality model is proposed, drawing on ISO/IEC 25058 and 25059 standards, to assess the effectiveness and transparency of AI systems in real-world environments. The adoption of these standards is shown to enhance corporate reputation, improve regulatory compliance, and mitigate risks, positioning organizations to leverage AI technologies responsibly and efficiently. http://sedici.unlp.edu.ar:80/handle/10915/182611 Objeto de conferencia XIII Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 24 al 26 de junio de 2025); 13th Conference on Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2025) Exergames have gained popularity as interactive systems that promote physical activity through gaming. This paper presents the development process of a hand motion-sensing glove designed to complement an existing ankle-worn motion sensor. The glove aims to provide a more immersive exergaming experience by accurately tracking hand movements. Several prototypes were developed and iteratively improved to refine the design and functionality. This paper outlines the background of exergaming technology, reviews related work, and details the iterative development of the glove, discussing challenges encountered and improvements made. Finally, future steps for completing the final prototype are discussed. Wed, 01 Jan 2025 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/182611 2025-01-01T00:00:00Z Exergames have gained popularity as interactive systems that promote physical activity through gaming. This paper presents the development process of a hand motion-sensing glove designed to complement an existing ankle-worn motion sensor. The glove aims to provide a more immersive exergaming experience by accurately tracking hand movements. Several prototypes were developed and iteratively improved to refine the design and functionality. This paper outlines the background of exergaming technology, reviews related work, and details the iterative development of the glove, discussing challenges encountered and improvements made. Finally, future steps for completing the final prototype are discussed. http://sedici.unlp.edu.ar:80/handle/10915/182610 Objeto de conferencia XIII Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 24 al 26 de junio de 2025); 13th Conference on Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2025) The growth of e-commerce has led to an increase in the complexity of automatic sorting systems (Sorters), especially evident during the COVID-19 pandemic. This is reflected in the growing number of destinations, variety of products, reduced batch sizes, diversity in box dimensions, varying routes, and the need for rapid response times, among other factors. These complexities hinder decision-making in the s ystem, particularly in developing a line balancing program for package unloading lines from the Sorter. Therefore, tools that support improved decision-making are required. This publication contributes, on one hand, to the conceptual modeling of the system using the IDEF-SIM conceptual modeling technique to better understand it. On the other hand, it contributes to the construction of the simulation model using the FlexSim® software. Finally, a heuristic-based simulation optimization methodology is proposed to enhance decision-making in balancing the sorting line. Wed, 01 Jan 2025 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/182610 2025-01-01T00:00:00Z The growth of e-commerce has led to an increase in the complexity of automatic sorting systems (Sorters), especially evident during the COVID-19 pandemic. This is reflected in the growing number of destinations, variety of products, reduced batch sizes, diversity in box dimensions, varying routes, and the need for rapid response times, among other factors. These complexities hinder decision-making in the s ystem, particularly in developing a line balancing program for package unloading lines from the Sorter. Therefore, tools that support improved decision-making are required. This publication contributes, on one hand, to the conceptual modeling of the system using the IDEF-SIM conceptual modeling technique to better understand it. On the other hand, it contributes to the construction of the simulation model using the FlexSim® software. Finally, a heuristic-based simulation optimization methodology is proposed to enhance decision-making in balancing the sorting line. http://sedici.unlp.edu.ar:80/handle/10915/182609 Objeto de conferencia XIII Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 24 al 26 de junio de 2025); 13th Conference on Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2025) ALERTAR system aims to assist healthcare providers in identifying early clinical deterioration in hospitalized patients on general wards. Its cloud-fog-edge architecture uses only mobile devices at the fog and edge levels to simplify its operability. As a critical healthcare system, it is of utmost importance to provide resilience across multiple layers of the architecture. To tolerate faults, the system can dynamically migrate devices between the fog and edge layers. This work focuses on describing an automated and secure login method for this system. To reduce user intervention and simplify system use, the login process has been automated by storing session credentials and utilizing a fog-level device discovery process. The design of the login mechanism is aligned with the strict security requirements of the system, considering the sensitivity of the data and the criticality of the service. This is a work in progress so performance evaluations are still pending. Wed, 01 Jan 2025 00:00:00 GMT http://sedici.unlp.edu.ar:80/handle/10915/182609 2025-01-01T00:00:00Z ALERTAR system aims to assist healthcare providers in identifying early clinical deterioration in hospitalized patients on general wards. Its cloud-fog-edge architecture uses only mobile devices at the fog and edge levels to simplify its operability. As a critical healthcare system, it is of utmost importance to provide resilience across multiple layers of the architecture. To tolerate faults, the system can dynamically migrate devices between the fog and edge layers. This work focuses on describing an automated and secure login method for this system. To reduce user intervention and simplify system use, the login process has been automated by storing session credentials and utilizing a fog-level device discovery process. The design of the login mechanism is aligned with the strict security requirements of the system, considering the sensitivity of the data and the criticality of the service. This is a work in progress so performance evaluations are still pending.