In data handling, security and privacy are essential for database administrators and users that own their information. Information about security models or architectures was analyzed for databases that minimize Cyber Attacks. The problem is that security models deployed in databases in public organizations suffer from computer attacks due to vulnerabilities in their security management systems. Data providers often publish the information for research purposes, publishing compromises on the privacy of the data of users registered in the entities. There are many security techniques for databases based on data encryption processes in transactions, however, the techniques found compromise information. The development of the document used the deductive method and exploratory research technique that allows us to study the information in the articles presented. In this document we have proposed a Security Model Assessment Prototype for Databases, a Prototype of Security Management Architecture in Blockchain for a Database, a Database Security Algorithm, a Logical Structure of the Management System in Blockchain, and a Prototype to mitigate cyberattacks. The use of a hybrid blockchain model provides the system with the optimal security that public organizations in relation to test results performed with a 99.50% security effectiveness. The simulations presented in this document were conducted based on in-depth studies and the data were randomly placed with a range according to the study results based on real-world situations.

Voice user interfaces (VUI) have been increasingly used in everyday settings and they are growing in popularity. These interfaces have predominantly eyes-free and hands-free interactions. This kind of experiences continues to be an inceptive field compared to other input methods such as touch or using the keyboard/mouse. Thus, it is important to identify tools used to evaluate the usability of VUIs. This article presents a systematic review, in which we analyzed 57 articles and describes nine questionnaires used for evaluating the usability of VUIs, assessing the potential suitability of these questionnaires to measure different types of interactions and various usability dimensions. We found that these questionnaires were used to evaluate the usability of voice-only and voice-added VUIs: AttrakDiff, ICF-US, MOS-X, SUISQ-R, SUS, SASSI, UEQ, PARADISE and USE, where the SUS questionnaire is the most commonly used. However, its items do not directly assess voice quality, although it evaluates the general user interaction with a system. All the questionnaires include items related to three usability dimensions (effectiveness, efficiency, and satisfaction). The questionnaire with the most homogeneous coverage regarding the number of items in each aspect of usability is the SASSI questionnaire. It is a normal practice to use multiple questionnaires to obtain a more complete measurement of usability. We perceive the necessity to increase usability research about the differences between the voice interaction with diverse display types (voice-first, voice-only, voice-added) and the dialog types (command-based and conversational), and how usability affects the user expectations about the VUIs.

This article focuses on the food supply chain and aligns with the United Nations Sustainable Development Goal of Zero Hunger. They are looking to avoid food waste where Smart Cities must work. Food waste requires innovation with technology to change consumer's behaviors, efficient supply chain management, and innovative food production systems not to harm the environment. Among the economic impact, water and carbon footprint affects vital performance indicators into a Smart City. Hence, this work considers water and carbon footprint, as well as economic impact. We contribute to the Smart Cities, providing a multi-Agent simulation able to be scaled with an ontology with the purpose to plot different scenarios to stakeholders in a Smart City and help to avoid food waste situations. To simplify the simulation, as preliminary work, we used a typical food in Guadalajara Metropolitan Area, which is a Taco. The model covers a community in the Zapopan Municipality, where a developed urban study is the first proof of the system's concept. Furthermore, based on the simulation scenarios fed with real, local data, we discussed how we can integrate this multi-agent platform to face COVID-19. Finally, we want to help family businesses on the food supply chain using information technologies and applying digital processes to better adapt to new operation rules since COVID-19 to fight food waste since COVID-19.

Several challenges arises in any Smart City ecosystem, such as the governance (management) of heterogeneous IoT (Internet of Things) systems deployed and managed by different entities. Usually, IoT-based smart city applications operate in isolation, leading to a fragmented IoT landscape that hinders the managements of the IoT systems in an integrated way. Thus, it is required the involvement of city authorities to structure and organize city-wide interventions among the different IoT systems/applications; but also it is needed the infrastructure and technology that allow these city-wide interventions between the different levels of government (from upper tier authorities to down tier) for the proper management of heterogeneous IoT systems deployed within a Smart City by public as well as private entities. In this paper we propose an infrastructure for the Smart Governance of heterogeneous IoT solutions within Smart Cities, which provides useful features such as the spread of power, security constraints and scalability, among others. The infrastructure is described through 1) a new architectural model, as an adapted version of the basic architectural model, 2) the use of Digital Objects as crucial artifacts to represent, store and interact with physical and digital elements and 3) the use of open-source technology to implements the architectural model and supports the management of the Digital Objects.

Contexto. La evaluación docente en universidades públicas multicampus depende aún de promedios y encuestas cerradas, limitadas para captar la complejidad del desempeño académico. Ante ello, este estudio presenta una revisión sistemática (2019–2024) sobre el uso de la Inteligencia Artificial (IA) y el Procesamiento de Lenguaje Natural (PLN) en procesos de evaluación docente universitaria, siguiendo como innovaciones de proceso enunciadas en el Manual de Oslo, alineadas con los Objetivos de Desarrollo Sostenibles (ODS) 4 y 9. Problema. Los sistemas tradicionales en instituciones de alta matrícula presentan baja precisión analítica, escaso aprovechamiento de datos cualitativos y retroalimentación tardía. Ante esto, se plantea la pregunta de investigación: ¿qué técnicas y algoritmos reporta la literatura para el análisis integrado de datos cuantitativos y cualitativos en la evaluación docente universitaria? Objetivo. Analizar críticamente el uso de la ciencia de datos y la IA —especialmente el PLN— para mejorar la retroalimentación docente, superando las limitaciones de los enfoques tradicionales y promover procesos evaluativos oportunos, profundos y escalables. Metodología. Se siguió el protocolo PRISMA mediante búsquedas con operadores booleanos en Scopus, Web of Science, IEEE Xplore y ERIC. Tras aplicar criterios de inclusión y un proceso de revisión por pares en dos fases, se analizaron 17 estudios publicados entre 2019 y 2024. Hallazgos. Las técnicas como el análisis de sentimientos, el modelado de tópicos (LDA) y modelos de lenguaje de gran escala (LLM) —notablemente DistilBERT, con precisiones cercanas al 93%— superan consistentemente a los métodos tradicionales en la gestión de grandes volúmenes de información. Originalidad. Radica en la integración de literatura dispersa sobre IA y PLN en educación superior bajo un marco de innovación de procesos, articulando rigor técnico, empírico y ético. Conclusiones y Limitaciones. Se concluye que los modelos avanzados de IA y PLN poseen un alto potencial para transformar la evaluación en redes universitarias latinoamericanas al permitir retroalimentación personalizada. No obstante, persisten desafíos en la interpretabilidad de modelos, mitigación de sesgos, escasez de datos etiquetados en español y resistencia institucional, lo que abre relevantes líneas para futuras investigaciones y desarrollos aplicados.

A virtual assistant is an artificial intelligence software designed to perform various tasks and assist users in their daily activities. This technology is integrated into devices like smartphones, providing users with convenient, hands-free access. Popular virtual assistants include Amazon Alexa, Apple's Siri, Google Assistant, and Microsoft's Cortana. Today, virtual assistants are very popular in other contexts, such as education, because virtual assistants can function as customer service 24 hours a day, seven days a week, thus, for example, avoiding professors and service staff having to answer repetitive and easy-to-answer questions. In this work, we present an extended summary of the development of a virtual assistant for university professors. We are using a user-centered design approach to develop such a tool, as we want to involve end users from the beginning of the process. We hope to bring together several participants to obtain more valuable contributions