Incorporating technologies across all sectors has meant that cybersecurity risk assessment is now a critical step in cybersecurity risk management. However, risk assessment can be a complicated process for organizations. Therefore, many authors have attempted to automate this step using qualitative and quantitative tools. The problems with the tools and the risk assessment stage in general are (1) not considering all the sub-steps of risk assessment and (2) not identifying the variables necessary for an accurate risk calculation. To address these issues, this article presents a systematic mapping review (SMR) of tools that automate the cybersecurity risk assessment stage based on studies published in the last decade. As a result, we identify and describe 35 tools from 40 primary studies. Most of the primary studies were published between 2012 and 2020, indicating an upward trend of cyber risk assessment tool publication in recent years. The main objectives of this paper are to: (I) identify the differences (reference models and applications) and coverage of the main qualitative and quantitative models, (II) identify relevant risk assessment variables, (III) propose a risk assessment model (qualitative and quantitative) that considers the main variables and sub-stages of risk assessment stage, and (IV) obtain an assessment of the proposed model by experts in the field of cybersecurity. The proposal was sent to a group of 28 cybersecurity experts who approved the proposed variables and their relevance in the cybersecurity risk assessment stage, identifying a majority use of qualitative tools but a preference of experts for quantitative tools.

This paper explores five multivariate techniques for information fusion on sorting the visual ripeness of Cape gooseberry fruits (principal component analysis, linear discriminant analysis, independent component analysis, eigenvector centrality feature selection, and multi-cluster feature selection.) These techniques are applied to the concatenated channels corresponding to red, green, and blue (RGB), hue, saturation, value (HSV), and lightness, red/green value, and blue/yellow value (L*a*b) color spaces (9 features in total). Machine learning techniques have been reported for sorting the Cape gooseberry fruits’ ripeness. Classifiers such as neural networks, support vector machines, and nearest neighbors discriminate on fruit samples using different color spaces. Despite the color spaces being equivalent up to a transformation, a few classifiers enable better performances due to differences in the pixel distribution of samples. Experimental results show that selection and combination of color channels allow classifiers to reach similar levels of accuracy; however, combination methods still require higher computational complexity. The highest level of accuracy was obtained using the seven-dimensional principal component analysis feature space.

La implementación de chatbots como ChatGPT en la educación STEM presenta beneficios y desafíos. Estas herramientas permiten a los estudiantes desarrollar habilidades tecnológicas y de resolución de problemas, así como mejorar la comunicación y recibir retroalimentación personalizada. En este artículo analizaremos el potencial de la integración de los chatbots en la educación STEM y exploraremos los beneficios que ofrecen, como el fomento del aprendizaje interactivo y personalizado, el desarrollo de habilidades en tecnología y comunicación, y la mejora de la eficiencia en la corrección de tareas. También discutiremos los desafíos que enfrentan los docentes al implementar esta tecnología, como la falta de conocimientos técnicos, la necesidad de formación especializada y las preocupaciones éticas y de privacidad. Proporcionaremos recomendaciones para superar estos desafíos, como ofrecer capacitación docente adecuada, seleccionar herramientas apropiadas y establecer políticas claras de protección de datos. Al finalizar, buscamos destacar la importancia de una implementación adecuada de los chatbots en la educación para maximizar sus beneficios y preparar a los estudiantes para un futuro impulsado por la tecnología.

Very small entities (VSEs) of software development have had a significant demand and economic impact in recent years, because most of them are the software producers for medium and big companies in order to satisfy the growing demand of software. In this context, it is important to ensure that they produce quality software to successfully meet the market needs. This task relies on having the knowledge and the experience to implement proven practices, which are contained in quality models and standards, to be able to develop quality software, while increasing their productivity and keeping or reducing their costs. A description of the implementation of ISO/IEC 29110 in Mexico, specifically at Zacatecas State is presented. This implementation was done as a strategy to increase the competitiveness of them. The study includes both, the strategy established to deploy the knowledge and the method followed to implement the ISO/IEC 29110 in four VSEs that uses scrum methodology as agile approach. The results show that the implementation of ISO/IEC 29110 was easy and with a high acceptance due to the benefits detected in the reinforcement of the VSEs’ processes without changing the way they work and solving their problems.

Around the world, the importance of Very Small Entities (VSEs), organisations having up to 25 people, has been constantly increasing. Worldwide, VSEs represent over 92% of the software industry. Therefore, two main needs can be highlighted: a) providing VSEs with resources to produce high‐quality software, and b) training software engineering undergraduates in proven practices to produce high‐quality software within the given schedule and budget. A logical way to meet these needs is transfering proven practices provided by software engineering standards. However, transferring the knowledge of software engineering standards is not always an easy task. The ISO/IEC 29110 is a series of international standards and guides that provide codified knowledge related to the software development process. The series was specifically developed to be used by VSEs. This study presents an exploratory analysis, conducted in 12 Mexican VSEs, which implemented the software Basic profile of the ISO/IEC 29110, to identify the pace at which they can adopt this standard to their environment. Besides, the benefits and difficulties encountered are provided. The results can be relevant for other VSEs interested in implementing this standard. Even if the exploratory analysis was performed in the VSEs of Mexico, this analysis can be of interest in other countries. The results obtained can help other VSEs that are interested in the adoption of this international standard to reduce the barriers to a successful implementation.