Ending poverty in all its forms everywhere remains the number one Sustainable Development Goal of the United Nations 2030 Agenda. Governments face challenges in measuring socioeconomic status with fine spatial resolution because traditional data collection methods, such as censuses and surveys, are time-consuming, labor-intensive, performed at long intervals, and cover only a limited population. This work is a data-driven study to analyze the digital traces left by humans in supermarket transactions and model the relationship between consumption behavior and the average per capita income, proposing a proxy to estimate socioeconomic status at the urban neighborhood level. We analyze more than 20 million supermarket shopping transactions in Guayaquil, the most populated city in Ecuador. Using customer consumption data, we created a basket graph and fed it into a graph neural network to predict neighborhood socioeconomic status. The model was trained with spectral and spatial convolutional filters using cross-validation to select the best approach for the prediction. The results show that the Chebyshev spectral convolutional filter has the highest predictive power to predict the socioeconomic status of the neighborhood, with R^2=0.91 . Our proposed approach contributes to measuring socioeconomic status at the neighborhood level to support policymakers in making informed decisions about resource allocation according to the needs of different geographical areas.

This study aims to analyze the sentiments expressed in social media posts (X and TikTok) concerning urban violence in five Ecuadorian provinces, namely, Esmeraldas, Guayas, Los Rios, Manabi, Pichincha, and Santo Domingo, from January 2021 to January 2025. The methodology uses web scraping tools to gather relevant comments, and integrates the RoBERTuito model, -a transformer-based model- with a BiLSTM layer, to provide critical insights into the dynamics of sentiment progression over time. Our hybrid approach produces a sentiment thermometer expressed in a trace map containing the five events considered the most violent in Ecuador with their respective chronology. Two of the facts that always remained with negative feelings were "corrupt judges" who released prisoners, as well as "missing children case" implying the rejection of the actions of the military involved with the minors for committing forced disappearance.

In the rapidly evolving landscape of modern education, Science, Technology, Engineering and Mathematics (STEM) disciplines stand out for their role in equipping students with the skills necessary to address complex real-world problems. Despite the critical importance of STEM careers, disparities persist, particularly among young women and students from public schools. This motivated the creation of an IoT bootcamp - referred to as PyTime IoT, a 2-day course aimed at high school students. This bootcamp integrates hands-on activities in Python programming and IoT systems that demonstrate the tangible application of theoretical STEM concepts through real-world scenarios. Our approach enhances participants' understanding of IoT and programming and also serves as a stepping-stone for high school students contemplating STEM careers. Preliminary results from the first two bootcamp editions indicate a positive shift in participants' skills and a marked increase in STEM career interest, suggesting that targeted bootcamps like PyTime IoT are effective in bridging the educational gap and inspiring the next generation of STEM professionals.

Interoperability is a property of software quality that is related to the cooperation between software systems for exchanging information. However, this concept is not well explained or understood. A theory would be useful to explain interoperability in terms of its essential elements and propositions. Theoretical contributions of interoperability are intended to formalize this concept by using common frameworks, models, and meta-models. However, tentative contributions developed in the past have failed to propose a theory of interoperability due to four reasons: (1) a disunified vocabulary is used, (2) the essential elements for describing interoperability are not well identified, (3) only a single level of interoperability is assessed, and (4) interoperability principles are not well formalized. This paper tentatively proposes an axiomatic theory of interoperability as a complementary approach to the existing knowledge. The proposed theory seeks to better formalize the concepts of interoperability and suggest actions aimed at establishing interoperability. After a brief review of related works and the state of the art, a set of axioms and propositions is presented. This theory is evaluated by a group of experts, and an example is presented to illustrate its use. Conclusions and future works are outlined at the end of the paper.

The development of Internet of Things (IoT) systems in university courses encourages students to use multiple skills. Hence, the importance of applying teaching methods like Project Based Learning (PBL) in the development of these kinds of systems for integrating hardware and software components while facing numerous real-life problems. This study presents an activity-based Approach for the Early Identification and Resolution of problems, in a checklist format, to be used by students for preventing them from wasting time in IoT academic projects. The checklist is based on an adapted taxonomy of problems in IoT systems. This study analyzed problems identified in 48 projects carried out by 183 engineering students registered in two courses in 2020 and 2021. For designing the structured checklist, we categorized 14 types of IoT problems, analyzed root causes and solutions, and created and evaluated a taxonomy of problems.

Interoperability is a software property for exchanging and using information between software systems.Some interoperability proposals are intended to use metamodels, models, and frameworks, in which visual representations are included.However, such proposals describe interoperability according to their point of view, focus of analysis, solution techniques, and specific level of interoperability; in addition, some of the visual representations use no standards, which lead to subjective interpretations by the reader.In this paper we propose a representation formalizing interoperability which includes essential elements and structural relationships among them.After a review of previous work, a summary of the analyzed literature is performed by using linguistic analysis in order to recognize the relationships among interoperability essential elements and mapping them to controlled language expressions.Finally, the mapping process is used to represent the interoperability essential elements and their relationships by using pre-conceptual schemas linked to the definition of each essential element.As a result, a pre-conceptual schema of the software system interoperability is proposed.Such a pre-conceptual schema is also used for explaining an interoperability lab study adapted from the literature.The proposed pre-conceptual schema explains the interoperability between two software systems and allows for characterizing interoperability problems and solutions.

Eradicating poverty in all its forms everywhere remains as the number one Sustainable Development Goal of the 2030 Agenda for Sustainable Development. Developing countries face challenges in measuring the progress of poverty rates at the intra-urban level because they use traditional data collection methods such as censuses that are costly in time and resources. Therefore, local and central governments need ways of producing reliable, accurate, and up-to-date indicators to design effective policies about resource allocation for poverty alleviation programs that prioritize the most vulnerable citizens. For this purpose, we propose to exploit patterns observed in developing countries, where mobile phone usage is pervasive even among the poorest, and the dominant mobile subscription modality is prepaid to purchase airtime credit in advance. Our study analyzes a novel digital source with more than 9M mobile airtime top-up transactions to calculate meaningful indicators of customer economic activity. We aggregate it at the neighborhood spatial resolution to build a regression model to predict the neighborhood socioeconomic status (per capita income). Using a Linear Regression with Regularization L2 (Ridge), we can explain the neighborhood socioeconomic status with a prediction rate of up to 74% for urban neighborhoods of Guayaquil and Quito, Ecuador.

The accelerated growth in exploiting vulnerabilities due to errors or failures in the software development process is a latent concern in the Software Industry. In this sense, this study aims to provide an overview of the Secure Software Development trends to help identify topics that have been extensively studied and those that still need to be. Therefore, in this paper, a systematic mapping review with PICo search strategies was conducted. A total of 867 papers were identified, of which only 528 papers were selected for this review. The main findings correspond to the Software Requirements Security, where the Elicitation and Misuse Cases reported more frequently. In Software Design Security, recurring themes are security in component-based software development, threat model, and security patterns. In the Software Construction Security, the most frequent topics are static code analysis and vulnerability detection. Finally, in Software Testing Security, the most frequent topics are vulnerability scanning and penetration testing. In conclusion, there is a diversity of methodologies, models, and tools with specific objectives in each secure software development stage.

The development of advanced mobile applications, which combines the use of IoT hardware and cloud computing services, is challenging in an academic environment; specialized knowledge of teachers in such subjects is required, as well as appropriate management of multidisciplinary groups of students with different knowledge and skills. This paper presents an educational model (CEMA Cloud-based Educational model for Mobile Application) where cloud computing services are used for developing mobile applications that are connected to IoT devices. The model includes the development of educational resources such as micro-training and lab sessions aimed to facilitate the learning process of IoT and Cloud Mobile Applications. Results indicate that students consider Github and Asana as tools easy to use. Also, students highly value the practical activities which are perceived as useful educational resources for achieving a high degree of competencies in managing projects using an agile methodology, Android programming and handling IoT sensors.

The concept of Big Data is being used in different business sectors; however, it is not certain which methodologies and process models have been used for the development of these kind of projects. This paper presents a systematic literature review of studies reported between 2012 and 2017 related to agile and non-agile methodologies applied in Big Data projects. For validating our review process, a text mining method was used. The results reveal that since 2016 the number of articles that integrate the agile manifesto in Big Data project has increased, being Scrum the agile framework most commonly applied. We also found that 44% of articles obtained from a manual systematic literature review were automatically identified by applying text mining.

DevOps is a modern software engineering paradigm that is gaining widespread adoption in industry. The goal of DevOps is to bring software changes into production with a high frequency and fast feedback cycles. This conflicts with software quality assurance activities, particularly with respect to performance. For instance, performance evaluation activities --- such as load testing --- require a considerable amount of time to get statistically significant results. We conducted an industrial survey to get insights into how performance is addressed in industrial DevOps settings. In particular, we were interested in the frequency of executing performance evaluations, the tools being used, the granularity of the obtained performance data, and the use of model-based techniques. The survey responses, which come from a wide variety of participants from different industry sectors, indicate that the complexity of performance engineering approaches and tools is a barrier for wide-spread adoption of performance analysis in DevOps. The implication of our results is that performance analysis tools need to have a short learning curve, and should be easy to integrate into the DevOps pipeline in order to be adopted by practitioners.

DevOps is a modern software engineering paradigm that is gaining widespread adoption in industry. The goal of DevOps is to bring software changes into production with a high frequency and fast feedback cycles. This conflicts with software quality assurance activities, particularly with respect to performance. For instance, performance evaluation activities --- such as load testing --- require a considerable amount of time to get statistically significant results. We conducted an industrial survey to get insights into how performance is addressed in industrial DevOps settings. In particular, we were interested in the frequency of executing performance evaluations, the tools being used, the granularity of the obtained performance data, and the use of model-based techniques. The survey responses, which come from a wide variety of participants from different industry sectors, indicate that the complexity of performance engineering approaches and tools is a barrier for wide-spread adoption of performance analysis in DevOps. The implication of our results is that performance analysis tools need to have a short learning curve, and should be easy to integrate into the DevOps pipeline in order to be adopted by practitioners.

Agile methodologies have been increasingly used in software development projects worldwide. However, there is little information about the adoption of these methodologies in Latin America. In this paper, we present a study conducted in Ecuador about the use, usefulness and causes of stop using agile methodologies in medium and large organizations. The results show that a considerable percentage of professionals do not receive formal training before adopting agile methodologies, and that a high percentage of organizations, especially public, decide to abandon its use. However, private companies from the Banking sector are the ones that keep using agile.

Looking for quality issues in a system can be a very demanding activity. In this article, we propose an approach based on text mining techniques to quickly identify usability and functionality drawbacks in a learning management system - LMS. The techniques were performed to 421 comments written by university students who frequently use a LMS. Results indicate that a dendrogram is a suitable tool to have a quick look of the issues faced by LMS’ users as well as their expectations about new functionalities that the system should provide. By using these techniques, we identified more than ten usability issues and the need for seven new functionalities to be implemented in the system.

Interoperability is commonly referred as a software quality sub-characteristic. Some proposals for formalizing interoperability include sets of concepts linked to this phenomenon. Such concepts differ among the proposals, resulting in a different terminology for referring to the same thing. Non-unified terminology of interoperability concepts exhibits three main problems: homonymy, synonymy and missing concepts. Due to such problems, crucial aspects of the interoperability process could be either misunderstood or just omitted, resulting in an ambiguous and incomplete description of interoperability. To solve these problems, we propose a terminology unification of interoperability concepts. This unification is based on published studies about the interoperability formalization. Results lead us to recognize six main concepts useful to unify the interoperability vocabulary.

Big data has become a subject of great interest among a variety of organizations, both from the scientific and business sectors. In this line, it is important to know the focus of attention that companies have on big data. This paper presents a study conducted in Ecuador about big data initiatives among large and medium companies. Results indicate that companies do not have a clear understanding of the implications of big data for their own benefits. Also, higher interest on big data initiatives comes from the private rather than the public sector. And, companies have a preference for contracting big data services from third-parties instead of hiring specialized personnel.

Considering that the images of different spectra provide an ample information that helps a lo in the process of identification and distinction of objects that have unique spectral signatures. In this paper, the use of cross-spectral images in the process of edge detection is evaluated. This study aims to assess the Canny edge detector with two variants. The first relates to the use of merged cross-spectral images and the second the inclusion of morphological filters. To ensure the quality of the data used in this study the GQM (Goal-Question-Metrics), framework, was applied to reduce noise and increase the entropy on images. The metrics obtained in the experiments confirm that the quantity and quality of the detected edges increases significantly after the inclusion of a morphological filter and a channel of near infrared spectrum in the merged images.

Despite the representation of a business process (BP) with Business Process Model and Notation (BPMN) can provide support for business designers, BPMN models lack of a formal semantics to conduct qualitative analysis. In this work, we describe the use of timed automata (TA) formal language to check BPs modelled with BPMN using the model checking verification technique. Two algorithms are introduced to transform a BPMN model into TA to obtain the formal specification of a BP-task model tantamount to a BPMN model. Our approach allows business analysts and designers to perform evaluation of BPMN models with respect to business performance indicators (e.g., service time, waiting time or queue size) derived from business needs. Our approach also incorporates the UPPAAL MC tool, as it is shown in an instance of an enterprise-project.

Considerando que las imágenes de diferentes espectros proporcionan una amplia información que ayuda mucho en el proceso de identificación y distinción de objetos que tienen firmas espectrales únicas. En este trabajo se evalúa el uso de imágenes cross-espectrales en el proceso de detección de bordes. Este estudio evalua el detector de bordes Canny con dos variantes. La primera se refiere al uso de imágenes cross-espectrales fusionadas, y la segunda al uso de filtros morfológicos. Para garantizar la calidad de los datos utilizados en este estudio se aplicó el marco de trabajo GQM (Goal-Question-Metrics), la cual fue utilizada como marco de trabajo para reducir el ruido y aumentar la entropía en las imágenes. Después de realizar los experimentos. Las métricas obtenidas en los experimentos confirman que la cantidad y calidad de los bordes detectados aumenta significativamente después de la inclusión de un filtro morfológico y un canal de espectro infrarrojo cercano en las imágenes fusionadas.

In this paper, the use of crossspectral images in the process of edge detection is evaluated, the main reason to use images of different spectra is that they provide extensive information that helps greatly in the process of identification and distinction of spectrally materials unique. The objective of this study is to assessment Canny edge detector with two variants. The first relates to the use of cross-spectral images merged, and the second using morphological filters. To ensure the quality of the data used in this study methodology GQM (Goal-QuestionMetrics) was applied as a framework to reduce noise and increase entropy. After the experiments, it is concluded that the edges detected significantly after the inclusion of a near infrared spectrum channel in the merged image, and variation of morphological filter.