Actas del Congreso Internacional de Ingeniería de Sistemas
ISSN: 2810-806X (En línea) The International Congress of Systems Engineering (CIIS), organized annually by the Systems Engineering program within the Faculty of Engineering at the University of Lima, serves as a participatory platform with global reach. It focuses on exploring themes related to emerging technologies, facilitating the exchange of knowledge, experiences, and research in fields such as information systems, information technology, software engineering, computer science, and allied disciplines. This publication compiles the contributions from participants of each annual edition.
Más allá de las fronteras: inteligencias artificiales generativas para el avance académico sostenible
Desde que fue lanzado en noviembre del 2022, el chatbot transformador generativo preentrenado (generative pre-trained transformer, en inglés) de Open IA, más conocido como ChatGPT, se ha convertido en una de las aplicaciones de inteligencia artificial más populares del mundo (OpenIA, s. f.). Académicos a lo largo y ancho del mundo han expresado su preocupación acerca de cómo ChatGPT está transformando dramáticamente el panorama pedagógico y de la investigación. En este artículo discuto algunas de las mejores prácticas para usar ChatGPT con propósitos académicos.
Hacia la sostenibilidad energética: una revisión de literatura del desarrollo de software verde
En el ámbito organizacional y empresarial, el creciente consumo de energía eléctrica por parte de los equipos informáticos representa un desafío en términos de costo, así como también de impacto ambiental. Para abordar esta problemática se propone una revisión de literatura para recopilar y examinar los avances más recientes en el campo del software verde y su impacto en la eficiencia del consumo de energía. Para llevar a cabo esta revisión de literatura, se adaptó y aplicó la estrategia de búsqueda PICo y se seleccionaron un total de 23 artículos relevantes. En relación a la problemática abordada, se ha desarrollado el concepto de green software, el cual busca crear programas eficientes y sostenibles que optimicen el consumo de energía. Se exploran las herramientas y prácticas de diseño de software sostenible, técnicas para el desarrollo de sistemas eficientes en mención de consumo energético y enfoques en cómo abordar los problemas del consumo de energía en centros de datos y los servicios basados en la nube.
Design of an application that recognizes medicines through a camera by means of Artificial Intelligence “AI-Pills”
In this project, we will make a proposal for an innovative mobile application capable of satisfying multiple needs of some people. The need to recognize the right pill in real time, just by scanning it with their camera. For this, we start from a very common need in old people and people with poor vision, which makes it difficult for them to find the name of the pill because of the size of the letters. AI-Pills is a revolutionary solution designed to help users, especially the ones with vision problems, to accurately identify their medications. The application uses advanced image recognition technology and a comprehensive drug database to provide reliable information about prescribed drugs.
Control energético de motores de combustión mediante redes neuronales para aplicaciones de software de automoción
Este artículo se enfoca en la aplicación del control mediante redes neuronales para la generación de energía en un motor de combustión interna. Se desarrolló una arquitectura de red neuronal de dos capas y se la probó utilizando datos de laboratorio obtenidos de un dinamómetro de banco para identificar con precisión los parámetros de la red. Esta se utiliza para establecer una correlación precisa entre la magnitud de las señales de actuación y las variables fundamentales responsables de regular la generación de energía dentro del sistema. El sistema de control implementa una rutina de programación de ganancia para ajustar la ganancia del controlador, lo que disminuye el incremento para valores de error bajos. Se presenta un modelo de generación de energía que permite diseñar un motor virtual, lo cual facilita el desarrollo de estrategias de control precisas. Para garantizar el funcionamiento seguro del motor, se implementa una rutina de seguridad que previene que la acción de control adquiera valores que podrían tener un impacto negativo en la respuesta del vehículo a las instrucciones del conductor. El controlador desarrollado demuestra un bajo error absoluto promedio en condiciones de estado estable y un bajo tiempo promedio de subida y caída durante estados transitorios, asegurando la capacidad de conducción y el buen rendimiento del motor. Para habilitar la aplicación en software, en estructuras como el hardware-in-the-loop y la unidad de control del motor, se implementan sistemas para garantizar la operación en tiempo real.
Prediction of voltage stability in smart power grids
Smart grids are a system of electricity transmission networks that enable efficient use of electricity without affecting the environment. A smart grid is considered stable when it can maintain reliable and consistent operation while effectively managing various factors that can cause outages or imbalances within the power grid, stability is important since the entire transmission process is time-dependent. In this work, Deep Learning is employed to predict stability in this type of network. A balanced and free database of 60,000 observations with consumer and producer information obtained from simulations was used. This work concludes that this technique obtained a high performance (Accuracy = 97.98 %), which allows us to affirm that Deep Learning can be safely considered for this task. The number of epochs significantly influenced the performance of the ANNs, those with more complex architectures presented a better Accuracy.
Exploración de la Identificación del Riesgo de Accidente Cerebrovascular mediante Machine Learning: Una Revisión Sistemática
Este trabajo busca sistematizar los estudios sobre la identificación del riesgo de sufrir un accidente cerebrovascular (ACV) en las bases de datos Web of Science y Scopus y su relación con el machine learning. La información se organizó en tres secciones: factores de riesgo del ACV, técnicas de preprocesamiento de datos y técnicas para identificar el riesgo de ACV haciendo énfasis en las características más relevantes. Los principales resultados son los siguientes: los factores de riesgo se dividen en modificables (ambiente de trabajo y contaminación del aire) y no modificables (sexo, hipertensión). Las técnicas de preprocesamiento más utilizadas son SMOTE, estandarización y eliminación/imputación de valores. Las técnicas más usadas para identificar el riesgo de sufrir ACV son support vector machine, random forest, logistic regression, naive Bayes, k-nearest neighbors y decision tree.
Heuristics in evaluating the usability of mobile applications – concepts and application
Usability is an essential factor in the development of products and interfaces, seeking to ensure that users can use a system effectively, efficiently and satisfactorily. The International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) definition highlights that usability is related to the ability of a product to be used by specific users to achieve specific goals in a given context. There are several heuristics and principles that can be applied to evaluate and improve the usability of interfaces. Among them, the principles of efficiency, learnability, memorability, satisfaction and errors stand out. This article presents, through a bibliographic review methodology, the concepts and applications of usability heuristics, their importance in the evaluation of mobile applications and also presents Nielsen’s heuristics as the most used basis in the development of systems for evaluating the usability of mobile applications.
Planning a Systemic Restructuring of the UNCAUS Medical Education Unit
In this article, we present a project to achieve the systemic restructuring of the Medical Educational Unit, dependent on the National University of Chaco Austral, Argentina. This Medical Unit has a variety of data about its patients, which is currently not automated in a patient’s Medical History. Because of that, it is difficult to obtain information with high added value for decision-making based on these records, as well as the development of tools for training medical students at the University. Using systemic techniques and tools, we planned changes to transform the data into structured knowledge. We are considering innovations through the incorporation of computer tools, such as databases and others based on Artificial Intelligence (AI), with the aim of representation and manipulation of knowledge to create models powerful enough to study real-life situations. Likewise, for the restructuring to be systemic, mechanisms are devised so that current employees accept the proposed changes without feeling underestimated.
Sustainability and Artificial Intelligent-based Systems
Artificial intelligence (AI) has evolved with advancements such as deep learning, Python, and deep neural networks. These advancements have driven the rise of AI, with developments like XAI, Small Data, and ImageNet. Generative AI is changing the economy and is expected to have a significant impact in areas such as content creation, software development, and marketing. The European Commission proposes AI regulation based on a risk approach, with different levels of risk and requirements according to the AI category. Software sustainability is multidimensional, covering technical, economic, environmental, and social aspects. Explainability and transparency in AI models are crucial to ensure accountability and trust in their use. Integrating AI systems with legacy systems and processes involves technical and economic considerations and can generate benefits such as process optimization but also requires significant investments.
Copyright (c) 2023 Actas del Congreso Internacional de Ingeniería de Sistemas
Review of obfuscated malware detection algorithms based on machine learning
Malware developers increasingly evolve their techniques to effectively attack the system, one of these techniques is code obfuscation that makes it difficult to detect malware in the traditional mechanisms used by current antiviruses. Given this, it is proposed to review articles related to the detection of obfuscated malware with machine learning to choose the best analysis techniques for this type of malware and use the best algorithms for future experimentation with them.
Copyright (c) 2022 Actas del Congreso Internacional de Ingeniería de Sistemas
Sero tawa pukllay: online/offline remote educational system
The recovery of ancestral Inca mathematical knowledge through the Tawa Pukllay method is a semiotic alternative that makes learning basic arithmetic operations easier. Rural education presents many challenges made evident by the COVID-19 pandemic. The online/offline system was a concrete response to some of them, allowing rural children in an Andean community in Peru to continue learning basic arithmetic by playing a serious game with a bilingual (Quechua and Spanish) interface on electronic tablets. SER0 Tawa Pukllay stimulated children's initiation to use electronic devices while maintaining their identity, reinforcing the use of Quechua, their Andean worldview, and their autonomy. The process fostered self-learning and motivation as the children strove to complete the challenges posed by learning to read numbers and solve arithmetic problems presented in a playful pedagogical approach to developing their mathematical thinking.
MOOCs for continuing education in information systems during the pandemic
The continuing education of the Universidad Autónoma de Campeche community during the COVID-19 pandemic was carried out through the Coursera for Campusvprogram. This paper presents the methodology and analysis of the results obtained from the university community that participated in the platform program in the area of Information Systems.
Dynamic information and analytical system for assessing and managing acquired knowledge
The paper presents the concept of a system for the intellectual digital e-university of the future (IDEUF) based on an integrated database that ensures the correct function of the whole university system. Particular attention is paid to the process of checking students’ required and current level of acquired knowledge, which is considered one of the main parts of the educational process. The article provides a detailed comparative analysis of actual testing systems and presents a proper testing system model within the IDEUF framework. The proposed system considers the testing principles, scales, and technologies for test building and preparing educational materials.
Global analysis of scientific production regarding organizational learning in higher education
The aim of this article is to study through a bibliometric and content analysis, the literature available in Web of Science on organizational learning in the context of higher education. The results reveal that, since 2012, there is an increased production of knowledge in the field; publications belong mainly to US authors; a low level of international scientific collaboration is observed; a large part of the publications are in education journals; and there are two main streams of analysis, the first one discusses organizational learning as a central focus for the adaptation process, while the second one concentrates on looking for relationships between this and other desirable outcomes. The findings are discussed and implications for the develo pment of organizational learning are presented.
Hybrid work: Is it a blessing or a curse?
Enabled by new technologies and informed by the experiences during COVID pandemic, many employees would like to continue working remotely (Teevan et al., 2022), as this enhances their work-life balance and well-being (George et al., 2021). Many leaders, on the contrary, would like to see their employees back to the office (Microsoft, 2022). To address this challenge, many organisations are considering moving to the hybrid work arrangements, allowing their employees to work some days in the remote settings, but requesting them to be in the office some other days (e.g., Barerro et al., 2022). This approach is often seen as a solution that meets the interests of both employees and employers and can capture the benefits of both remote and in-office work (e.g., Choudhury et al., 2022). But does hybrid work really work?
Supervised machine learning algorithms to determine the location of Wi-Fi devices
This article aims to choose the best-supervised machine learning algorithm for locating a terminal that supports Wi-Fi in a specific scenario. It uses a dataset of 2000 received signal strength indicator (RSSI) records obtained from 7 access points (AP), loaded into eight supervised machine learning algorithms. The algorithm that produces the most accurate prediction is then chosen, even when fewer APs are available. The Naive Bayes algorithm achieved the highest accuracy for the 7AP (99 % accuracy) scenario and for a smaller number of APs. The algorithms based on neural networks had the worst performance. The article proposes future research on the location of Wi-Fi devices indoors.
Estimation of the number of injuries in traffic accidents within the province of Lima using multiple linear regression models and PCA
There is a high probability that traffic accidents will leave several victims in such a critical condition that they will require immediate medical care to survive. In Peru, where healthcare resources for emergencies are quite limited, it is essential to prioritize some cases over others. This work aims to build a model that estimates the number of people injured in traffic accidents in the province of Lima based on 2016 and 2017 accident records from the Police Station National Census. It develops a multiple linear regression analysis based on variables taken from the accident records and principal component analysis to represent better and simplify some of the original model variables and work with the most relevant information. The model seeks to provide valuable information to medical professionals to prioritize attention and maximize the number of survivors.
Gamit!: a gamification platform for university students based on a reward system
Gamification is a technique that uses game concepts in domains different from games. There are many gamification initiatives in education because of its impact on student experience, especially in motivation (Surendeleg et al., 2014; Hamari et al., 2015; Rincón-Flores et al., 2018) and focus (Hamari & Keronen, 2017). A methodology for easy game-concept adoption and tools to help teachers and students in gamification activities are necessary for proper gamification. Gamit! uses a narrative-based methodology to make gamification easier to follow and implement in classes.
Challenges of deep learning in computer vision
Computer vision is a field of study within artificial intelligence that focuses on developing computational techniques to perceive the world through visual data, such as video or images. Deep learning has proven to be efficient in visual data analysis and interpretation. Nevertheless, it faces countless challenges, given its application in several computer vision tasks. This panel brings together deep learning experts, who will share information about deep learning applications and challenges to overcome in their research fields regarding computer vision.
Overcoming the challenges for the inclusion of women in STEM
Disruptive changes in organizations and societies that emerged from technology, economy, and environmental areas, challenge the flexibility of companies and institutions. In this context, the demand for professionals and researchers has increased, especially in areas such as STEM. The lack of researchers and professionals in STEM is a problem, especially in contexts such as Latin America. In this sense, the inclusion of women in STEM is seen as a key challenge. Consequently, it has received increasing attention from scholars and policy-makers. Several ongoing projects (e.g., ELLAS Project, financed by the International Development Research Center – Canada) have been working on different initiatives to close gender gaps in STEM. In this panel, we will discuss the main barriers to the inclusion of women in STEM in Latin America. In addition, we will address initiatives and good practices that have contributed to mitigating some of these barriers. Then, we will discuss these successful practices’ applicability in contexts such as the Peruvian.
Trustworthy Autonomous Systems (TAS): The Verifiability Approach
Autonomous systems are taking over the decision-making in many crucial aspects of our lives. Trust in them will help users benefit from such systems without harming themselves. Establishing the right level of trust involves a holistic validation and verification process, accounting for aspects such as interactions with the physical world and human users. In this talk, I present our ongoing effort to provide a holistic framework for ensuring the verifiability of autonomous systems
Languages and models underlying knowledge graphs
A knowledge graph is a large database that integrates information from different data sources, this with the objective of supporting knowledge extraction, and allowing the transformation of such knowledge into value for the users. Such database is represented as a graph where the nodes represent entities, and each edge represents a relationship between nodes, or the attribute of a node. The objective of this article is to review the data models used to represent knowledge graphs, and the query languages that allow to extract explicit and implicit information contained in such graphs.
The EU trustworthy artificial intelligence: how the EU wants to regulate the artificial intelligence practices
The European Union (EU) recently proposed in April 2021 an Artificial Intelligence legal framework. The EU highlights the advantages of using Artificial Intelligence (AI) specially in the areas of prediction, optimizing operations, resource allocation and personalizing service delivery. Nevertheless, the EU considers that the AI might bring new risks and negative consequences for individuals and society when an AI system violates the EU values and fundamental rights. This paper explains how the EU is proposing a legal framework for trustworthy AI. Tech or nontech companies and professionals should be aware about what kind of AI applications and practices might be prohibited or restricted within the EU. To be more specific, this paper focuses on what type or AI system can be considered as unacceptable risk, high or low risk AI.
Copyright (c) 2021 Actas del Congreso Internacional de Ingeniería de Sistemas
McQueen Ambulance: optimization of routes for ambulances based on the distance between the accident and the health center
Currently, pre-hospital transport, such as that provided by ambulances for emergency medical care or support for primary care, faces a significant problem in Lima: traffic, which is also affected by the time that elapses from the emergency that occurs until the health center sends an ambulance for care. In this sense, it is essential to optimize the response time of this process so that medical attention is provided as quickly as possible, attention time is crucial during an emergency. Therefore, this work proposes a methodology to optimize the route and time of dispatch of an ambulance for emergency care, considering the distance between the health center and the place of occurrence of the emergency, specifically focusing our pilot on the district of San Borja. On the other hand, on the patient’s side, it is sought that he can request and access an ambulance as quickly as possible through a single platform. For this, an open-source program has been designed and coded in Python with the OSMnx and Networks libraries, which also uses Dijkstra's algorithm to optimize the route to follow to the emergency site. The experimental tests were able to validate that through this process, it was possible to optimize the action time for the attention of cases by facilitating the logistics necessary to send the ambulances, also considering that this saved time could offer the possibility of saving the lives of the people involved in such events.
HONEYCLOUD - Combining Research and Teaching in a Project for the Digitalization of Beekeeping
The HONEYCLOUD project is a research project of the University of Würzburg in the field of bioeconomics. Within the scope of technolog y transfer, tools and methods that were originally developed for aerospace applications are now to be made available for precision agriculture. In particular, the field of precision beekeeping was selected for this purpose. An IT infrastructure to support the honeybees and the work of beekeeping is to be developed. The aim is to ensure that the electronics remain unobtrusive and that the beehive does not become a switch cabinet. This results in an experimental test setup that can be used in the field of teaching Internet-of-Things (IoT) systems. In this way, hardware and software components are created in the project and used directly in teaching.
Implementing Machine Learning Algorithms to Predict Donor Status: Preliminary Work with Data from an Institution of Higher Learning
Identifying potential donors allows institutions of higher learning to conduct more effective fundraising campaigns. Machine learning classification algorithms can be useful in building models to predict donor status. However, when data contains imbalanced classes, like the data we used for this project, models tend to over-index the majority class, which was non-donors in this case. These results have significant implications for institutions in that they may not pursue entities that may, in fact, become donors. In order to improve the usefulness of our model, we used a resampling technique called random undersampling (RUS) to balance the data and also the area under the receiver operating characteristic curve (AUC-ROC) metric to evaluate the performance. Our final model improved its predictive power from 67% to 76%. Institutions of higher learning can use this machine learning model to more efficiently target the pool of potential donors, saving money and time. Future research will focus on improving the predictive accuracy of our model by exploring other data manipulation techniques that minimize the effect of imbalanced data, changing thresholds for classification algorithms, and using genetic programming and feature engineering.
Open-Source Software & Personal Medical Devices: Interpreting Risk Through an Evaluation of Software Testing
The availability of powerful low-cost hardware and advanced software tools has made open-source medical devices possible. Deciding to use an open-source medical device may require acceptance of some risk. Fully comprehending the risk level is essential since failure of the software or the medical device is dangerous. As many medical applications contain complicated codes, an excellent method for understanding software readiness is to evaluate how much testing has been completed on the software. As a case study, this project evaluates the level of testing performed on software components of the Loop Artificial Pancreas system. Classic methods for evaluating complicated source codes are used to demonstrate how much testing is needed in a project. Our analysis shows that the Loop Artificial Pancreas system (master branch) has been thoroughly tested with most of the faults likely discovered. By using classic software engineering metrics and techniques, it is possible to gauge how completely an open-source medical product has been tested and make an educated decision about the risk associated with using it.
Our study introduces the emotion twenty questions (EMO20Q) game, an experiment into the cognition and expression of emotions in ordinary people who speak Chinese. The preliminary results show that such a game is felicitous and that the questions generated to describe emotions have commonalities with earlier studies conducted in English.