The modernization of public administration software systems increasingly adopts microservices architectures to achieve scalability, flexibility, and resilience. However, the complexity of distributed systems presents challenges in access authorization management. This case study presents the SIARE-Artefactos solution, designed to automate the registration and authorization of resources within Paraguay’s Integrated State Resource Management System (SIARE), leveraging OAuth 2.0 and Spring Boot Starter to ensure secure and efficient authorization processes that automate authorization, reducing configuration time from minutes to seconds, while its integration into CI/CD processes ensures consistency, minimizes errors, and enhances deployment reliability. SIARE-Artefactos has been implemented and used by public employees in Paraguay, allowing for validation with real actors. The validation of the proposal shows that the solution is replicable to other cases with similar challenges and resources and demonstrates significant improvements: a 60% reduction in configuration time and enhanced system reliability, highlighting its potential to transform large-scale public administration systems using modern DevOps practices and agile methodologies.

Detecting remote homologs, proteins that share evolutionary ancestry despite low sequence similarity, remains a central challenge in computational biology. The Structural Classification of Proteins extended (SCOPe) database organizes protein domains into superfamilies based on structural and functional evidence of common origin, making it a widely used benchmark for remote homology detection. In this study, we investigate the effectiveness of protein language model (PLM) embeddings for predicting SCOPe superfamilies directly from sequence. We introduce DOMCLASS, a deep learning framework that combines supervised contrastive learning with a distance-weighted K-NN classifier to learn and exploit an embedding space aligned with SCOPe superfamily annotations. Our empirical results show that general-purpose PLM embeddings already outperform sequence similarity- and profile-based methods for remote homology detection, and that contrastive learning can further improve performance, even in challenging low sequence identity settings.

This study presents the experience of Autumn of Code UC as a new modality for the semester project in the Software Engineering II course at the Universidad CatÓlica Nuestra Señora de la Asunción (Paraguay). Students participate in a real Open Source project under the mentoring of a leading professional or a company. The study follows a qualitative approach and focuses on the observations and reflections on the experience of a privileged actor in the process. The main findings are: i) Autumn of Code UC is contributing to a greater linkage with the productive sector; ii) it helps the development of skills valued in professional performance; iii) the quality of the contributions is variable but usually does not reach the use of the community; iv) the main motivations for participation are: mentoring, enriching the curriculum vitae, and learning in challenging projects. A challenge is to engage the mentors, although companies are motivated by the talents they can incorporate into their team.

SOS TUTORÍA UC’ is a student connection application aimed at facilitating academic assistance between students through external tutoring outside of the application. This study presents the development and validation of the experience in the application by evaluating the importance of incorporating the dimension of personality traits, according to the Big Five model, in the process of recommending students for academic tutoring. The goal is to provide support for students to find others with greater knowledge and with a personality that is “different”, “similar” or “indifferent” to their own preferences for receiving academic assistance on a specific topic. To achieve this, a responsive web application was designed and implemented, integrated with the WeNet platform, which provides various services for user management and user recommendation algorithms. The integration with the platform was successful in terms of components, and the results of the recommendation system testing were positive but have room for improvement seeking to better satisfy the personality requirement and allowing the requester to choose whether to diversify the recommendation by gender. Additionally, participants highlighted the importance of considering personality traits in the tutor-tutee matching process, expressing the desire for additional information and parameters to facilitate tutor selection, such as compatibility levels and more detailed personality trait profiles.

WeNet is a multidisciplinary project aimed at introducing a platform capable of enhancing socially aware, diverseconscious, richer, and deeper social interactions. This study presents the integration and validation of a tutoring application, named SOS Tutoring UC, based on social interactions on the WeNet platform. The validation has encompassed both the system and the diversity-conscious matching algorithm. The experience reveals several relevant challenges for integrating vertical applications on the WeNet platform and underscores the pivotal role played by understanding diversity-conscious matching algorithms in the integration process.

Over the past years, the service mesh paradigm has gained traction, solidifying its utility in modern infrastructures. Additionally, numerous projects in the field have recently incorporated support for io_uring — a technique to avoid system calls — affirming the trend in exploring this new approach to accelerate processing capabilities in network services. Within this context, Envoy is also in the process of gaining io_uring support. This work proposes the exploration of metrics to measure the impact of io_uring, which avoids the cost of utilizing resource-intensive system calls in an Envoy-based service mesh. Alongside a proposed measurement strategy, the design and implementation of a testing environment are presented. Test results showcase improvements in both latency performance and bandwidth utilization efficiency.

WeNet is a multidisciplinary project that proposes a paradigm of social relations mediated by computers connected through the Internet and having diversity as the focal point. To this end, an online platform has been developed that is capable of fostering richer and deeper diversity-aware social interactions and specific applications to respond to specific needs. In particular, this study focuses on a tutoring application, called SOS TUTORÍA UC. This study describes and analyzes the evaluation of user perception when using SOS TUTORÍA UC, as well as its UX status, in order to propose improvements. Seventeen users participated in the study, for which the questionnaires SEQ, SUS, UEQ+ and an interview with open questions were used. The results have been encouraging and suggest that the main factors affecting a good UX are ease of understanding, usability, intuitive use and effectiveness. Finally, proposals for improvements were collected taking into account the factors that influence the user experience and the recommendations given by the participants.

Emerging infectious diseases such as COVID-19, caused by the SARS-CoV-2 virus, require systematic strategies to assist in the discovery of effective treatments. Drug repositioning, the process of finding new therapeutic indications for commercialized drugs, is a promising alternative to the development of new drugs, with lower costs and shorter development times. In this paper, we propose a recommendation system called geometric confidence non-negative matrix factorization (GcNMF) to assist in the repositioning of 126 broad spectrum antiviral drugs for 80 viruses, including SARS-CoV-2. GcNMF models the non-Euclidean structure of the space using graphs, and produces a ranked list of drugs for each virus. Our experiments reveal that GcNMF significanlty outperforms other matrix decomposition methods at predicting missing drug-virus associations. Our analysis suggests that GcNMF could assist pharmacological experts in the search for effective drugs against viral diseases.

In today's world, characterized by the massive generation of data, a major problem related to data manipulation occurs when people's privacy is violated. To face this situation different regulations and solutions, to help users get in control over their data, emerged. However, many of the solutions require a certain level of knowledge in the field of privacy or offer unclear information that does not facilitate a real control of their data by the users. Added to this is the unfriendly user interface design as one of the factors that prevents users from managing their privacy settings effectively. Thus, in this work we explore the effect of the application of Participatory Design (PD) techniques in the implementation of privacy enhancing technologies. In particular, we focus on the use of PD for the design of a privacy dashboard that encourages the immersion of users with privacy issues and gives them greater control with a user interface according to usability criteria. The evaluation of the PD process, which has resulted in a high-fidelity prototype of the dashboard, shows encouraging results and greater user immersion in privacy management.

In the context of the project “WeNet: Internet of us” we are studying the role of diversity in relation to Internetmediated social interactions. In this paper, in particular, we analyze a possible relationship between personality aspects and social interaction mediated by digital platforms. More specifically, we rely on the five personality traits (Extraversion, Agreeableness, Conscientiousness, Emotional Stability and Openness to Experience), commonly referred to as “Big-five”, and associate them to automatically extracted behavioral characteristics derived from the experience of using a Chatbot for a closed community of students at the Universidad Católica “Nuestra Señora de la Asunción” (UC). The personality data comes from a self-report made by the users through questionnaires. The main results show very positive appraisals about the use of the Chatbot in terms of user experience and its main functionalities. As for the role of personality in relation to the main use of the Chatbot, although further experience is required to confirm trends, the results suggest that there are some correlations between some of the five personality traits and the length of questions and answers as well as the selection of best answers.

This paper explores and offers guidance on a specific and relevant problem in task design for crowdsourcing: how to formulate a complex question used to classify a set of items. In micro-task markets, classification is still among the most popular tasks. We situate our work in the context of information retrieval and multi-predicate classification, i.e., classifying a set of items based on a set of conditions. Our experiments cover a wide range of tasks and domains, and also consider crowd workers alone and in tandem with machine learning classifiers. We provide empirical evidence into how the resulting classification performance is affected by different predicate formulation strategies, emphasizing the importance of predicate formulation as a task design dimension in crowdsourcing.

The state of the sidewalks in Asunción (Paraguay) is far from being optimal. There are many problems related to obstructions, necessary repairs, lack of ramps, unevenness of surface, among others. In addition, the Municipality of Asunción does not have automated mechanisms to know their updated status. In this work we propose SmartMoving, a mobile application that collects information on the state of the sidewalks, with the help of citizens, and recommends pedestrian paths with less obstacles. The application can be especially useful for people with reduced mobility, as well as for the Municipality of Asunción. This type of application is based on citizen participation, since it receive data from them, and therefore requires a particularly friendly user experience, adapted to users and their daily context. Therefore, the need for a participatory and user-centered design, as a basis for the development of the application. Therefore, in this article we present the user-centered design process that has been followed for the development of SmartMoving, involving users with reduced mobility.

Crowdsourced civic engagement is a novel form of democratic participation that allows citizens to share their ideas and deliberate in a multitude of diverse participatory processes that are emerging all over the world, influencing, often with binding power, urban plans, city budgets, and even legislation, among many other forms of public policy decisions. As a result, hundreds of thousands of civic contributions are produced as ideas, comments, and proposals circulate among citizens and between them and government officials, generating an avalanche of mostly unstructured data, which decision-makers have difficulty to manage. Sentiment analysis techniques have the potential to process and classify these contributions in ways that can make it easier to make sense of them. In this paper, we present the design and implementation of a rule based sentiment analyzer that integrates a lexicon, optimized for the Spanish language and the application domain of civic contributions. We present the results of our first evaluation and discuss the aspects of the proposal that have room for improvement in future work.

A low average number of people per private vehicle and inappropriate road infrastructure results in heavy traffic that wastes space, time and money for the people involved. To optimize these resources, it is intended to promote carpooling between people who share the same destination, for example, colleagues at work or students at a university. This paper presents UCarpooling, a matching system for commuting between people of a same institution. UCarpooling is aimed at optimizing the number of passengers in vehicles during routine trips to and from work or study. The difference with respect to other similar proposals is that UCarpooling takes into account logistical details (place of departure, time of entry, etc.) and personal traits (if you smoke, what genres of music you listen to, etc.) as variables to calculate the percentage compatibility that different people have to carry out a carpool. A simulation of the use of UCarpooling in a university in Asunción, Paraguay, yields favorable data reaching the conclusion that its adoption is quite beneficial for the institution that adopts it, the people who use it, and the cities where it is adopted.

This paper reports on the challenges and lessons we learned while running controlled experiments in crowdsourcing platforms. Crowdsourcing is becoming an attractive technique to engage a diverse and large pool of subjects in experimental research, allowing researchers to achieve levels of scale and completion times that would otherwise not be feasible in lab settings. However, the scale and flexibility comes at the cost of multiple and sometimes unknown sources of bias and confounding factors that arise from technical limitations of crowdsourcing platforms and from the challenges of running controlled experiments in the “wild”. In this paper, we take our experience in running systematic evaluations of task design as a motivating example to explore, describe, and quantify the potential impact of running uncontrolled crowdsourcing experiments and derive possible coping strategies. Among the challenges identified, we can mention sampling bias, controlling the assignment of subjects to experimental conditions, learning effects, and reliability of crowdsourcing results. According to our empirical studies, the impact of potential biases and confounding factors can amount to a 38% loss in the utility of the data collected in uncontrolled settings; and it can significantly change the outcome of experiments. These issues ultimately inspired us to implement CrowdHub, a system that sits on top of major crowdsourcing platforms and allows researchers and practitioners to run controlled crowdsourcing projects.

Due to the complexity of software systems and the high probabilities of new errors appearing in any stage of the software life cycle, techniques for quality verification are needed. Software testing is a widely used approach but, due to the costs involved in this process, development teams often debate its applicability in their projects. In the endeavor to reduce the complexity of this process, this document presents an approach for software development based in Test-Driven Development (TDD) supported by Model-Based Testing (MBT) tools to allow automatic test-case generation. Besides, a toolkit for the generation of unit and acceptance tests for Web applications is proposed.

Many Web applications have among their features the possibility of distributing their data and their business logic between the client and the server, also allowing an asynchronous communication between them. These features, originally associated with the arrival of Rich Internet Applications (RIA), remain particularly relevant and desirable. In the area of RIA, there are few proposals that simultaneously consider these features, adopt MDD (Model-Driven Development), and use implementation technologies based on scripting. In this work, we start from MoWebA, an MDD approach to web application development, and we extend it by defining a specific architecture model with RIA functionalities, supporting the previously mentioned features. We have defined the necessary metamodels and UML profiles, as well as transformation rules that allow you to generate code based on HTML5, Javascript, jQuery, jQuery Datatables and jQuery UI. The preliminary validation of the proposal shows positive evidences regarding the effectiveness, efficiency and satisfaction of the users with respect to the modeling and code generation processes of the proposal.

Chagas disease is a parasitic disease, endemic in South America. As of today, there is no effective treatment in its chronic stage. We have recently identified 134 FDA approved drugs with potential antitrypanosomal activity. In this paper, we propose a novel method for selecting combinations of drugs (drug cocktails), to provide a more effective treatment against Chagas disease. We define three measures to evaluate the predicted performance of a cocktail, establishing in this way a mathematical foundation for its analysis. This allows us to model the drug cocktail selection as a multi-objective optimisation problem, that we show can be solved efficiently with state-of-the-art evolutionary algorithms. Our analysis retrieves 57 drug cocktails containing between 2 and 6 drugs. We discuss the improvement of the cocktail selection given by our method, and the application of this approach to the identification of cocktails against other parasitic diseases.

This study focuses on model-to-model transformations, as part of the Model-Driven Development (MDD) approach, for Rich Internet Applications (RIA). The main aim of this study is to identify fields that require further contributions, and/or research opportunities in the previously mentioned context. We applied mapping studies techniques, allowing a broader extent than reviews, by taking into consideration every paper related to our field of interest and not only those based on empirical studies. From an initial set of 80 papers, we selected 26 papers first, and another 3 papers were added later. Therefore, we considered 29 research papers. The performed analysis led to various considerations. Among the important ones, we can mention: there are many newly proposed methods, the scarcity of empirical work, the problem of the portability of platform independent model (PIM) and the low numbers of tools available for MDD.

Currently, a growing interest is being caused by mobile cloud applications. Improvements related to the portability of these applications among different platforms and different service providers are a critical need. Model Driven Development (MDD) constitutes one of the alternatives to address the portability problem. This work presents a systematic mapping study that analyzes different proposals that apply MDD to the development of mobile cloud applications and that, at the same time, consider the improvement of the portability of these applications. Even though we have identified just a few studies related to our subject of interest, the validation experiences that are presented in them, encourage the adoption of MDD to address the portability problem. However, further validation experiences that consider more complex cases in industrial environments will be required to justify the benefits of MDD in a substantial manner.

Computer-enabled social services like tagging or sharing are ubiquitous in current web applications that are aimed to a group of users. These services do not only add value and new functionalities to their applications but also create a network of users and services that interconnect them to a wider on-line ecosystem. Currently these social networks mainly use the vast amount of user-created content, and activity logs to apply to provide recommendations and more complex services. This paper presents the Social Core a social network engine that implements semantic-based functionalities like semantic annotations, semantic search semantic-enhanced access control and user privacy protection. The Social Core was integrated as part of the SmartCampus mobile platform, which was tested by around one hundred students, and it is currently being further developed as part of European FP7 project SmartSociety.

The digital content of users is commonly organised in local directories representing entities from the real world (e.g., people, locations, organisations, and events). Different representations can show different "versions", using different names to refer to the same real world entity (e.g., George Lombardi, Lombardi G., Dr. Lombardi). Although the data in these directories are related and can even complement each other, there are no formal links connecting them and allowing users to share and search across them. In this work we propose a Distributed Directory System, applied to A Healthcare Use Case for Rural Areas that allows peers: (i) to maintain full control over their data; and (ii) to find different versions of an entity based on any name that is used in the network to refer to it. We evaluate the approach in networks of different sizes using PlanetLab and we show promising results in terms of scalability.

Marketing strategies and relationship management of customers are increasingly important today, so investments for these aspects of the business are growing exponentially. To carry out the above, it is necessary to take a look inside the stored knowledge of any enterprise that could visualize the commercial behavior and preferences of their customers. Telecommunications companies deal with a special type of information that is related to the connections that exist between customers. Such information can be used to build a network to examine how customers are related with each other. In this paper, we build a social network based on the analysis of terabytes of Call Detail Record (CDR) data from a telecommunication company to identify and to select the most significant variables that express the link between the actors. As a next step we define the degree of customer relationships using a weighting function based on business rules. Finally, we apply the spreading activation-based technique to predict potential churners.