Repositorio UV :: Examinando por Autor "Riquelme, Fabián"

Examinando por Autor "Riquelme, Fabián"

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A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations
(MDPI, 2021) Vieira, Felipe; Cechinel, Cristian; Ramos, Vinicius; Riquelme, Fabián; Noel, Rene; Villarroel, Rodolfo; Cornide-Reyes, Hector; Munoz, Roberto
Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.
A multi-objective linear threshold influence spread model solved by swarm intelligence-based methods
(Elsevier, 2021) Olivares, Rodrigo; Muñoz, Francisco; Riquelme, Fabián
The influence maximization problem (IMP) is one of the most important topics in social network analysis. It consists of finding the smallest seed of users that maximizes the influence spread in a social network. The main influence spread models are the linear threshold model (LT-model) and the independent cascade model (IC-model). These models have mainly been treated by using the single-objective paradigm which covers just one perspective: maximize the influence spread starting by given seed size, or minimize the seed set to reach a given number of influenced nodes. Sometimes, this minimization problem has been called the least cost influence problem (LCI). In this work, we propose a new optimization model for both perspectives under conflict, through the LT-model, by applying a binary multi-objective approach. Swarm intelligence methods are implemented to solve our proposal on real networks. Results are promising and suggest that the new multi-objective solution proposed can be properly solved in harder instances.
Contagion Modeling and Simulation in Transport and Air Travel Networks During the COVID-19 Pandemic: A Survey
(IEEE, 2021) Riquelme, Fabián; Aguilera, Ana; Inostrosa-Psijas, Alonso
The COVID-19 pandemic has generated a huge volume of research from various disciplines, such as health sciences, social sciences, mathematical modeling, social network analysis, complex systems, decision-making processes, computer simulation, economics, among many others. One of the key problems has been to understand the diffusion processes of the virus, which quickly spread worldwide through transport networks, mainly air flights. Almost two years after start the pandemic, it is necessary to collect and synthesize the existing work on this matter. This work focuses on studies related to the COVID-19 contagion simulation through transport networks. In particular, we are specially interested in the different datasets and epidemiological models used. The search methodology consists of four exhaustive searches in Google Scholar carried out between January 2020 and January 2021. Of the 1786 findings, we chose 53 articles related to Covid-19 contagion modeling and simulation through transport networks. The results show 30 different data sources for the collection of air flights and 11 additional sources for maritime and land transport. These datasets are usually complemented with other data sources, local and international, with demographic information, economic data, and statistics of traceability of the pandemic. The findings also found 15 spread models of contagion, with the SEIR model being the most widely used, followed by mathematical-based risk models. This diversity of results validates the need for these types of compilation efforts since researchers do not have a single centralized repository to collect air flight data.
Emotion-based decision support tool for learning processes
(IEEE, 2021) Puraivan, Eduardo; León, Marcelo; Beltran, Jarnishs; Riquelme, Fabián
Student stress is a problem that hinders the teaching-learning processes, and that has increased considerably since the beginning of the Covid-19 pandemic. This article introduces a framework for the development of an emotion-based decision support tool for learning processes. As a case study, we consider undergraduate students starting their academic year virtually in the context of a pandemic. Through the application of the PANAS questionnaire and NLP techniques on free-text responses, students' emotions are automatically classified as positive and negative, as well as a level of basic emotions of the Plutchik model. The results allow to identify the most frequent sentiments in students. Also, they show concordances between both measurement instruments and a high capacity for the classification of emotions.
Extremal Coalitions for Influence Games Through Swarm Intelligence-Based Methods
(Tech Science Press, 2022) Riquelme, Fabián; Olivares, Rodrigo; Muñoz, Francisco; Molinero, Xavier; Serna, Maria
An influence game is a simple game represented over an influence graph (i.e., a labeled, weighted graph) on which the influence spread phenomenon is exerted. Influence games allow applying different properties and parameters coming from cooperative game theory to the contexts of social network analysis, decision-systems, voting systems, and collective behavior. The exact calculation of several of these properties and parameters is computationally hard, even for a small number of players. Two examples of these parameters are the length and the width of a game. The length of a game is the size of its smaller winning coalition, while the width of a game is the size of its larger losing coalition. Both parameters are relevant to know the levels of difficulty in reaching agreements in collective decision-making systems. Despite the above, new bio-inspired metaheuristic algorithms have recently been developed to solve the NP-hard influence maximization problem in an efficient and approximate way, being able to find small winning coalitions that maximize the influence spread within an influence graph. In this article, we apply some variations of this solution to find extreme winning and losing coalitions, and thus efficient approximate solutions for the length and the width of influence games. As a case study, we consider two real social networks, one formed by the 58 members of the European Union Council under nice voting rules, and the other formed by the 705 members of the European Parliament, connected by political affinity. Results are promising and show that it is feasible to generate approximate solutions for the length and width parameters of influence games, in reduced solving time.
Influence decision models: From cooperative game theory to social network analysis
(Elsevier, 2021) Molinero, Xavier; Riquelme, Fabián
Cooperative game theory considers simple games and influence games as essential classes of games. A simple game can be viewed as a model of voting systems in which a single alternative, such as a bill or an amendment, is pitted against the status quo. An influence game is a cooperative game in which a team of players (or coalition) succeeds if it is able to convince sufficiently many agents to participate in a task. Furthermore, influence decision models allow to represent discrete system dynamics as graphs whose nodes are activated according to an influence spread model. It let us to depth in the social network analysis. All these concepts are applied to a wide variety of disciplines, such as social sciences, economics, marketing, cognitive sciences, political science, biology, computer science, among others. In this survey we present different advances in these topics, joint work with M. Serna. These advances include representations of simple games, the definition of influence games, and how to characterize different problems on influence games (measures, values, properties and problems for particular cases with respect to both the spread of influence and the structure of the graph). Moreover, we also present equivalent models to the simple games, the computation of satisfaction and power in collective decision-making models, and the definition of new centrality measures used for social network analysis. In addition, several interesting computational complexity results have been found.
Key Skills to Work With Agile Frameworks in Software Engineering: Chilean Perspectives
(IEEE, 2021) Cornide-Reyes, Héctor; Riquelme, Fabián; Noel, Rene; Villarroel, Rodolfo; Cechinel, Cristian
Agile frameworks continue to provide positive evidence regarding the benefits of their use in the software products. Since these methods develop professional skills in those who practice them, their knowledge and use will acquire greater demand in areas other than software development. For this reason, it is essential to recognize the key skills for agile team building. The goal of this paper is to identify the agile professional skills that the Chilean industry considers key to conform high-performance agile teams. A survey was applied to agile community professionals in Chile to validate the results of previous work and to identify relevant information regarding learning processes, techniques, and tools for working with agile frameworks. The results allowed to establish three key skills for high-performance teams with their respective levels of achievement.
MMLA approach to analyze collaborative work in Lego Serious Play activities
(IEEE, 2021) Ponce-Sandoval, Aaron; Monsalves, Diego; Riquelme, Fabián; Cornide-Reyes, Héctor
Today, the training of professionals has the enormous challenge of covering both the development of professional skills and soft skills. In the educational field of software engineering, teachers should be supported by technology to facilitate the monitoring and development of these skills in students. The use of technology should help teachers to fulfill their role as facilitators of learning and students to become active agents of their learning. In this paper, we present Naira-Hand, as a new approach to multimodal learning analytics that facilitates the analysis of collaborative work conducted with the Lego Serious Play methodology. The multimodal data are obtained through a neural network that processes a video of the activity. These multimodal data are then processed, and different visualizations allow us to observe the collaborative work from different perspectives. Even though health restrictions have prevented us from carrying out the planned experiments, we have obtained very positive results in the detection of people interactions and in visualizations, which allow us to obtain a panoramic view of the collaborative work developed by the students.
Using centrality measures to improve the classification performance of tweets during natural disasters
(Universidad De Tarapacá, 2021) Vasquez, Rodrigo; Riquelme, Fabián; Gonzalez-Cantergiani, Pablo; Vasquez, Cristobal
Online social networks like Twitter facilitate instant communication during natural disasters. A key problem is to distinguish in real-time the most assertive and contingent tweets related to the current disaster from the whole streaming. To address this problem, machine learning allows to classify tweets according to their relevance or credibility. In this article, it is proposed to use centrality measures to improve the training data sample of active learning classifiers. As a case study, tweets collected during the massive floods in Santiago of Chile at 2016 are considered. This approach improves the consistency and pertinence of the labeling process, as well as the classifiers' performance.

Examinando por Autor "Riquelme, Fabián"

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