Person:
Portela García-Miguel, Javier

First Name

Javier

Last Name

Portela García-Miguel

URI

https://hdl.handle.net/20.500.14352/77436

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Estudios estadísticos

Department

Estadística y Ciencia de los Datos

Area

Estadística e Investigación Operativa

Identifiers

Full item page

Search Results

Now showing 1 - 10 of 13

Differentiated models in the collaborative transport economy: a mixture analysis for Blablacar and Uber
(2021) Quirós Romero, Cipriano; Portela García-Miguel, Javier; Marín Sanz, Raquel; Dabić, Marina; Griffy-Brown, Charla
The collaborative economy has become one of the fastest-growing areas since the great recession of 2008, with passenger transport activities being of particular importance. Based on on-demand ride services, in which passengers are connected with community drivers through a smartphone app, these collaborative transport activities are not all of a homogeneous nature, and need to be differentiated according to the type of service provided to understand the socioeconomic and environmental characteristics that determine their widespread use. The objective of this paper is to analyze the determinants of use of the two main online platforms for transport in Europe: Blablacar (ride-sharing) and Uber (ride-hailing), representing two different business models for shared mobility. Our contribution is an empirical analysis emphasizing the role of digital skills and employing a mixture analysis technique that permits the distinction between these two platforms models. Our results show that beyond their strong linkage to the digital environment, the determinants of using both platforms differ. In the case of Blablacar, the results show a closed user profile influenced by age, educational level, live as a couple or urban environment. However, the user profile for Uber is open and more diffuse, with a preference for female population.
Project number: 390
Aprendizaje virtual de las matemáticas utilizando distintas TICs
(2021) Riomoros Callejo, María Isabel; Medina Sánchez, María Ángeles; Ávila Tejera, Juan Julián; Belope Nguema, Sabina; García Ruiz, Yolanda; García Villalba, Luis Javier; Portela García-Miguel, Javier; Blanco García, Susana; Miguel Vicente, Carmen; García Pineda, María Pilar; Sandoval Orozco, Ana Lucila
Proyecto para ayudar al estudiantado en el aprendizaje de las Matemáticas y promover su aprendizaje autónomo. Todo ello, con materiales más visuales como vídeos, píldoras educativas, clases en modo síncrono, ejemplos con GeoGebra y cuestionarios.
Project number: 104
Las Matemáticas Empresariales en el marco Erasmus Mundus
(2017) García Pineda, María Pilar; Heras Martínez, Antonio José; Blanco García, Susana; Balbás Aparicio, Raquel; García Villalba, Luis Javier; Riomoros Callejo, María Isabel; Portela García-Miguel, Javier; Sandoval Orozco, Ana Lucila; Rebollo Castillo, Francisco Javier
La creciente importancia de los métodos cuantitativos en las ciencias económicas y empresariales nos motiva a proponer una revisión detallada de los syllabus de las materias de matemáticas que se imparten en el Grado de Administración y Dirección de Empresas, con el objetivo de Investigar las correspondencias entre nuestros syllabus y los de las mas importantes universidades a nivel internacional (en el marco Erasmus Mundus). La investigación que proponemos llevará a cabo comparaciones exhaustivas de los temarios de esta categoría de asignaturas y sus metodologías docentes, y detectará las posibles discrepancias existentes en este tipo de estudios dependiendo de la universidad que los imparte. En una segunda fase, estudiaremos las causas de las posibles diferencias detectadas y, finalmente, produciremos un sistema capaz de sugerir medidas concretas que solventen los posibles problemas detectados.
Clustering using ordered weighted averaging operator and 2-tuple linguistic model for hotel segmentation: the case of TripAdvisor
(Expert Systems with Applications, 2023) Shu, Ziwei; Carrasco González, Ramón Alberto; Portela García-Miguel, Javier; Sanchez-Montañés, Manuel; Lin, Binshan
With the growth of online tourism, it is important to analyze the reviews left by numerous customers on social networks to improve the hotel’s online reputation. Hotel segmentation based on online reviews has attracted an increasing interest from many academics. The problem is that many hotel segmentation models overlook the fact that some customers focus on positive reviews when choosing a hotel, while others focus on negative ones. To address this shortcoming, this paper develops a novel approach to classify hotels using the ordered weighted averaging (OWA) operator, the 2-tuple linguistic model, and K-means clustering. The proposed approach has been evaluated with a real dataset from TripAdvisor, which contains more than 50 million customer online reviews on eight aspects of the hotel. The results show that the proposed model can produce denser and more separated clusters than the model without linguistic quantifiers. From a business point of view, this model enables hotels to distinguish customers’ perceptions (from the less demanding to the most demanding) about their eight aspects, allowing them to specify which of them need to be improved and develop strategies more quickly. At the same time, it introduces a new way of ranking hotels online, allowing customers to create personalized rankings of hotels based on their degree of demand for various hotel aspects (better location, cleaner rooms, etc.) rather than the average ratings, so that they can select the most suitable hotels more quickly.
Multiple scenarios of quality of life index using fuzzy linguistic quantifiers: the case of 85 countries in Numbeo
(Mathematics, 2022) Shu, Ziwei; Carrasco González, Ramón Alberto; Portela García-Miguel, Javier; Manuel Sánchez-Montañés, Manuel; Chiclana, Francisco
In economic development, in addition to comparing the gross domestic product (GDP) between nations, it is critical to assess the quality of life to gain a holistic perspective of their different aspects. However, the quality of life index (QOLI) is a subjective term that can be difficult to quantify. Although this composite index is typically calculated using universal weights proposed by experts to aggregate indicators, such as safety indexes, healthcare indexes, pollution indexes, and housing indicators, it is complicated to balance multiple dimensions whose weights are adjusted to account for different countries’ circumstances. Therefore, this paper aims to construct various scenarios of the QOLI, using linguistic quantifiers of the ordered weighted averaging (OWA) operator, and the 2-tuple linguistic model. Numbeo, one of the largest quality of life information databases, was used in this paper to estimate the QOLI in 85 countries. Uncertainty and sensitivity analyses were employed to assess the robustness of the QOLI. The results of the proposed model are compared with those obtained using the Numbeo formulation. The results show that the proposed model increases the linguistic interpretability of the QOLI, and obtains different QOLIs, based on diverse country contexts.
Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks
(Sensors, 2016) Portela García-Miguel, Javier; García Villalba, Luis Javier; Silva Trujillo, Alejandra Guadalupe; Sandoval Orozco, Ana Lucila; Kim, Tai-Hoon
Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks.
Estudio de la percepción pública de la vacuna contra la COVID-19 mediante técnicas de PLN y de aprendizaje automático
(2021) Povedano Álvarez, Daniel; Portela García-Miguel, Javier; Armas Vega, Esteban Alejandro
La pandemia de COVID-19 causada por el nuevo coronavirus SARS-CoV-2 ha tenido un impacto significativo en la sociedad, tanto por los graves efectos sanitarios y económicos como por los efectos de las medidas sanitarias para evitar su propagación. Gracias a las técnicas de PLN se ha podido investigar las actitudes hacia la vacunación, siendo particularmente oportuno en estos momentos ante la llegada de las vacunas contra la COVID-19. Este trabajo tiene una doble finalidad, por un lado estudiar la percepción hacia la vacunación contra la COVID-19, mediante técnicas de Procesamiento de Lenguaje Natural y por otro, la construcción de un clasificador de sentimientos interpretable mediante técnicas de Aprendizaje Automático. Para ello se uitlizaron 4.000.000 de tweets relacionados con la vacunación en el periodo comprendido entre el 15 de noviembre de 2020 y el 16 de diciembre de 2020 como conjunto de datos. El periodo de tiempo seleccionado es esencial porque durante este tiempo se publicaron los primeros resultados de las vacunas contra la COVID-19, como Pfizer y Moderna, surgieron un debate público. El análisis de la percepción sugiere que hay un número significativo de tweets negativos que pueden poner en peligro el objetivo de alcanzar la inmunidad de rebaño. En cuanto a los resultados del clasificador de sentimientos multiclase se obtuvo un 92% de ROC-AUC con el algoritmo LinearSVC.
Learning strategies for sensitive content detection
(Electronics, 2023) Povedano Álvarez, Daniel; Sandoval Orozco, Ana Lucila; Portela García-Miguel, Javier; García Villalba, Luis Javier; Guo, Zhenhua
Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.
A model integrating the 2‑tuple linguistic model and the CRITIC‑AHP method for hotel classification
(SN Computer Science, 2023) Shu, Ziwei; Carrasco González, Ramón Alberto; Portela García-Miguel, Javier; Sánchez‑Montañés, Manuel; Pal, Umapada; Yuen, Chau
Hotel classification is essential for hotel managers and customers. It can assist hotel managers in better understanding the needs of their customers and in improving various aspects of the hotel through relevant strategies. It also aids customers in choosing appropriate accommodations according to their preferences regarding hotel location, services, and other aspects. This paper aims to improve our previous model by incorporating expert opinions into the weight calculation, thereby increasing its practical applicability. The extended model combines the analytical hierarchy process (AHP) and the CRiteria Importance Through Intercriteria Correlation (CRITIC) methods, introducing a novel approach for calculating the weights of each aspect. The 2-tuple linguistic model is retained in the extended model to resolve the problem of information loss in linguistic information fusion. Finally, various hotel segments are obtained with the weighted K-means clustering. A dataset with over fifty million hotel reviews from TripAdvisor has been applied to evaluate the extended model. The results show that the extended model achieves denser and better separated hotel clusters than our previous model, while maintaining the same advantages. This model is more likely to help hotel managers create better strategies to tackle hotel weaknesses or gain competitive advantages, as it combines two types of weights that improve clustering results: the quantity of information in each hotel aspect and the expert judgment of each aspect's importance in hotel development.
Extracting Association Patterns in Network Communications
(Sensors, 2015) Portela García-Miguel, Javier; García Villalba, Luis Javier; Silva Trujillo, Alejandra Guadalupe; Sandoval Orozco, Ana Lucila; Kim, Tai-hoon
In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense.