https://hdl.handle.net/10481/55261 2026-04-11T19:04:39Z 2026-04-11T19:04:39Z Robles Carrillo, Margarita https://hdl.handle.net/10481/94868 2024-09-23T09:08:30Z

Digital identity: an approach to its nature, concept, and functionalities Robles Carrillo, Margarita Digital identity is a basic component of the knowledge economy and society. It is the key for access- ing the digital world and for carrying out commercial, economic, or any kind of transactions and communications. Far from being a merely digital version of the physical identity, digital identity is a singular and complex construct which poses three main dilemmas that provide the framework for its analysis. The first arises from the context in which it is located, the digital ecosystem, that changes its scope and nature. The second, conceptual, is a consequence of the lack of agreement about its definition but also of the different legal framework derived from it. A third dilemma, func- tional, is due to the fact that digital identity can fulfil different, even contradictory, functionalities. An analysis of these dilemmas can contribute to a better understanding of this category leading to a proposal for its definition and legal framework.

Robles Carrillo, Margarita https://hdl.handle.net/10481/70797 2021-10-13T06:34:31Z

European Union policy on 5G: Context, scope and limits Robles Carrillo, Margarita 5G is considered a key technology for society but its implementation is currently surrounded by controversy. Beyond its technical aspects, 5G has become a question of security and national interest for many States as well as an international policy issue. Technological autonomy and digital sovereignty are increasingly recognised as strategic priorities on a global scale. In this context, the EU’s position is unique, basically for two reasons. On the one hand, the EU has unintentionally become part of the playing field in the US-China dispute over technology companies and 5G. On the other hand, any policy of the EU or its Member States is constrained by the nature of 5G as an area of either European or national competence. The delimitation of their competences is not clear, just as there is no transparent and understandable distinction of their 5G responsibilities. In order to clarify this situation, a comprehensive analysis of the European competence and legal frameworks is necessary. After that, the study of the evolution process of this European policy provides an overview of its scope and limits. Finally, the paper explains the procedures and instruments of this European policy and concludes by assessing its implementation and development prospects. The possibility of reaching technological autonomy and digital sovereignty for the EU and its member states depends, for the time being, on this European policy. Work partially supported by Spanish Government through Project PID 2020-114495RB-I00 and by Network Engineering & Security Group (NESG) . Funding for Open Access Charge: Universidad de Granada/CBUA

Saccenti, Edoardo Camacho Páez, José https://hdl.handle.net/10481/55302 2021-06-15T13:25:57Z

On the use of the observation-wise k-fold operation in PCA cross-validation Saccenti, Edoardo; Camacho Páez, José Cross-validation (CV) is a common approach for determining the optimal number of components in a principal component analysis model. To guarantee the independence between model testing and calibration, the observation-wise k-fold operation is commonly implemented in each cross-validation step. This operation renders the CV algorithm computationally intensive and it is the main limitation to apply CV on very large data sets. In this paper we carry out an empirical and theoretical investigation of the use of this operation in the element wise k-fold (ekf ) algorithm, the state-of-the-art CV algorithm. We show that when very large data sets need to be cross-validated and the computational time is a matter of concern, the observation-wise k-fold operation can be skipped. The theoretical properties of the resulting modi ed algorithm, referred to as column wise k-fold (ckf ) algorithm, are derived. Also, its performance is evaluated with several arti cial and real data sets. We suggest the ckf algorithm to be a valid alternative to the standard ekf to reduce the computational time needed to cross-validate a data set.

Saccenti, Edoardo Smilde, Age K. Camacho Páez, José https://hdl.handle.net/10481/55294 2021-06-15T13:25:59Z

Group‑wise ANOVA simultaneous component analysis for designed omics experiments Saccenti, Edoardo; Smilde, Age K.; Camacho Páez, José Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation

Fuentes García, Noemí Marta Macía Fernández, Gabriel Camacho Páez, José https://hdl.handle.net/10481/55293 2021-06-15T13:25:59Z

Evaluation of Diagnosis Methods in PCA-based Multivariate Statistical Process Control Fuentes García, Noemí Marta; Macía Fernández, Gabriel; Camacho Páez, José Multivariate Statistical Process Control (MSPC) based on Principal Component Analysis (PCA) is a well-known methodology in chemometrics that is aimed at testing whether an industrial process is under Normal Operation Conditions (NOC). As a part of the methodology, once an anomalous behaviour is detected, the root causes need to be diagnosed to troubleshoot the problem and/or avoid it in the future. While there have been a number of developments in diagnosis in the past decades, no sound method for comparing existing approaches has been proposed. In this paper, we propose such a procedure and use it to compare several diagnosis methods using randomly simulated data and from realistic data sources. This is a general comparative approach that takes into account factors that have not previously been considered in the literature. The results show that univariate diagnosis is more reliable than its multivariate counterpart.