VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

A eficácia e a equidade das parcerias público-privadas na educação: uma avaliação quase experimental de 17 países

I estimate achievement effects of education public-private partnerships (PPPs) in 17 countries on the 2009 PISA assessment. Enrollment in PPP schools is tied to student wealth and prior academic ability. PPP students outperform their public peers on half of all outcomes. After accounting for selection, the PPP performance advantage remains on one-quarter of outcomes. However, nearly all of these performance differences are accounted for by school-level peer group effects. PPP schools are outperforming their public counterparts not through any advantages in productive efficiency but through sorting of more capable students. Estimo los efectos de logro de las alianzas público-privadas (PPP) educativas en 17 países en la evaluación PISA 2009. La inscripción en las escuelas PPP está ligada a la riqueza estudiantil y la capacidad académica previa. Los estudiantes de PPP superan a sus pares públicos en la mitad de todos los resultados. Después de tomar en cuenta la selección, la ventaja de rendimiento de PPP permanece en una cuarta parte de los resultados. Sin embargo, casi todas estas diferencias de rendimiento se explican por los efectos del grupo de iguales a nivel escolar. Las escuelas PPP están superando a sus contrapartes públicas no por ninguna ventaja en la eficiencia productiva, sino por la clasificación de estudiantes más capaces.Estimo os efeitos de realização de parcerias de educação público-privadas (PPPs) em 17 países na avaliação do PISA 2009. A inscrição em escolas de PPP está vinculada à riqueza dos estudantes e à capacidade acadêmica anterior. Os alunos de PPP superam seus pares em metade de todos os resultados. Depois de levar em consideração a seleção, a vantagem de desempenho do PPP permanece em um quarto dos resultados. No entanto, quase todas essas diferenças de desempenho são explicadas pelos efeitos do grupo de pares no nível da escola. As escolas de PPP estão superando suas contrapartes públicas não por causa de qualquer vantagem em eficiência produtiva, mas por causa da classificação de alunos mais capacitados

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

The playthrough evaluation framework reliable usability evaluation for video games

This thesis presents the playthrough evaluation framework, a novel framework for the reliable usability evaluation of first-person shooter console video games. The framework includes playthrough evaluation, a structured usability evaluation method adapted from heuristic evaluation. Usability evaluation can help guide developers by pointing out design issues that cause users problems. However, usability evaluation methods suffer from the evaluator effect, where separate evaluations of the same data do not produce reliably consistent results. This can result in a number of undesirable consequences affecting issues such as: • Unreliable evaluation: Without reliable results, evaluation reports risk giving incorrect or misleading advice. • Weak methodological validation: Typically new methods (e.g., new heuristics) are validated against user tests. However, without a reliable means to describe observations, attempts to validate novel methods against user test data will also be affected by weak reliability. The playthrough evaluation framework addresses these points through a series of studies presenting the need for, and showing the development of the framework, including the following stages, 1. Explication of poor reliability in heuristic evaluation. 2. Development and validation of a reliable user test coding scheme. 3. Derivation of a novel usability evaluation method, playthrough evaluation. 4. Testing the method, quantifying results. Evaluations were conducted with 22 participants, on 3 first-person shooter action console video games, using two methodologies, heuristic evaluation and the novel playthrough evaluation developed in this thesis. Both methods proved effective, with playthrough evaluation providing more detailed analysis but requiring more time to conduct

WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City

The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%