Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.

A Soft Computing Approach to Quality Evaluation of General Chemistry Learning in Higher Education

In contemporary societies higher education must shape individuals able to solve problems in a workable and simpler manner and, therefore, a multidisciplinary view of the problems, with insights in disciplines like psychology, mathematics or computer science becomes mandatory. Undeniably, the great challenge for teachers is to provide a comprehensive training in General Chemistry with high standards of quality, and aiming not only at the promotion of the student’s academic success, but also at the understanding of the competences/skills required to their future doings. Thus, this work will be focused on the development of an intelligent system to assess the Quality-of-General-Chemistry-Learning, based on factors related with subject, teachers and students.

Concepções dos alunos sobre a avaliação das aprendizagens na disciplina de matemática em anos terminais dos 1º e 2º ciclos do ensino básico

A presente investigação tem como objectivo o estudo das concepções dos alunos sobre a avaliação das aprendizagens na disciplina de Matemática em anos terminais dos 1º e 2° ciclos do Ensino Básico. Em particular, procurou-se estudar e comparar as concepções que alunos desses anos tinham sobre a avaliação e as práticas avaliativas dos seus professores, e compreender se existiam algumas relações de dependência entre essas concepções e a perspectiva face à Matemática e o desempenho escolar desses alunos. O enquadramento teórico está organizado em dois capítulos. O primeiro relacionado com a avaliação e o segundo referente às concepções. Este estudo segue uma metodologia de natureza interpretativa. A recolha de dados foi feita através da aplicação de um questionário a quatro turmas, uma do 4o e outra do 6° ano de escolaridade de dois agrupamentos distintos, um de Elvas e outro de Portalegre, e de entrevista semi-estruturada a dois alunos por turma. A análise de dados foi organizada em tomo de duas categorias: (i) perspectivas face à Matemática e (ii) perspectiva face à avaliação das aprendizagens. Os resultados do estudo indicam que as concepções sobre a avaliação das aprendizagens em Matemática dos alunos participantes incidem, preferencialmente, sobre sentimentos, consequências, funções e instrumentos de avaliação. Verifica-se uma tendência para a existência de relações de dependência entre a imagem negativa da Matemática escolar e a concepção de avaliação associada aos sentimentos. Alunos com classificações negativas a Matemática associa, igualmente, a avaliação a sentimentos. Já os alunos que têm uma imagem positiva da Matemática, assim com os que têm classificações mais elevadas, tendem a associar a avaliação às suas consequências. No que diz respeito às práticas avaliativas que lhes têm sido proporcionadas, os alunos do 1º e do 2°ciclos apresentam concepções quase semelhantes, reconhecendo as fichas de avaliação como os instrumentos com mais peso para o professor na atribuição de notas no final do período. Os alunos do 1º ciclo são os que mais revelam concordar que o professor está atento às suas dificuldades. Porém, quer os alunos do 1 o e do 2°ciclos não reconhecem poder combinar com o professor a forma como são avaliados. ABSTRACT; The main purpose of the present work is to study the students' beliefs on assessment learning related to the Mathematics subject in ending years of 1st and 2nd key stage of elementary school. ln particular, the research was aimed to study and compare the students’ beliefs that pupils of those particular years had on assessment and the assessment practices of their teachers and also if there were any kind of dependence relationships between those beliefs and the perspective towards Mathematics and those students' school performance. The theoretical framework is organized in two chapters. The first related with the assessment and the second regarding the beliefs. This study follows a methodology of interpretative nature. The data gathering was done through the application of a questionnaire to four classes, one of the 4th and another of the 6th year of two different school groups, one belonging to Elva’s and another one to Portalegre and also through a semi-structured interview done to two students per group. The data analysis was organized around two categories: (i) perspectives towards Mathematics and (ii) perspective towards assessment learning. The results of the study show that the beliefs of those students on assessment learning on Mathematics are preferably based on feelings, consequences, functions and assessment instruments. ln general terms, there seems to be dependence relationships between the Mathematics negative image and the assessment conception associated to feelings. Students with negative marks at Mathematics also associate assessment to feelings. Those who have a positive Mathematics image, as well as those with higher marks at the subject, seem to associate assessment to its consequences. Concerning the assessment practices that have been provided to students from 1 51 and 2nd key stage of elementary school, these same pupils show very similar beliefs, pointing the summative tests as having higher importance when the assessment term comes. The students of the 1st key stage of elementary school are those who most agree that the teacher is attentive to their difficulties. Even so, both groups of students say that they cannot negotiate with their teacher the way they are supposed to be assessed.

Triangular mesh parameterization with trimmed surfaces

Given a 2manifold triangular mesh \(M \subset {\mathbb {R}}^3\), with border, a parameterization of \(M\) is a FACE or trimmed surface \(F=\{S,L_0,\ldots, L_m\}\) -- \(F\) is a connected subset or region of a parametric surface \(S\), bounded by a set of LOOPs \(L_0,\ldots ,L_m\) such that each \(L_i \subset S\) is a closed 1manifold having no intersection with the other \(L_j\) LOOPs -- The parametric surface \(S\) is a statistical fit of the mesh \(M\) -- \(L_0\) is the outermost LOOP bounding \(F\) and \(L_i\) is the LOOP of the ith hole in \(F\) (if any) -- The problem of parameterizing triangular meshes is relevant for reverse engineering, tool path planning, feature detection, redesign, etc -- Stateofart mesh procedures parameterize a rectangular mesh \(M\) -- To improve such procedures, we report here the implementation of an algorithm which parameterizes meshes \(M\) presenting holes and concavities -- We synthesize a parametric surface \(S \subset {\mathbb {R}}^3\) which approximates a superset of the mesh \(M\) -- Then, we compute a set of LOOPs trimming \(S\), and therefore completing the FACE \(F=\ {S,L_0,\ldots ,L_m\}\) -- Our algorithm gives satisfactory results for \(M\) having low Gaussian curvature (i.e., \(M\) being quasi-developable or developable) -- This assumption is a reasonable one, since \(M\) is the product of manifold segmentation preprocessing -- Our algorithm computes: (1) a manifold learning mapping \(\phi : M \rightarrow U \subset {\mathbb {R}}^2\), (2) an inverse mapping \(S: W \subset {\mathbb {R}}^2 \rightarrow {\mathbb {R}}^3\), with \ (W\) being a rectangular grid containing and surpassing \(U\) -- To compute \(\phi\) we test IsoMap, Laplacian Eigenmaps and Hessian local linear embedding (best results with HLLE) -- For the back mapping (NURBS) \(S\) the crucial step is to find a control polyhedron \(P\), which is an extrapolation of \(M\) -- We calculate \(P\) by extrapolating radial basis functions that interpolate points inside \(\phi (M)\) -- We successfully test our implementation with several datasets presenting concavities, holes, and are extremely nondevelopable -- Ongoing work is being devoted to manifold segmentation which facilitates mesh parameterization

Tau Functions and the Limit of Block Toeplitz Determinants

International audience

Aplicación e impacto de las tics en la enseñanza de las matemáticas: una revisión sistemática

Esta investigación buscó, recolectó, analizó y sistematizó información, en español, sobre estudios empíricos delas Tics aplicadas a la enseñanza de las matemáticas. Se utilizó el método de la revisión sistemática; las unidades de estudio fueron los artículos y tesis de maestría publicados en los últimos 5 años,a los que se pudo acceder a través de repositorios digitales de la Universidad de Cuenca: EBSCO, SCOPUS y COBUEC;de los 219 estudiosencontrados, en base a los criterios de inclusión y exclusión, se seleccionaron 13 con el objetivo de integrar sistemáticamente la información, determinar el estado del arte, impacto, aportaciones y limitaciones en la enseñanza de las matemáticas. Los resultados más importantes queobtuvieron de esta investigación son:Es escasa la información en español sobre las Tics en la enseñanza de las matemáticas realizadas en estudios empíricos;en el Ecuador la mayoría de las tesis de maestría sobre esta temática son propuestas metodológicas o guías didácticas. En los estudios analizados,el software Geogebra es el más utilizado y los temas más abordados son función lineal y ecuaciones lineales y se veque todos los estudios generan aportaciones y limitaciones en los estudiantes durante las intervenciones con las Tics en el proceso de enseñanza aprendizaje. Se puede concluir que utilizar adecuadamente las Tics en la enseñanza de las matemáticas puede ayudar a mejorar aspectos motivacionales, actitudinales y académicos en los estudiantes, aunque todavía queda abierto un gran campo por estudiar sobre esta temática, tanto en el nivel básico, secundario y superior.

Guía didáctica de rúbricas de evaluación para el bloque uno de números y funciones, dirigida a docentes de matemáticas de segundo año de bachillerato

El presente trabajo titulado “Guía Didáctica de Rúbricas de Evaluación para el Bloque Uno de Números y Funciones, dirigida a docentes de matemática de Segundo Año de Bachillerato” ha sido diseñado con la intención de fortalecer el proceso de evaluación a través de la utilización de un nuevo instrumento, la rúbrica; con el objetivo de facilitarla tarea evaluadora del docente. En el primer capítulo, se da a conocer de manera general los cambios que ha sufrido la educación en los últimos años, la necesidad de conocer y relacionar las corrientes pedagógicas innovadoras como el constructivismo y la teoría del aprendizaje significativo con la evaluación del proceso educativo, la didáctica en el ámbito de la matemática, y la implementación de guías didácticas y rúbricas como instrumentos de apoyo para el docente. En el segundo capítulo se busca dar sustento a la propuesta mediante la aplicación de una encuesta, en la cual se evidenciala falta de instrumentos de evaluación que rompan el estigma de la utilización únicamente de pruebas en este proceso, además la factibilidad del uso de una guía didáctica de rúbricas para la matemática. Finalmente, en el tercer capítulo se presentan20 modelos de rúbricas holísticas y analíticas desarrolladas por cada actividad, referidas al Bloque Uno de “Números y Funciones” en base a las Destrezas con Criterio de Desempeño de acuerdo a lo que se establece en el Currículo del sistema educativo ecuatoriano.

Learning math through movement: physical education as another alternative

The purpose of this study was to determine the cognitive effects of applying physical recreational activities to two groups of pre-school students, related to mathematics to one of the groups and recreational games to the other.  A total of 27 subjects (13 girls and 14 boys) of 5 and a half and 6 and half years of age participated in the study.  The instrument used was a questionnaire including basic math concepts such as geometry, basic operations with concrete elements, and how to read the clock, based on the topics established by the Costa Rican Ministry of Public Education.  Once the instrument was developed, a plan of physical recreational activities related to math was prepared and applied to the experimental group (pre-school B) for one and a half months, while the other group played recreational games.  Data was analyzed using descriptive and inferential statistics.  Positive and significant effects were found in the physical recreational activity program regarding student performance in 10 of the 12 items that were applied to assess mastery of basic math concepts.  In conclusion, using physical education as another instrument to teach other disciplines represents an excellent alternative for pre-school teachers that try to satisfy the learning needs of children that will soon be attending school.  Using movement as part of guided and planned activities plays an indispensable role in children’s lives; therefore, learning academic subjects should be adapted to their needs to explore and know their environment.

Guidelines for Creating ICT-based Adaptable Learning Resources

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Cálculo de constantes ópticas de películas delgadas de Cu3BiS3 a través del método de Wolfe

Se calculó la obtención de las constantes ópticas usando el método de Wolfe. Dichas contantes: coeficiente de absorción (α), índice de refracción (n) y espesor de una película delgada (d ), son de importancia en el proceso de caracterización óptica del material. Se realizó una comparación del método del Wolfe con el método empleado por R. Swanepoel. Se desarrolló un modelo de programación no lineal con restricciones, de manera que fue posible estimar las constantes ópticas de películas delgadas semiconductoras, a partir únicamente, de datos de transmisión conocidos. Se presentó una solución al modelo de programación no lineal para programación cuadrática. Se demostró la confiabilidad del método propuesto, obteniendo valores de α = 10378.34 cm−1, n = 2.4595, d =989.71 nm y Eg = 1.39 Ev, a través de experimentos numéricos con datos de medidas de transmitancia espectral en películas delgadas de Cu3BiS3.

Strategies to Promote Research and Logical Reasoning in the Teaching of Mathematics in the Schools

The purpose of this paper is to raise a debate on the urgent need for teachers to generate innovative situations in the teaching-learning process, in the field of Mathematics, as a way for students to develop logical reasoning and research skills applicable to everyday situations. It includes some statistical data and possible reasons for the poor performance and dissatisfaction of students towards Mathematics. Since teachers are called to offer meaningful and functional learning experiences to students, in order to promote the pleasure of learning, teacher training should include experiences that can be put into practice by teachers in the education centers. This paper includes a work proposal for Mathematics Teaching to generate discussion, curiosity and logical reasoning in students, together with the Mathematical problem solving study.

The three-dimensional single-bin-size bin packing problem: combining metaheuristic and machine learning approaches

The Three-Dimensional Single-Bin-Size Bin Packing Problem is one of the most studied problem in the Cutting & Packing category. From a strictly mathematical point of view, it consists of packing a finite set of strongly heterogeneous “small” boxes, called items, into a finite set of identical “large” rectangles, called bins, minimizing the unused volume and requiring that the items are packed without overlapping. The great interest is mainly due to the number of real-world applications in which it arises, such as pallet and container loading, cutting objects out of a piece of material and packaging design. Depending on these real-world applications, more objective functions and more practical constraints could be needed. After a brief discussion about the real-world applications of the problem and a exhaustive literature review, the design of a two-stage algorithm to solve the aforementioned problem is presented. The algorithm must be able to provide the spatial coordinates of the placed boxes vertices and also the optimal boxes input sequence, while guaranteeing geometric, stability, fragility constraints and a reduced computational time. Due to NP-hard complexity of this type of combinatorial problems, a fusion of metaheuristic and machine learning techniques is adopted. In particular, a hybrid genetic algorithm coupled with a feedforward neural network is used. In the first stage, a rich dataset is created starting from a set of real input instances provided by an industrial company and the feedforward neural network is trained on it. After its training, given a new input instance, the hybrid genetic algorithm is able to run using the neural network output as input parameter vector, providing as output the optimal solution. The effectiveness of the proposed works is confirmed via several experimental tests.

On the effect of confounding in linear regression models: an approach based on the theory of quadratic forms

The main topic of this thesis is confounding in linear regression models. It arises when a relationship between an observed process, the covariate, and an outcome process, the response, is influenced by an unmeasured process, the confounder, associated with both. Consequently, the estimators for the regression coefficients of the measured covariates might be severely biased, less efficient and characterized by misleading interpretations. Confounding is an issue when the primary target of the work is the estimation of the regression parameters. The central point of the dissertation is the evaluation of the sampling properties of parameter estimators. This work aims to extend the spatial confounding framework to general structured settings and to understand the behaviour of confounding as a function of the data generating process structure parameters in several scenarios focusing on the joint covariate-confounder structure. In line with the spatial statistics literature, our purpose is to quantify the sampling properties of the regression coefficient estimators and, in turn, to identify the most prominent quantities depending on the generative mechanism impacting confounding. Once the sampling properties of the estimator conditionally on the covariate process are derived as ratios of dependent quadratic forms in Gaussian random variables, we provide an analytic expression of the marginal sampling properties of the estimator using Carlson’s R function. Additionally, we propose a representative quantity for the magnitude of confounding as a proxy of the bias, its first-order Laplace approximation. To conclude, we work under several frameworks considering spatial and temporal data with specific assumptions regarding the covariance and cross-covariance functions used to generate the processes involved. This study allows us to claim that the variability of the confounder-covariate interaction and of the covariate plays the most relevant role in determining the principal marker of the magnitude of confounding.

The “FRA wheel” as a teaching tool to elaborate on the identity aspects of physics and mathematics as disciplines in interdisciplinary contexts

This thesis project is framed in the research field of Physics Education and aims to contribute to the reflection on the importance of disciplinary identities in addressing interdisciplinarity through the lens of the Nature of Science (NOS). In particular, the study focuses on the module on the parabola and parabolic motion, which was designed within the EU project IDENTITIES. The project aims to design modules to innovate pre-service teacher education according to contemporary challenges, focusing on interdisciplinarity in curricular and STEM topics (especially between physics, mathematics and computer science). The modules are designed according to a model of disciplines and interdisciplinarity that the project IDENTITIES has been elaborating on two main theoretical frameworks: the Family Resemblance Approach (FRA), reconceptualized for the Nature of science (Erduran & Dagher, 2014), and the boundary crossing and boundary objects framework by Akkerman and Bakker (2011). The main aim of the thesis is to explore the impact of this interdisciplinary model in the specific case of the implementation of the parabola and parabolic motion module in a context of preservice teacher education. To reach this purpose, we have analyzed some data collected during the implementation in order to investigate, in particular, the role of the FRA as a learning tool to: a) elaborate on the concept of “discipline”, within the broader problem to define interdisciplinarity; b) compare the epistemic core of physics and mathematics; c) develop epistemic skills and interdisciplinary competences in student-teachers. The analysis of the data led us to recognize three different roles played by the FRA: FRA as epistemological activator, FRA as scaffolding for reasoning and navigating (inhabiting) the complexity, and FRA as lens to investigate the relationship between physics and mathematics in the historical case.

Variational Quantum Splines: Moving Beyond Linearity for Quantum Activation Functions

Activation functions within neural networks play a crucial role in Deep Learning since they allow to learn complex and non-trivial patterns in the data. However, the ability to approximate non-linear functions is a significant limitation when implementing neural networks in a quantum computer to solve typical machine learning tasks. The main burden lies in the unitarity constraint of quantum operators, which forbids non-linearity and poses a considerable obstacle to developing such non-linear functions in a quantum setting. Nevertheless, several attempts have been made to tackle the realization of the quantum activation function in the literature. Recently, the idea of the QSplines has been proposed to approximate a non-linear activation function by implementing the quantum version of the spline functions. Yet, QSplines suffers from various drawbacks. Firstly, the final function estimation requires a post-processing step; thus, the value of the activation function is not available directly as a quantum state. Secondly, QSplines need many error-corrected qubits and a very long quantum circuits to be executed. These constraints do not allow the adoption of the QSplines on near-term quantum devices and limit their generalization capabilities. This thesis aims to overcome these limitations by leveraging hybrid quantum-classical computation. In particular, a few different methods for Variational Quantum Splines are proposed and implemented, to pave the way for the development of complete quantum activation functions and unlock the full potential of quantum neural networks in the field of quantum machine learning.