Este trabalho apresenta um estudo prospectivo referente ao desenvolvimento de um medidor eletromagnético inteligente de vazão, cuja finalidade é determinar a vazão de escoamento a partir da reconstrução do perfil de velocidade utilizando técnicas tomográficas. Em conseqüência disso, o medidor de vazão será capaz de corrigir a vazão dada, através da integração do perfil de velocidade correto reconstruído por tomografia. A técnica de reconstrução tomográfica utilizada é baseada na construção de um funcional de erro, gerado a partir da diferença entre voltagens simuladas numericamente para uma condição experimental, conhecidos os parâmetros determinantes da velocidade no interior da tubulação, e voltagens aproximadas simuladas numericamente para aproximações destes parâmetros. Neste trabalho, o modelo físico do medidor eletromagnético de vazão é baseado em um número de eletrodos colocados sobre as paredes do tubo sob uma estratégia de excitação específica, sem injeção de corrente, considerando o campo magnético uniforme. A partir da expansão do funcional de erro, sobre um conjunto de funções conhecidas, uma superfície de erro é gerada. As características da patologia desta superfície requerem outros tipos de técnicas de otimização. Técnicas tradicionais de otimização não são viáveis, pois o processo de busca pára no primeiro mínimo local encontrado. Essa convergência para mínimos locais é justificada devido à presença de regiões planas e vales apresentando vários mínimos locais circundando o ponto de mínimo global (ou ponto referente aos parâmetros ótimos da velocidade). Em vista da ocorrência deste fato, técnicas baseadas em algoritmos evolucionários são testadas e apresentadas para uma série de casos demonstrando a praticidade de nossa pesquisa. / This work presents a prospective study on the development of an intelligent electromagnetic flow meter intended to determine output based on the reconstruction of velocity profile using tomographic techniques. As a result, the flow meter will be able to correct the output measure through the integration of the right velocity profile produced by tomography. The tomographic reconstruction technique utilized is based on the definition of an error functional generated from the difference between voltages simulated numerically for a experimental condition, being known the parameters which define the velocity within the pipe and approximate voltages simulated numerically for approaches of these parameters. In this work the physical model of the electromagnetic flow meter is based on a number of electrodes flush mounted on pipe walls and under a specific strategy of excitement, without electrical current input and considering the magnetic field uniform. From the expansion of the error functional over a set of known functions an error surface is generated. The characteristics of the pathology of this surface require other types of optimization techniques. Traditional optimization techniques are not viable since the search stops at the first local minimum. This convergence to local minimums is justified due to the presence of flat regions and valleys presenting several local minimums around the global minimum point (or point relative to the optimum parameters of velocity). Due to this fact techniques based on evolutionary algorithms are tested and presented for a series of cases demonstrating the usefulness of our research.

Em 2009, N. Heninger e H. Shacham apresentaram um algoritmo de reconstrução que permite recuperar a chave secreta sk do criptossistema RSA básico em tempo polinomial tendo em forma aleatória 27 % dos seus bits. Sabemos que podemos obter uma versão com erros (bits modicados) da chave secreta RSA graças aos ataques cold boot. O algoritmo apresentado por Heninger-Shacham corrige esses erros fazendo uso das relações matemáticas que existe entre as chaves pública e secreta do criptossistema RSA básico. O objetivo deste trabalho é estudar esse algoritmo para implementar e analisar seu análogo para o criptossistema RSA multi-primo. Os resultados obtidos mostram que para reconstruir a chave secreta sk do criptossistema RSA u-primos é preciso ter uma fração de bits corretos maior a 2 - 2^((u+2)/(2u+1)), mostrando assim que a segurança oferecida pelo criptossistema RSA multi-primo (u>/ 3) é maior com relação ao criptossistema RSA básico (u = 2). / In 2009, N. Heninger and H. Shacham presented an algoritm for reconstructing the secret key sk of the basic RSA cryptosystem in polynomial time With a fraction of random bits greater or equal to 0.27 of its bits. We know that secret key with errors sk can be obtained from DRAM using cold-boot attacks. The Heninger and Shacham\'s algorithm xes these errors using the redundancy of secret and public key of basic RSA cryptosystem. In this work, the topic is to study this algoritm to implement and analyze its analogous for the multi-prime RSA cryptosystem. Our obtained results show the secret key sk of multi-prime RSA cryptosystem can be Reconstructed having a fraction equal or greater than 2 - 2^((u+2)/(2u+1)) of random bits. therefore the security of multi-prime RSA cryptosystem (u >/ 3) is greater than basic RSA cryptosystem (u = 2).

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Computational learning theory is a branch of theoretical computer science that re-imagines the role of an algorithm from an agent of computation to an agent of learning. The operations of computers become those of the human mind; an important step towards illuminating the limitations of artificial intelligence. The central difference between a learning algorithm and a traditional algorithm is that the learner has access to an oracle who, in constant time, can answer queries about that to be learned. Normally an algorithm would have to discover such information on its own accord. This subtle change in how we model problem solving results in changes in the computational complexity of some classic problems; allowing us to re-examine them in a new light. Specifically two known result are examined: one positive, one negative. It is know that one can efficiently learn Deterministic Finite Automatons with queries, not so of Non-Deterministic Finite Automatons. We generalize these Automatons into Labeled Transition Systems and attempt to learn them using a stronger query.

The digital preservation of cultural heritage is an important and challenging task for the research community. Reconstructing historical objects, which do not exist anymore, in the form of digital 3D models makes it possible to visualize them and present them to the public. The reconstruction process as well as the visualization lead to a deeper understanding of the lost historical objects. But the process of the digitalreconstruction is complex and time consuming as diverse sources have to be consulted and interpreted. Therefore, in this paper the latest technology in the feld of artifcial intelligence (AI) is used to support researchers in the feld of Digital Humanities: A Transformer deep learning model based on questions answering methods is introduced to assist to digitally reconstruct historical objects in 3D. It implies a new dimension of data availability, which supports the knowledge process by making large amounts of data qualitatively accessible. [Aus: Einleitung]

La tomographie est un ensemble de techniques visant à reconstruirel’intérieur d’un objet sans toucher l’objet lui même comme dans le casd’un scanner. Les principes théoriques de la tomographie ont été énoncéspar Radon en 1917. On peut assimiler l’objet à reconstruire à une image,matrice, etc.Le problème de reconstruction tomographique consiste à estimer l’objet àpartir d’un ensemble de projections obtenues par mesures expérimentalesautour de l’objet à reconstruire. La tomographie discrète étudie le cas où lenombre de projections est limité et l’objet est défini de façon discrète. Leschamps d’applications de la tomographie discrète sont nombreux et variés.Citons par exemple les applications de type non destructif comme l’imageriemédicale. Il existe d’autres applications de la tomographie discrète, commeles problèmes d’emplois du temps.La tomographie discrète peut être considérée comme un problème d’optimisationcombinatoire car le domaine de reconstruction est discret et le nombrede projections est fini. La programmation mathématique en nombres entiersconstitue un outil pour traiter les problèmes d’optimisation combinatoire.L’objectif de cette thèse est d’étudier et d’utiliser les techniques d’optimisationcombinatoire pour résoudre les problèmes de tomographie. / The tomographic imaging problem deals with reconstructing an objectfrom a data called a projections and collected by illuminating the objectfrom many different directions. A projection means the information derivedfrom the transmitted energies, when an object is illuminated from a particularangle. The solution to the problem of how to reconstruct an object fromits projections dates to 1917 by Radon. The tomographic reconstructingis applicable in many interesting contexts such as nondestructive testing,image processing, electron microscopy, data security, industrial tomographyand material sciences.Discete tomography (DT) deals with the reconstruction of discret objectfrom limited number of projections. The projections are the sums along fewangles of the object to be reconstruct. One of the main problems in DTis the reconstruction of binary matrices from two projections. In general,the reconstruction of binary matrices from a small number of projections isundetermined and the number of solutions can be very large. Moreover, theprojections data and the prior knowledge about the object to reconstructare not sufficient to determine a unique solution. So DT is usually reducedto an optimization problem to select the best solution in a certain sense.In this thesis, we deal with the tomographic reconstruction of binaryand colored images. In particular, research objectives are to derive thecombinatorial optimization techniques in discrete tomography problems.

The global ‘refugee crisis’ is increasingly affecting Europe and especially Greece, which has been the country through which many people on the move enter Europe. Within refugee education the role of teachers is recognised as the key facilitator of education, yet there are few studies which centre on teachers. This paper aims to explore and compare how teachers re/construct their ideas about the value of education, their vision on education and their role as a teacher as a result of their experience working in refugee education. Key concepts of the study are a holistic approach to teachers’ professional development, construed as teacher identity, and refugee education. The study centralises the narratives of eight teachers of different nationalities who worked or are working in Greece, either on the mainland or on the islands. Data was collected through narrative interviews applying the biographical method. An inductive coding process led to four themes: teachers’ development, teachers’ profile, educating refugees, and vision on refugee education. Similarities and differences between the stories of teachers form the basis of comparisons about teaching refugees of different ages, female refugees, and at other locations. The findings are discussed in light of the principles and concepts of Life Course Theory and Transformative Learning Theory. The findings show that teachers’ choices and actions in teaching refugees are embedded in the contemporary context of time and place, motivated by the current global crisis. The experiences of teaching refugees impact teachers personally and shape not only individual but also collective attitudes through principles of linked lives, time, and place. Teachers describe their ongoing and cumulative teacher identity re/construction, including altering their perspectives on the narrative of refugees due to their work experiences, which aligns with key concepts and principles of (adult) learning in the Life Course Theory and Transformative Learning Theory.

My thesis discusses the ways in which Julia Alvarez's In the Time of the Butterflies (1994) and In the Name of Salomé (2000) are revolutionary texts contesting traditional, male dominated history and redirecting historical and communal foci to the lives of Dominican women. I employ Walter Benjamin's theories found in his essays "The Storyteller" (1936) and "On the Concept of History" (1940) to assist my exploration of Alvarez's questions concerning the power and effect of storytelling, and the importance of reconstructing various historical voices and images, specifically, the importance of reconstructing female voices in male dominated cultures. I discuss the female-narrated component to Dominican history which Alvarez creates in her reconstruction of the lives of these women. Alvarez confronts the challenge of breaking these women out of their marginalized status by combining fiction with history in her reconstruction of their lives. Alvarez assumes the multifaceted role of mediator, story-teller, and historian as she remembers and re-presents Dominican history through the eyes of women who lived, experienced, and affected change within the Dominican Republic. Without merely act as a reporter of historical "facts," Alvarez reconstructs the lives of these women fictionally, applying her impressions and ideas about the personalities, feelings, and thoughts of these women, and historically, utilizing first and secondhand accounts and information about the women. Ultimately, the women are presented as individuals but are also connected to a collective memory and history. As individuals with human characteristics, the women are no longer inaccessible legends. As members of a collective memory and history, the women are redeemed from the isolating effect of their patriarchal society which would have women remain silent. Due to Alvarez's reconstruction, their stories finally have the potential for further dissemination in the future with the possibility to affect other oppressed peoples. Thus, Alvarez's reconstruction of the resistance of a few women in Dominican history produces the capacity for additional resistance by Alvarez's audience to the same forces that these women were combating which continue to exist today — forces such as patriarchy, dictatorial governments, fascism, and economic disparity.