[pt] A presente dissertação aborda a escrita bilíngue – Português como L1 e Inglês como L2, a partir de uma perspectiva cognitiva, com vistas a buscar caracterizar, de forma integrada, o processo e o produto da escrita, e possíveis correlações entre desempenho em escrita e aspectos atencionais. Participam da pesquisa 15 professores de língua inglesa (10 mulheres e 5 homens), idade média de 43,5 anos (DP 13,25), nativos do Português brasileiro. No estudo, foram empregadas ferramentas computacionais que possibilitam o registro das ações de escrita no curso da produção textual de textos argumentativos (programa Inputlog), a análise automática de características linguísticas do texto final (Nilc-Metrix (L1) e Coh-Metrix (L2) e a verificação de padrões de conectividade no texto final, por meio de atributos de grafos (SpeechGraphs). Adotou-se também o teste ANT - Attention Network Test com o intuito de ampliar a reflexão a respeito de fatores cognitivos e possíveis influências na produção textual. Na análise do processo de escrita, foram examinados tanto padrões de pausa como operações de escrita ativa e ações de revisão (inserções e apagamentos). Na análise do produto, consideraram-se parâmetros ligados a aspectos vocabulares, semânticos, sintáticos e índices de legibilidade, e informações sobre recorrência lexical e conectividade entre palavras. No que tange ao processo, os resultados do estudo revelaram diferenças entre as duas línguas, com valores mais altos associados à escrita em Inglês, para (i) pausas no interior de palavras - possivelmente sinalizando uma demanda de ordem ortográfica - e (ii) percentual de escrita ininterrupta, indicando uma escrita com menos interrupções, com menor número de alterações/revisões. O estudo de correlação revelou que os participantes apresentam o mesmo perfil de escrita na L1 e na L2. Na análise do produto por meio do Coh-Metrix (Inglês) e Nilc-Metrix (Português), verificou-se, por meio de índice de legibilidade, que os textos apresentam complexidade moderada nas duas línguas. A despeito de diferenças em como as métricas são definidas em cada Programa, os resultados sugerem que os textos em Português apresentam graus de complexidade que se correlacionam com aspectos sintáticos (como número de palavras antes do verbo principal e índice de Flesch) e semânticos (grau de concretude). Na L2, destaca-se que a diversidade lexical permanece sendo um dos indicadores mais confiáveis de proficiência e graus de complexidade, correlacionando-se com comportamentos de pausas (antes de palavras) e revisão (normal production). Em relação ao SpeechGraphs, foram observadas diferenças significativas entre os textos na L1 e na L2 para quase todos os atributos de grafos analisados, o que é interpretado como um reflexo da forma como o programa lida com características morfológicas das duas línguas. Não foram observadas correlações entre o comportamento dos falantes na L1 e na L2. Foram ainda conduzidos estudos de correlação entre os dados do Inputlog e os das ferramentas Coh-Metrix e Nilc-Metrix e entre estas e os dados do SpeechGraphs. Nos dois estudos, observou-se uma correspondência entre parâmetros indicativos de complexidade das ferramentas utilizadas, sugerindo um caminho relevante de exploração de análise integrada processo-produto para trabalhos futuros. Em relação ao estudo de correlação entre dados do Inputlog e do ANT, destacaram-se as correlações entre acurácia e tempo de reação nas condições experimentais e os percentuais de apagamentos. Os presentes achados abrem caminho e trazem contribuições significativas para o campo da psicolinguística no âmbito da pesquisa entre L1 e L2. / [en] This dissertation addresses bilingual writing – Portuguese as L1 and English as L2 – from a cognitive perspective, aiming to characterize both the writing process and the final product in an integrated manner and explore correlations between writing performance and attentional aspects. The research involves 15 English language teachers (10 women and 5 men) with an average age of 43.5 years (SD 13.25), native speakers of Brazilian Portuguese. The study utilized computational tools to record writing actions during the production of argumentative texts (Inputlog program), automatically analyzed linguistic aspects from the text (Nilc-Metrix program for Portuguese and Coh-Metrix for English) and verify connectivity patterns in the final text using graph attributes (SpeechGraphs program). The Attention Network Test (ANT) was also adopted. In the analysis of the writing process, patterns of pauses, active writing operations, and revision actions (insertions and deletions) were examined. In the product analysis, parameters related to vocabulary, semantics, syntax, readability índices, as well as information on lexical recurrence and word connectivity, were considered. Regarding the writing process, the results of the study revealed differences between the two languages, with higher values associated with writing in English, particularly in terms of (i) pauses within words, indicating orthographic demands, and (ii) the percentage of uninterrupted writing, suggesting less interruption and fewer alterations/revisions. Correlation analysis indicated that participants exhibited a similar writing profile in both L1 and L2. In the product analysis using Coh-Metrix (English) and Nilc-Metrix (Portuguese), it was found, through readability índices, that the texts exhibited moderate complexity in both languages. Despite differences in how metrics are defined in each program, the results suggest that texts in Portuguese show a higher level of complexity when considering syntactic aspects (such as the number of words before main verbs) and semantic aspects (concreteness degree). For L2, lexical diversity remains one of the most reliable proficiency indicators, correlating with pause behavior (before words) and revision (normal production). Regarding SpeechGraphs, significant differences were observed between texts in L1 and L2 for almost all analyzed graph attributes, reflecting how the program deals with morphological characteristics of the two languages. No correlations were observed between the behavior of speakers in L1 and L2. Additionally, correlation studies were conducted between Inputlog data and Coh-Metrix and Nilc-Metrix tools, as well as between these tools and Speech Graph data. In both studies, a correspondence was observed between parameters indicative of complexity in the tools used, suggesting a relevant path for exploring integrated process-product analysis in future research. Regarding the correlation study between Inputlog and ANT data, notable correlations emerged between accuracy and reaction time in experimental conditions and percentages of deletions. These findings pave the way for significant contributions to the field of psycholinguistics in the context of research between L1 and L2.

Protein-Protein Interaktions (PPI) Netzwerke liefern ein Grundgerüst für systematische Untersuchungen der komplexen molekularen Maschinerie in der Zelle. Die Komplexität von Protein-Wechselwirkungen stellt jedoch in Bezug auf ihre Identifizierung, Validierung und Annotation eine große experimentelle und rechnerische Herausforderung dar. In dieser Arbeit analysierte ich diese Probleme und lieferte Lösungen, um die Limitierungen aktueller humanen PPI Netzwerke zu überwinden. Meine Arbeit kann in zwei Teile aufgeteilt werden: Im ersten Teil führte ich eine kritischen Vergleich von acht unabhängig konstruierten humanen PPI Netzwerke durch, um mögliche experimentellen Verzerrungen zu erkennen. Die Ergebnisse zeigten starke Tendenzen bezüglich der Selektion und Detektion von Interaktionen, die in zukünftigen Anwendungen dieser Netzwerke berücksichtigt werden sollten. Einer der wichtigsten Schlussfolgerungen dieser Studie war, dass die derzeitigen humanen Interaktions Netzwerke komplementär sind und deshalb wurde eine Datenbank mit der Bezeichnung Unified Human Interaktome (UniHI) entwickelt, die menschliche PPI Daten aus zwölf wichtigsten Quellen integriert. Im zweiten Teil dieser Forschungsarbeit benutzte ich die Daten aus der UniHI Datenbank, die genetischen Modifikatoren in einer bestimmten Krankheit, Chorea Huntington (HD) eine autosomal dominante neurodegenerative Erkrankung, zu charakterisieren. Um die Proteine zu identifizieren, die den Krankheitsverlauf modifizieren können, wurden Protein Interaktion Daten mit Genexpressionsdaten von HD-Patienten in Kombination mit einem Mehrschritt-Filterungsverfahren integriert. Mit dem neuartigen Ansatz wurde ein Nucleus caudatus-spezifische Protein-Interaktion HD (PPI)-Netzwerk vorhergesagt, das 14 potentiell dysregulierten Proteine direkt oder indirekt mit dem Huntingtin-Protein verlinkt, mit mögliche Verbindung zu Molekularen Prozessen wie z.B. Apoptose, Metabolismus, neuronale Entwicklung. / Protein interaction networks aim to provide the scaffold maps for systematic studies of the complex molecular machinery in the cell. The complexity of protein interactions poses, however, large experimental and computational challenges regarding their identification, validation and annotation. Additionally, storage and linking is demanding since new data are rapidly accumulating. In this research work, I addressed these issues and provided solutions to overcome the limitations of current human protein-protein interaction (PPI) maps. In particular, my thesis can be partitioned into two parts: In the first part, I conducted a comparative assessment of eight recently constructed human protein-protein interaction networks to identify experimental biases. Results showed strong selection and detection biases which are necessary to take into consideration in future applications of these maps. One of the important conclusions of this study was that the current human interaction networks contain complementary information; hence, a database was developed, termed as Unified Human Interactome (UniHI), integrating human PPI data from twelve major sources. Several new tools were included for querying, analyzing and visualizing human PPI networks. In the second part of this research work, UniHI dataset was applied to characterize the genetic modifiers involved in a specific disease: Chorea Huntington (HD), an autosomal dominant neurodegenerative disease. To find the modifiers, a network-based modeling approach was implemented by integrating huntingtin-specific protein interaction network with gene expression data from HD patients in multiple steps. Using this approach, a Caudate Nucleus-specific HD protein interaction (PPI) network was predicted, connecting 14 potentially dysregulated proteins directly or indirectly to the disease protein, showing a possible link to molecular processes such as pro-apoptotic pathways, cell survival, anti-apoptotic, growth, and neuronal diseases.

<div>Graph-structured data is pervasive. Modeling large-scale network-structured datasets require graph processing and management systems such as graph databases. Further, the analysis of graph-structured data often necessitates bulk downloads/uploads from/to the cloud or edge nodes. Unfortunately, experience has shown that malicious actors can compromise the confidentiality of highly-sensitive data stored in the cloud or shared nodes, even in an encrypted form. For particular use cases —multi-modal knowledge graphs, electronic health records, finance— network-structured datasets can be highly sensitive and require auditability, authentication, integrity protection, and privacy-preserving computation in a controlled and trusted environment, i.e., the traditional cloud computation is not suitable for these use cases. Similarly, many modern applications utilize a "shared, replicated database" approach to provide accountability and traceability. Those applications often suffer from significant privacy issues because every node in the network can access a copy of relevant contract code and data to guarantee the integrity of transactions and reach consensus, even in the presence of malicious actors.</div><div><br></div><div>This dissertation proposes breaking from the traditional cloud computation model, and instead ship certified pre-approved trusted code closer to the data to protect graph-structured data confidentiality. Further, our technique runs in a controlled environment in a trusted data owner node and provides proof of correct code execution. This computation can be audited in the future and provides the building block to automate a variety of real use cases that require preserving data ownership. This project utilizes trusted execution environments (TEEs) but does not rely solely on TEE's architecture to provide privacy for data and code. We thoughtfully examine the drawbacks of using trusted execution environments in cloud environments. Similarly, we analyze the privacy challenges exposed by the use of blockchain technologies to provide accountability and traceability.</div><div><br></div><div>First, we propose AGAPECert, an Auditable, Generalized, Automated, Privacy-Enabling, Certification framework capable of performing auditable computation on private graph-structured data and reporting real-time aggregate certification status without disclosing underlying private graph-structured data. AGAPECert utilizes a novel mix of trusted execution environments, blockchain technologies, and a real-time graph-based API standard to provide automated, oblivious, and auditable certification. This dissertation includes the invention of two core concepts that provide accountability, data provenance, and automation for the certification process: Oblivious Smart Contracts and Private Automated Certifications. Second, we contribute an auditable and integrity-preserving graph processing model called AuditGraph.io. AuditGraph.io utilizes a unique block-based layout and a multi-modal knowledge graph, potentially improving access locality, encryption, and integrity of highly-sensitive graph-structured data. Third, we contribute a unique data store and compute engine that facilitates the analysis and presentation of graph-structured data, i.e., TruenoDB. TruenoDB offers better throughput than the state-of-the-art. Finally, this dissertation proposes integrity-preserving streaming frameworks at the edge of the network with a personalized graph-based object lookup.</div>

O presente texto descreve a tese de doutorado intitulada Análise de Formas usando Wavelets em Grafos. O tema está relacionado à área de Visão Computacional, particularmente aos tópicos de Caracterização, Descrição e Classificação de Formas. Dentre os métodos da extensa literatura em Análise de Formas 2D, percebe-se uma presença menor daqueles baseados em grafos com topologia arbitrária e irregular. As contribuições desta tese procuram preencher esta lacuna. É proposta uma metodologia baseada no seguinte pipeline : (i) Amostragem da forma, (ii) Estruturação das amostras em grafos, (iii) Função-base definida nos vértices, (iv) Análise multiescala de grafos por meio da Transformada Wavelet Espectral em grafos, (v) Extração de Características da Transformada Wavelet e (vi) Discriminação. Para cada uma das etapas (i), (ii), (iii), (v) e (vi), são inúmeras as abordagens possíveis. Um dos desafios é encontrar uma combinação de abordagens, dentre as muitas alternativas, que resulte em um pipeline eficaz para nossos propósitos. Em particular, para a etapa (iii), dado um grafo que representa uma forma, o desafio é identificar uma característica associada às amostras que possa ser definida sobre os vértices do grafo. Esta característica deve capturar a influência subjacente da estrutura combinatória de toda a rede sobre cada vértice, em diversas escalas. A Transformada Wavelet Espectral sobre os Grafos revelará esta influência subjacente em cada vértice. São apresentados resultados obtidos de experimentos usando formas 2D de benchmarks conhecidos na literatura, bem como de experimentos de aplicações em astronomia para análise de formas de galáxias do Sloan Digital Sky Survey não-rotuladas e rotuladas pelo projeto Galaxy Zoo 2 , demonstrando o sucesso da técnica proposta, comparada a abordagens clássicas como Transformada de Fourier e Transformada Wavelet Contínua 2D. / This document describes the PhD thesis entitled Shape Analysis by using Wavelets on Graphs. The addressed theme is related to Computer Vision, particularly to the Characterization, Description and Classication topics. Amongst the methods presented in an extensive literature on Shape Analysis 2D, it is perceived a smaller presence of graph-based methods with arbitrary and irregular topologies. The contributions of this thesis aim at fullling this gap. A methodology based on the following pipeline is proposed: (i) Shape sampling, (ii) Samples structuring in graphs, (iii) Function dened on vertices, (iv) Multiscale analysis of graphs through the Spectral Wavelet Transform, (v) Features extraction from the Wavelet Transforms and (vi) Classication. For the stages (i), (ii), (iii), (v) and (vi), there are numerous possible approaches. One great challenge is to nd a proper combination of approaches from the several available alternatives, which may be able to yield an eective pipeline for our purposes. In particular, for the stage (iii), given a graph representing a shape, the challenge is to identify a feature, which may be dened over the graph vertices. This feature should capture the underlying inuence from the combinatorial structure of the entire network over each vertex, in multiple scales. The Spectral Graph Wavelet Transform will reveal such an underpining inuence over each vertex. Yielded results from experiments on 2D benchmarks shapes widely known in literature, as well as results from astronomy applications to the analysis of unlabeled galaxies shapes from the Sloan Digital Sky Survey and labeled galaxies shapes by the Galaxy Zoo 2 Project are presented, demonstrating the achievements of the proposed technique, in comparison to classic approaches such as the 2D Fourier Transform and the 2D Continuous Wavelet Transform.

O presente texto descreve a tese de doutorado intitulada Análise de Formas usando Wavelets em Grafos. O tema está relacionado à área de Visão Computacional, particularmente aos tópicos de Caracterização, Descrição e Classificação de Formas. Dentre os métodos da extensa literatura em Análise de Formas 2D, percebe-se uma presença menor daqueles baseados em grafos com topologia arbitrária e irregular. As contribuições desta tese procuram preencher esta lacuna. É proposta uma metodologia baseada no seguinte pipeline : (i) Amostragem da forma, (ii) Estruturação das amostras em grafos, (iii) Função-base definida nos vértices, (iv) Análise multiescala de grafos por meio da Transformada Wavelet Espectral em grafos, (v) Extração de Características da Transformada Wavelet e (vi) Discriminação. Para cada uma das etapas (i), (ii), (iii), (v) e (vi), são inúmeras as abordagens possíveis. Um dos desafios é encontrar uma combinação de abordagens, dentre as muitas alternativas, que resulte em um pipeline eficaz para nossos propósitos. Em particular, para a etapa (iii), dado um grafo que representa uma forma, o desafio é identificar uma característica associada às amostras que possa ser definida sobre os vértices do grafo. Esta característica deve capturar a influência subjacente da estrutura combinatória de toda a rede sobre cada vértice, em diversas escalas. A Transformada Wavelet Espectral sobre os Grafos revelará esta influência subjacente em cada vértice. São apresentados resultados obtidos de experimentos usando formas 2D de benchmarks conhecidos na literatura, bem como de experimentos de aplicações em astronomia para análise de formas de galáxias do Sloan Digital Sky Survey não-rotuladas e rotuladas pelo projeto Galaxy Zoo 2 , demonstrando o sucesso da técnica proposta, comparada a abordagens clássicas como Transformada de Fourier e Transformada Wavelet Contínua 2D. / This document describes the PhD thesis entitled Shape Analysis by using Wavelets on Graphs. The addressed theme is related to Computer Vision, particularly to the Characterization, Description and Classication topics. Amongst the methods presented in an extensive literature on Shape Analysis 2D, it is perceived a smaller presence of graph-based methods with arbitrary and irregular topologies. The contributions of this thesis aim at fullling this gap. A methodology based on the following pipeline is proposed: (i) Shape sampling, (ii) Samples structuring in graphs, (iii) Function dened on vertices, (iv) Multiscale analysis of graphs through the Spectral Wavelet Transform, (v) Features extraction from the Wavelet Transforms and (vi) Classication. For the stages (i), (ii), (iii), (v) and (vi), there are numerous possible approaches. One great challenge is to nd a proper combination of approaches from the several available alternatives, which may be able to yield an eective pipeline for our purposes. In particular, for the stage (iii), given a graph representing a shape, the challenge is to identify a feature, which may be dened over the graph vertices. This feature should capture the underlying inuence from the combinatorial structure of the entire network over each vertex, in multiple scales. The Spectral Graph Wavelet Transform will reveal such an underpining inuence over each vertex. Yielded results from experiments on 2D benchmarks shapes widely known in literature, as well as results from astronomy applications to the analysis of unlabeled galaxies shapes from the Sloan Digital Sky Survey and labeled galaxies shapes by the Galaxy Zoo 2 Project are presented, demonstrating the achievements of the proposed technique, in comparison to classic approaches such as the 2D Fourier Transform and the 2D Continuous Wavelet Transform.