La présente thèse est consacrée à la modélisation 2D et 3D d’environnements urbains à l’aide de représentations structurées et de grammaires de formes. Notre approche consiste à introduire une représentation sémantique de bâtiments, qui encode les contraintes architecturales attendues, et qui soit capable de traiter des exemples complexes en utilisant des grammaires très simples. En outre, nous proposons deux nouveaux algorithmes d’inférence permettant l’analyse grammaticale d’images en utilisant ces grammaires. En premier lieu, un algorithme dit de hill climbing permet d’extraire les règles de grammaire et les paramètres correspondants à partir d’une vue unique d’une façade. Ce concept combine astucieusement les contraintes grammaticales et les propriétés visuelles attendues pour les différents éléments architecturaux. Cependant, afin de pouvoir traiter de cas plus complexes et également d’incorporer de l’information 3D, une deuxième stratégie d’inférence basée sur des algorithmes évolutionnaires a été adoptée pour optimiser un fonction à deux objectifs qui introduit notamment des notions de profondeur. Le système proposé a été évalué tant qualitativement que quantitativement sur un panel de façades de référence toute munies d’annotations, démontrant ainsi sa robustesse face à des situations d’abords difficiles. Grâce à la force du contexte grammatical, des améliorations substantielles ont été démontrées par rapport aux performances des mêmes modèles couplés à des a priori uniquement locaux. Par conséquent, notre approche fournit des outils puissants permettant de faire face à la demande croissante en modélisation 3D d’environnements réels à large échelle, grâce à des représentations sémantiques compactes et structurées. Ce travail ouvre par ailleurs un vaste champ de perspectives pour le domaine de l’interprétation d’images / This thesis is devoted to 2D and 3D modeling of urban environments using structured representations and grammars. Our approach introduces a semantic representation for buildings that encodes expected architectural constraints and is able to derive complex instances using fairly simple grammars. Furthermore, we propose two novel inference algorithms to parse images using such grammars. To this end, a steepest ascent hill climbing concept is considered to derive the grammar and the corresponding parameters from a single facade view. It combines the grammar constraints with the expected visual properties of the different architectural elements. Towards addressing more complex scenarios and incorporating 3D information, a second inference strategy based on evolutionary computational algorithms is adopted to optimize a two-component objective function introducing depth cues. The proposed framework was evaluated qualitatively and quantitatively on a benchmark of annotated facades, demonstrating robustness to challenging situations. Substantial improvement due to the strong grammatical context was shown in comparison to the performance of the same appearance models coupled with local priors. Therefore, our approach provides powerful techniques in response to increasing demand on large scale 3D modeling of real environments through compact, structured and semantic representations, while opening new perspectives for image understanding

Medication prescriptions constitute an important type of clinical intervention. Medication adherence is the degree to which a patient consumes their medication as agreed upon with a prescriber. Despite many years of research, medication non-adherence continues to be a problem of note, partially due to its multi-faceted in nature. Numerous interventions have attempted to improve adherence but none have emerged as definitive. A significant sub-problem is the lack of consensus regarding definitions and measurement of adherence. Several recent reviews indicate that discrepancies in definitions, measurement techniques, and study methodologies make it impossible to draw strong conclusions via meta-analyses of the literature. Technological interventions aimed at improving adherence have been the subject of ongoing research. Due to the increasing prevalence of the Internet of Things, technology can be used to provide a continuous stream of data regarding a patient's behaviour. To date, several researchers have proposed interventions that leverage data from the Internet of Things, however none have established an acceptable means of analyzing and acting upon this wealth of data. This thesis introduces a computational definition for adherence that can be used to support continued development of technological adherence interventions. A central part of the proposed definition is a formal language for specifying prescriptions that uses fuzzy set theory to accommodate imprecise concepts commonly found in natural language medication prescriptions. A prescription specified in this language can be transformed into an evaluation function which can be used to score the adherence of a given medication taking behaviour. Additionally, the evaluator function is applied to the problem of scheduling medication administrations. A compiler for the proposed language was implemented and had its breadth of expression and clinical accuracy evaluated. The results indicate that the proposed computational definition of adherence is acceptable as a proof of concept and merits further works. / Graduate

[EN] Mathematical notation is well-known and used all over the world. Humankind has evolved from simple methods representing countings to current well-defined math notation able to account for complex problems. Furthermore, mathematical expressions constitute a universal language in scientific fields, and many information resources containing mathematics have been created during the last decades. However, in order to efficiently access all that information, scientific documents have to be digitized or produced directly in electronic formats. Although most people is able to understand and produce mathematical information, introducing math expressions into electronic devices requires learning specific notations or using editors. Automatic recognition of mathematical expressions aims at filling this gap between the knowledge of a person and the input accepted by computers. This way, printed documents containing math expressions could be automatically digitized, and handwriting could be used for direct input of math notation into electronic devices. This thesis is devoted to develop an approach for mathematical expression recognition. In this document we propose an approach for recognizing any type of mathematical expression (printed or handwritten) based on probabilistic grammars. In order to do so, we develop the formal statistical framework such that derives several probability distributions. Along the document, we deal with the definition and estimation of all these probabilistic sources of information. Finally, we define the parsing algorithm that globally computes the most probable mathematical expression for a given input according to the statistical framework. An important point in this study is to provide objective performance evaluation and report results using public data and standard metrics. We inspected the problems of automatic evaluation in this field and looked for the best solutions. We also report several experiments using public databases and we participated in several international competitions. Furthermore, we have released most of the software developed in this thesis as open source. We also explore some of the applications of mathematical expression recognition. In addition to the direct applications of transcription and digitization, we report two important proposals. First, we developed mucaptcha, a method to tell humans and computers apart by means of math handwriting input, which represents a novel application of math expression recognition. Second, we tackled the problem of layout analysis of structured documents using the statistical framework developed in this thesis, because both are two-dimensional problems that can be modeled with probabilistic grammars. The approach developed in this thesis for mathematical expression recognition has obtained good results at different levels. It has produced several scientific publications in international conferences and journals, and has been awarded in international competitions. / [ES] La notación matemática es bien conocida y se utiliza en todo el mundo. La humanidad ha evolucionado desde simples métodos para representar cuentas hasta la notación formal actual capaz de modelar problemas complejos. Además, las expresiones matemáticas constituyen un idioma universal en el mundo científico, y se han creado muchos recursos que contienen matemáticas durante las últimas décadas. Sin embargo, para acceder de forma eficiente a toda esa información, los documentos científicos han de ser digitalizados o producidos directamente en formatos electrónicos. Aunque la mayoría de personas es capaz de entender y producir información matemática, introducir expresiones matemáticas en dispositivos electrónicos requiere aprender notaciones especiales o usar editores. El reconocimiento automático de expresiones matemáticas tiene como objetivo llenar ese espacio existente entre el conocimiento de una persona y la entrada que aceptan los ordenadores. De este modo, documentos impresos que contienen fórmulas podrían digitalizarse automáticamente, y la escritura se podría utilizar para introducir directamente notación matemática en dispositivos electrónicos. Esta tesis está centrada en desarrollar un método para reconocer expresiones matemáticas. En este documento proponemos un método para reconocer cualquier tipo de fórmula (impresa o manuscrita) basado en gramáticas probabilísticas. Para ello, desarrollamos el marco estadístico formal que deriva varias distribuciones de probabilidad. A lo largo del documento, abordamos la definición y estimación de todas estas fuentes de información probabilística. Finalmente, definimos el algoritmo que, dada cierta entrada, calcula globalmente la expresión matemática más probable de acuerdo al marco estadístico. Un aspecto importante de este trabajo es proporcionar una evaluación objetiva de los resultados y presentarlos usando datos públicos y medidas estándar. Por ello, estudiamos los problemas de la evaluación automática en este campo y buscamos las mejores soluciones. Asimismo, presentamos diversos experimentos usando bases de datos públicas y hemos participado en varias competiciones internacionales. Además, hemos publicado como código abierto la mayoría del software desarrollado en esta tesis. También hemos explorado algunas de las aplicaciones del reconocimiento de expresiones matemáticas. Además de las aplicaciones directas de transcripción y digitalización, presentamos dos propuestas importantes. En primer lugar, desarrollamos mucaptcha, un método para discriminar entre humanos y ordenadores mediante la escritura de expresiones matemáticas, el cual representa una novedosa aplicación del reconocimiento de fórmulas. En segundo lugar, abordamos el problema de detectar y segmentar la estructura de documentos utilizando el marco estadístico formal desarrollado en esta tesis, dado que ambos son problemas bidimensionales que pueden modelarse con gramáticas probabilísticas. El método desarrollado en esta tesis para reconocer expresiones matemáticas ha obtenido buenos resultados a diferentes niveles. Este trabajo ha producido varias publicaciones en conferencias internacionales y revistas, y ha sido premiado en competiciones internacionales. / [CAT] La notació matemàtica és ben coneguda i s'utilitza a tot el món. La humanitat ha evolucionat des de simples mètodes per representar comptes fins a la notació formal actual capaç de modelar problemes complexos. A més, les expressions matemàtiques constitueixen un idioma universal al món científic, i s'han creat molts recursos que contenen matemàtiques durant les últimes dècades. No obstant això, per accedir de forma eficient a tota aquesta informació, els documents científics han de ser digitalitzats o produïts directament en formats electrònics. Encara que la majoria de persones és capaç d'entendre i produir informació matemàtica, introduir expressions matemàtiques en dispositius electrònics requereix aprendre notacions especials o usar editors. El reconeixement automàtic d'expressions matemàtiques té per objectiu omplir aquest espai existent entre el coneixement d'una persona i l'entrada que accepten els ordinadors. D'aquesta manera, documents impresos que contenen fórmules podrien digitalitzar-se automàticament, i l'escriptura es podria utilitzar per introduir directament notació matemàtica en dispositius electrònics. Aquesta tesi està centrada en desenvolupar un mètode per reconèixer expressions matemàtiques. En aquest document proposem un mètode per reconèixer qualsevol tipus de fórmula (impresa o manuscrita) basat en gramàtiques probabilístiques. Amb aquesta finalitat, desenvolupem el marc estadístic formal que deriva diverses distribucions de probabilitat. Al llarg del document, abordem la definició i estimació de totes aquestes fonts d'informació probabilística. Finalment, definim l'algorisme que, donada certa entrada, calcula globalment l'expressió matemàtica més probable d'acord al marc estadístic. Un aspecte important d'aquest treball és proporcionar una avaluació objectiva dels resultats i presentar-los usant dades públiques i mesures estàndard. Per això, estudiem els problemes de l'avaluació automàtica en aquest camp i busquem les millors solucions. Així mateix, presentem diversos experiments usant bases de dades públiques i hem participat en diverses competicions internacionals. A més, hem publicat com a codi obert la majoria del software desenvolupat en aquesta tesi. També hem explorat algunes de les aplicacions del reconeixement d'expressions matemàtiques. A més de les aplicacions directes de transcripció i digitalització, presentem dues propostes importants. En primer lloc, desenvolupem mucaptcha, un mètode per discriminar entre humans i ordinadors mitjançant l'escriptura d'expressions matemàtiques, el qual representa una nova aplicació del reconeixement de fórmules. En segon lloc, abordem el problema de detectar i segmentar l'estructura de documents utilitzant el marc estadístic formal desenvolupat en aquesta tesi, donat que ambdós són problemes bidimensionals que poden modelar-se amb gramàtiques probabilístiques. El mètode desenvolupat en aquesta tesi per reconèixer expressions matemàtiques ha obtingut bons resultats a diferents nivells. Aquest treball ha produït diverses publicacions en conferències internacionals i revistes, i ha sigut premiat en competicions internacionals. / Álvaro Muñoz, F. (2015). Mathematical Expression Recognition based on Probabilistic Grammars [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/51665 / TESIS

Master's thesis studies the possibilities of generating 3D trees using variety of methods including context-free grammars and L-systems. Master's thesis also includes chapter on evolutionary and genetic algorithms, which briefly summarize their function. In this project genetic algorithm which takes 2D image of tree and the beginning of its trunk is proposed. Based on this information it will generate 3D tree which is visually close to the original image. In addition to methods of generating trees, reader will get information about processing input image and designing test application.

This work introduces and discusses generalized scattered context grammars that are based upon sequences of productions whose left-hand sides are formed by nonterminal strings, not just single nonterminals. It places two restrictions on the derivations in these grammars. More specifically, let k be a constant. The first restriction requires that rewriting all symbols occurs within the first k symbols of the first continuous block of nonterminals in the sentential form during every derivation step. The other restriction defines the derivations over sentential forms containing no more than k occurrences of nonterminals. As its main result, the thesis demonstrates that both restrictions decrease the generative power of these grammars to the power of context-free grammars.

The purpose of this study is to compare Computer-Assisted Language Learning (CALL) grammar instruction with traditional classroom teacher-directed grammar instruction for post-secondary English as a Second Language (ESL) students enrolled in an Intensive English Program (IEP). Students‟ achievement was measured by their performance on three measures (multiple choice, cloze/fill-in-the-blank, and open-ended tests) of passive grammatical forms. This study gathered quantitative data on students‟ performance on the three measures for both teacher-directed and CALL instruction groups as well as qualitative data with respect to the CALL participants‟ perception of the Azar Interactive online grammar instruction program. Results of the mixed design repeated measures factorial MANOVA (multivariate analysis of variance) showed that there was no statistically significant difference in acquisition of the passive grammatical forms for ESL students taught in a conventional classroom setting as compared to those taught solely by CALL. However, there was a statistically significant increase in scores on the open-ended tests for Level 4, the most advanced students at the IEP from pretest to delayed test as well as from posttest to delayed test. Students‟ level of proficiency affected the amount of increase in their scores over time regardless of the method of instruction. This study has offered a research-based indication that CALL instruction was as effective as traditional classroom teacher-directed instruction for teaching grammar to students of different levels of English proficiency. Recommendations for future research have also been discussed

Tato disertační práce představuje derivační stromy několika různých typů gramatik ve zobecněné Kurodově normální formě; jmenovitě obecné a regulárně řízené gramatiky, gramatiky s rozptýleným kontextem a spolupracující distribuované gramatické systémy. Definuje jednoduché stromové rysy založené na kontextových vlastnostech jednotlivých diskutovaných gramatik a dokazuje, že pokud existuje limitující konstanta k taková, že každá věta generovaného jazyka L odpovídá řetězci listových uzlů derivačního stromu, ve kterém je výskyt definovaných stromových rysů omezen konstantou k, jazyk L je ve skutečnosti bezkontextový. Tato práce dále ukazuje, že dosažený výsledek představuje silný nástroj důkazu bezkontextovosti jazyka. Vše je doplněno příklady praktického využití nástroje.

Ce mémoire présente un outil informatique pour développer et tester des grammaires d’unification polarisées, conçu entre autres pour faciliter la validation expérimentale du formalisme de la grammaire d’unification Sens-Texte (GUST) de Kahane & Lareau. Les fondements théoriques du formalisme GUP sont d’abord expliqués, puis nous décrivons la conception du module et l’évaluons avec un fragment de la grammaire GUST. / This thesis presents a computer tool to develop and test polarized unification grammars that was built, namely, to help validate experimentally Kahane & Lareau’s Meaning-Text Unification Grammar (MTUG) formalism. We first describe the theory behind the PUG formalism, then we explain how the module was developed and we test it using a fragment of the MTUG grammar.

Semantic parsing is the process of mapping a natural-language sentence into a machine-readable, formal representation of its meaning. The LSTM Encoder-Decoder is a neural architecture with the ability to map a source language into a target one. We are interested in the problem of mapping natural language into SPARQL queries, and we seek to contribute with strategies that do not rely on handcrafted rules, high-quality lexicons, manually-built templates or other handmade complex structures. In this context, we present two contributions to the problem of semantic parsing departing from the LSTM encoder-decoder. While natural language has well defined vector representation methods that use a very large volume of texts, formal languages, like SPARQL queries, suffer from lack of suitable methods for vector representation. In the first contribution we improve the representation of SPARQL vectors. We start by obtaining an alignment matrix between the two vocabularies, natural language and SPARQL terms, which allows us to refine a vectorial representation of SPARQL items. With this refinement we obtained better results in the posterior training for the semantic parsing model. In the second contribution we propose a neural architecture, that we call Encoder CFG-Decoder, whose output conforms to a given context-free grammar. Unlike the traditional LSTM encoder-decoder, our model provides a grammatical guarantee for the mapping process, which is particularly important for practical cases where grammatical errors can cause critical failures. Results confirm that any output generated by our model obeys the given CFG, and we observe a translation accuracy improvement when compared with other results from the literature. / A análise semântica é o processo de mapear uma sentença em linguagem natural para uma representação formal, interpretável por máquina, do seu significado. O LSTM Encoder-Decoder é uma arquitetura de rede neural com a capacidade de mapear uma sequência de origem para uma sequência de destino. Estamos interessados no problema de mapear a linguagem natural em consultas SPARQL e procuramos contribuir com estratégias que não dependam de regras artesanais, léxico de alta qualidade, modelos construídos manualmente ou outras estruturas complexas feitas à mão. Neste contexto, apresentamos duas contribuições para o problema de análise semântica partindo da arquitetura LSTM Encoder-Decoder. Enquanto para a linguagem natural existem métodos de representação vetorial bem definidos que usam um volume muito grande de textos, as linguagens formais, como as consultas SPARQL, sofrem com a falta de métodos adequados para representação vetorial. Na primeira contribuição, melhoramos a representação dos vetores SPARQL. Começamos obtendo uma matriz de alinhamento entre os dois vocabulários, linguagem natural e termos SPARQL, o que nos permite refinar uma representação vetorial dos termos SPARQL. Com esse refinamento, obtivemos melhores resultados no treinamento posterior para o modelo de análise semântica. Na segunda contribuição, propomos uma arquitetura neural, que chamamos de Encoder CFG-Decoder, cuja saída está de acordo com uma determinada gramática livre de contexto. Ao contrário do modelo tradicional LSTM Encoder-Decoder, nosso modelo fornece uma garantia gramatical para o processo de mapeamento, o que é particularmente importante para casos práticos nos quais erros gramaticais podem causar falhas críticas em um compilador ou interpretador. Os resultados confirmam que qualquer resultado gerado pelo nosso modelo obedece à CFG dada, e observamos uma melhora na precisão da tradução quando comparada com outros resultados da literatura.

Cette thèse présente une modélisation des principaux aspects syntaxiques de la coordination dans les grammaires d'interaction de Guy Perrier. Les grammaires d'interaction permettent d'expliciter la valence des groupes conjoints. C'est précisément sur cette notion qu'est fondée notre modélisation. Nous présentons également tous les travaux autour de cette modélisation qui nous ont permis d'aboutir à une implantation réaliste: le développement du logiciel XMG et son utilisation pour l'écriture de grammaires lexicalisées, le filtrage lexical par intersection d'automates et l'analyse syntaxique. / This thesis presents a modelisation of the main syntactical aspects of coordination using Guy Perrier's Interaction Grammars as the target formalism. Interaction Grammars make it possible to explicitly define conjuncts' valencies. This is precisely what our modelisation is based upon. We also present work around this modelisation that enabled us to provide a realistic implementation: lexicalized grammar development (using our tool XMG), lexical disambiguation based on automata intersection and parsing.