There is an increasing interest in using deep neural networks in various downstream natural language processing tasks. Such models are commonly used as black boxes, meaning that their decision-making is difficult to interpret. In order to build trust in models, it is crucial to analyse their inner workings which lead to predictions. The need to interpret natural language processing models has induced research on linguistically-informed interpretability. This field revolves around choosing specific linguistic phenomena and inspecting models' capability to capture them without being explicitly trained for it.  This thesis project contributes to the field by assessing the ability of BERT-style models to learn subject number in Lithuanian and English. The experiments revolve around designing diagnostic classifiers which are used to determine if the models are capable of learning this particular linguistic phenomenon. The results show that BERT-style models are capable of implicitly learning the number of a subject both in Lithuanian and English. However, this seems to be harder in Lithuanian, as diagnostic classifiers show a lower accuracy. The study observes that the accuracy of logistic regression diagnostic classifiers fluctuates to a large extent. Fully connected neural network classifiers outperform logistic regression classifiers.

Insolvenzprognosen und Ratings sind wichtige Aufgaben der Finanzbranche und dienen der Kreditwürdigkeitsprüfung von Unternehmen. Eine Möglichkeit dieses Aufgabenfeld anzugehen, ist maschinelles Lernen. Dabei werden Vorhersagemodelle aufgrund von Beispieldaten aufgestellt. Methoden aus diesem Bereich sind aufgrund Ihrer Automatisierbarkeit vorteilhaft. Dies macht menschliche Expertise in den meisten Fällen überflüssig und bietet dadurch einen höheren Grad an Objektivität. Allerdings sind auch diese Ansätze nicht perfekt und können deshalb menschliche Expertise nicht gänzlich ersetzen. Sie bieten sich aber als Entscheidungshilfen an und können als solche von Experten genutzt werden, weshalb interpretierbare Modelle wünschenswert sind. Leider bieten nur wenige Lernalgorithmen interpretierbare Modelle. Darüber hinaus sind einige Aufgaben wie z.B. Rating häufig Mehrklassenprobleme. Mehrklassenklassifikationen werden häufig durch Meta-Algorithmen erreicht, welche mehrere binäre Algorithmen trainieren. Die meisten der üblicherweise verwendeten Meta-Algorithmen eliminieren jedoch eine gegebenenfalls vorhandene Interpretierbarkeit. In dieser Dissertation untersuchen wir die Vorhersagegenauigkeit von interpretierbaren Modellen im Vergleich zu nicht interpretierbaren Modellen für Insolvenzprognosen und Ratings. Wir verwenden disjunktive Normalformen und Entscheidungsbäume mit Schwellwerten von Finanzkennzahlen als interpretierbare Modelle. Als nicht interpretierbare Modelle werden Random Forests, künstliche Neuronale Netze und Support Vector Machines verwendet. Darüber hinaus haben wir einen eigenen Lernalgorithmus Thresholder entwickelt, welcher disjunktive Normalformen und interpretierbare Mehrklassenmodelle generiert. Für die Aufgabe der Insolvenzprognose zeigen wir, dass interpretierbare Modelle den nicht interpretierbaren Modellen nicht unterlegen sind. Dazu wird in einer ersten Fallstudie eine in der Praxis verwendete Datenbank mit Jahresabschlüssen von 5152 Unternehmen verwendet, um die Vorhersagegenauigkeit aller oben genannter Modelle zu messen. In einer zweiten Fallstudie zur Vorhersage von Ratings demonstrieren wir, dass interpretierbare Modelle den nicht interpretierbaren Modellen sogar überlegen sind. Die Vorhersagegenauigkeit aller Modelle wird anhand von drei in der Praxis verwendeten Datensätzen bestimmt, welche jeweils drei Ratingklassen aufweisen. In den Fallstudien vergleichen wir verschiedene interpretierbare Ansätze bezüglich deren Modellgrößen und der Form der Interpretierbarkeit. Wir präsentieren exemplarische Modelle, welche auf den entsprechenden Datensätzen basieren und bieten dafür Interpretationsansätze an. Unsere Ergebnisse zeigen, dass interpretierbare, schwellwertbasierte Modelle den Klassifikationsproblemen in der Finanzbranche angemessen sind. In diesem Bereich sind sie komplexeren Modellen, wie z.B. den Support Vector Machines, nicht unterlegen. Unser Algorithmus Thresholder erzeugt die kleinsten Modelle während seine Vorhersagegenauigkeit vergleichbar mit den anderen interpretierbaren Modellen bleibt. In unserer Fallstudie zu Rating liefern die interpretierbaren Modelle deutlich bessere Ergebnisse als bei der zur Insolvenzprognose (s. o.). Eine mögliche Erklärung dieser Ergebnisse bietet die Tatsache, dass Ratings im Gegensatz zu Insolvenzen menschengemacht sind. Das bedeutet, dass Ratings auf Entscheidungen von Menschen beruhen, welche in interpretierbaren Regeln, z.B. logischen Verknüpfungen von Schwellwerten, denken. Daher gehen wir davon aus, dass interpretierbare Modelle zu den Problemstellungen passen und diese interpretierbaren Regeln erkennen und abbilden.

L'objectif de cette thèse est d'améliorer la recherche d'objets visuels à l'aide de l'interactivité avec l'utilisateur. Notre solution est de construire un système intéractif permettant aux utilisateurs de définir leurs propres concepts visuels à partir de certains mots-clés visuels. Ces mots-clés visuels, qui en théorie représentent les mots visuels les plus informatifs liés à une catégorie d'objets, sont appris auparavant à l'aide d'un algorithme d'apprentissage supervisé et d'une manière discriminative. Le challenge est de construire des mots-clés visuels concis et interprétables. Notre contribution repose sur deux points. D'abord, contrairement aux approches existantes qui utilisent les sacs de mots, nous proposons d'employer les descripteurs locaux sans aucune quantification préalable. Deuxièmement, nous proposons d'ajouter une contrainte de régularisation à la fonction de perte de notre classifieur pour favoriser la parcimonie des modèles produits. La parcimonie est en effet préférable pour sa concision (nombre de mots visuels réduits) ainsi pour sa diminution du temps de prédiction. Afin d'atteindre ces objectifs, nous avons développé une méthode d'apprentissage à instances multiples utilisant une version modifiée de l'algorithme BLasso. Cet algorithme est une forme de boosting qui se comporte similairement au LASSO (Least Absolute Shrinkage and Selection Operator). Il régularise efficacement la fonction de perte avec une contrainte additive de type L1 et ceci en alternant entre des itérations en avant et en arrière. La méthode proposée est générique dans le sens où elle pourrait être utilisée avec divers descripteurs locaux voire un ensemble structuré de descripteurs locaux qui décrit une région locale de l'image. / This thesis is an attempt to improve visual object retrieval by allowing users to interact with the system. Our solution lies in constructing an interactive system that allows users to define their own visual concept from a concise set of visual patches given as input. These patches, which represent the most informative clues of a given visual category, are trained beforehand with a supervised learning algorithm in a discriminative manner. Then, and in order to specialize their models, users have the possibility to send their feedback on the model itself by choosing and weighting the patches they are confident of. The real challenge consists in how to generate concise and visually interpretable models. Our contribution relies on two points. First, in contrast to the state-of-the-art approaches that use bag-of-words, we propose embedding local visual features without any quantization, which means that each component of the high-dimensional feature vectors used to describe an image is associated to a unique and precisely localized image patch. Second, we suggest using regularization constraints in the loss function of our classifier to favor sparsity in the models produced. Sparsity is indeed preferable for concision (a reduced number of patches in the model) as well as for decreasing prediction time. To meet these objectives, we developed a multiple-instance learning scheme using a modified version of the BLasso algorithm. BLasso is a boosting-like procedure that behaves in the same way as Lasso (Least Absolute Shrinkage and Selection Operator). It efficiently regularizes the loss function with an additive L1-constraint by alternating between forward and backward steps at each iteration. The method we propose here is generic in the sense that it can be used with any local features or feature sets representing the content of an image region. / تعالج هذه الأطروحة مسألة البحث عن الأشياء في الصور الثابتة و هي محاولة لتحسين نتائج البحث المنتظرة عن طريق تفاعل المستخدم مع النظام . يتمثل الحل المقترح في تصميم نظام تفاعلي يتيح للمستخدم صياغة مفهومه المرئي عن طريق مجموعة مقتضبة من أجزاء صغيرة للصور هي عبارة عن كلمات مفاتيح قد تم تعلمها سابقا عن طريق تعلم آلي استنتاجي . يمكن للمستخدم حينئذ تخصيص أنموذجه أولا بالاختيار ثم بترجيح الأجزاء التي يراها مناسبة . يتمثل التحدي القائم في كيفية توليد نماذج مرئية مفهومة و مقتضبة . نكون قد ساهمنا في هذا المجال بنقطتين أساسيتين تتمثل الأولى في إدماج الواصفات المحلية للصور دون أي تكميم ، و بذلك يكون كل مكون من ناقلات الميزات ذات الأبعاد العالية مرتبط حصريا بمكان وحيد و محدد في الصورة . ثانيا ، نقترح إضافة قيود تسوية لدالة الخسارة من أجل التحصل على حلول متفرقة و مقتضبة . يساهم ذلك في تقلص عدد هذه الأجزاء المرئية و بالتالي في ربح إضافي لوقت التكهن . في إطار تحقيق الأهداف المرسومة ، قمنا بإعداد مشروع تعلم قائم على تعدد الأمثلة يرتكز أساسا على نسخة محورة لخوارزمية بلاسو . تجدر الإشارة في الأخير أنه يمكن توظيف هذا العمل باستخدام نوع أو عدة أنواع من الواصفات المحلية للصور.

A Escala Facial de Dor - Revisada (EFD-R) é uma das escalas mais recomendadas na mensuração da intensidade da dor aguda em crianças. A versão original desta escala foi testada em crianças canadenses. O objetivo deste trabalho foi avaliar a validade, interpretação e preferência da versão brasileira da Escala Facial de Dor - Revisada (EFD-R-B), em duas amostras de crianças brasileiras: uma envolvendo dor aguda procedural e outra dor aguda pós-cirúrgica. Na primeira amostra participaram 77 crianças com idades entre 6 e 12 anos, do sexo feminino e masculino, que foram submetidas à coleta de sangue (dor procedural). As crianças estimaram a intensidade da sua dor, antes e após a punção venosa, na EFD-R-B. Na estimação após a punção venosa, a Escala Colorida Analógica (ECA) foi administrada junto com a EFD-R-B e, além disso, as crianças indicaram as faces que expressavam uma dor leve, moderada e severa, a escala que preferiam e o porquê. Na segunda amostra, participaram 53 crianças com idades entre 6 e 12 anos, do sexo feminino e masculino, que tinham sido submetidas a pequenas cirurgias (dor pós-cirúrgica). Nesta amostra, as crianças estimaram, na EFD-R-B e na ECA, a intensidade da dor que estavam sentindo no momento da entrevista. Também indicaram as faces que expressavam uma dor leve, moderada e severa, o limiar de tratamento da dor, a escala que preferiam e o porquê. Na comparação entre as pontuações obtidas na EFD-R-B e na ECA (validade convergente), nas duas amostras, os valores dos coeficientes Kendall\'s tau foram altos e significativos: =0,75 para o grupo de dor procedural e =0,79 para o grupo de dor pós-cirúrgica (p=0,00 nas duas amostras). No grupo de dor procedural, a EFD-R-B refletiu as mudanças na intensidade da dor vivenciada pelas crianças antes e após a punção venosa (validade concorrente): Teste de Wilcoxon z=-6,65; p=0,00. Considerando uma escala de 0 a 10 para a EFD-R-B, a mediana e a amplitude interquartil (AIQ) para as faces indicadas como expressivas de intensidade leve, moderada e severa, foram 2 (2-2), 4 (4-6) e 10 (10-10) respectivamente, no grupo de dor procedural, e 2 (2-2), 6 (4-8) e 10 (10-10) respectivamente, no grupo de dor pós-cirúrgica. Na estimação do limiar de tratamento da dor (grupo de dor pós-cirúrgica), a mediana (AIQ) foi 6 (4-10). No grupo de dor procedural, a EFD-R-B foi a escala preferida por 57,1% das crianças e a ECA por 41,6%; no grupo de dor pós-cirúrgica, a EFD-R-B foi escolhida por 66% das crianças e a ECA por 34%. Estas proporções somente foram significativas no grupo de dor pós-cirúrgica (X²=5,453 p=0,02). Nossos resultados mostram que a EFD-R-B possui propriedades similares à escala original e boa aceitação entre as crianças entrevistadas. A determinação dos valores das diferentes intensidades de dor e do limiar de tratamento da dor, para cada participante, representa uma evidência importante sobre a interpretação da EFD-R. / The Faces Pain Scale Revised (FPS-R) is one of the most recommended tools in measuring the intensity of acute pain in children. The aim of this study was to assess validity, interpretability and preference of the Brazilian version of the FPS-R (FPS-R-B), in two different clinical samples. The first sample contained seventy-seven children, 6 to 12 years old and both sexes, undergoing venipuncture for blood sample (procedural pain). These children estimated their perceived pain intensity in FPS-R-B before and after venipuncture. Furthermore, after venipuncture, children were asked: a) to evaluate the intensity of their needle pain using the Coloured Analogue Scale (CAS), b) to indicate on the Faces scale the intensities representing the mild, moderate and severe pain, and c) to choose the scale they preferred and indicate the reasons for the preference. The second sample included fifty-three children, 6 to 12 years old and both sexes, undergoing minor surgery (postoperative pain). Following surgery, children were asked: a) to provide a rating of their current pain intensity using the FPS-R-B and the CAS, b) to indicate on the Faces scale the intensities representing the mild, moderate and severe pain, c) to estimate, on the FPS-R-B, the intensity of pain that their felt to warrant pharmacologic intervention (pain treatment threshold), and d) to choose the scale they preferred and indicate the reasons for the preference. The degree of concordance between FPS-R-B and CAS ratings (convergent validity), for both samples, was high and statistically significant Kendall\'s tau value was 0.75 for the first sample, and 0.79 for the second sample, (p<0.05) . FPS-R-B reflected the changes in pain intensity before and after venipuncture (concurrent validity): Wilcoxon Test z=- 6.24; p< 0.05. On the 0-10 scale for the FPS-R-B, the median and interquartile range (IQR) of the intensities that represented mild, moderate and severe pain were 2 (2-2), 4 (4-6) e 10 (10-10) respectively, for the first sample, and 2 (2-2), 6 (4-8) e 10 (10-10) respectively, for the second sample. The median and IQR for pain treatment threshold were 6 (4-10). Fifty-seven percent of children in the first sample and 64.8% in the second sample preferred the FPS-R-B. These proportions were statistically significant for the second sample (X²=5,453 p<0,05). Our data show that the FPS-R-B has similar statistical properties to the original. New evidences were presented regarding interpretability of the FPS-R by determining each children\'s treatment threshold and estimate of mild, moderate and severe pain. In this study, the FPS-R-B was preferred by the majority of children.

Esta dissertação apresenta um sistema de indução de classificadores fuzzy. Ao invés de utilizar a abordagem tradicional de sistemas fuzzy baseados em regras, foi utilizado o modelo de Árvore de Padrões Fuzzy(APF), que é um modelo hierárquico, com uma estrutura baseada em árvores que possuem como nós internos operadores lógicos fuzzy e as folhas são compostas pela associação de termos fuzzy com os atributos de entrada. O classificador foi obtido sintetizando uma árvore para cada classe, esta árvore será uma descrição lógica da classe o que permite analisar e interpretar como é feita a classificação. O método de aprendizado originalmente concebido para a APF foi substituído pela Programação Genética Cartesiana com o intuito de explorar melhor o espaço de busca. O classificador APF foi comparado com as Máquinas de Vetores de Suporte, K-Vizinhos mais próximos, florestas aleatórias e outros métodos Fuzzy-Genéticos em diversas bases de dados do UCI Machine Learning Repository e observou-se que o classificador APF apresenta resultados competitivos. Ele também foi comparado com o método de aprendizado original e obteve resultados comparáveis com árvores mais compactas e com um menor número de avaliações. / This work presents a system for induction of fuzzy classifiers. Instead of the traditional fuzzy based rules, it was used a model called Fuzzy Pattern Trees (FPT), which is a hierarchical tree-based model, having as internal nodes, fuzzy logical operators and the leaves are composed of a combination of fuzzy terms with the input attributes. The classifier was obtained by creating a tree for each class, this tree will be a logic class description which allows the interpretation of the results. The learning method originally designed for FPT was replaced by Cartesian Genetic Programming in order to provide a better exploration of the search space. The FPT classifier was compared against Support Vector Machines, K Nearest Neighbour, Random Forests and others Fuzzy-Genetics methods on several datasets from the UCI Machine Learning Repository and it presented competitive results. It was also compared with Fuzzy Pattern trees generated by the former learning method and presented comparable results with smaller trees and a lower number of functions evaluations.

Made available in DSpace on 2016-06-02T19:05:32Z (GMT). No. of bitstreams: 1 2113.pdf: 882226 bytes, checksum: 022c380c1d469988d9e4617a030f17c3 (MD5) Previous issue date: 2008-08-06 / The combination of fuzzy systems with clustering algorithms has great acceptance in the scientific community mainly due to its adherence to the advantage balance principle of computational intelligence, in which different methodologies collaborate with each other potentializing the usefulness and applicability of the resulting systems. Fuzzy Modeling using clustering algorithms presents the transparency and comprehensibility typical of the linguistic fuzzy systems at the same time that benefits from the possibilities of dimensionality reduction by means of clustering. In this work is presented the Fuzzy-CCM method (Fuzzy Conditional Clustering based Modeling) which consists of a new approach for Fuzzy Modeling based on the Fuzzy Conditional Clustering algorithm aiming at providing new means to address the topic of interpretability of fuzzy rules bases. With the Fuzzy-CCM method the balance between interpretability and accuracy of fuzzy rules is dealt with through the definition of contexts defined by a small number of input variables and the generation of clusters induced by these contexts. The rules are generated in a different format, with linguistic variables and clusters in the antecedent. Some experiments have been carried out using different knowledge domains in order to validate the proposed approach by comparing the results with the ones obtained by the Wang&Mendel and conventional Fuzzy C-Means methods. The theoretical foundations, the advantages of the method, the experiments and results are presented and discussed. / A combinação de sistemas fuzzy com algoritmos de agrupamento tem grande aceitação na comunidade científica devido; principalmente, a sua aderência ao princípio de balanceamento de vantagens da inteligência computacional, no qual metodologias diferentes colaboram entre si, potencializando a utilidade e aplicabilidade dos sistemas resultantes. A modelagem fuzzy usando algoritmos de agrupamento apresenta a transparência e facilidade de compreensão típica dos sistemas fuzzy lingüísticos ao mesmo tempo em que se beneficia das possibilidades de redução da dimensionalidade por intermédio do agrupamento. Neste trabalho é apresentado o método Fuzzy-CCM (Fuzzy Conditional Clustering based Modeling), que consiste de uma nova abordagem de Modelagem Fuzzy baseada no algoritmo de Agrupamento Fuzzy Condicional, cujo objetivo é prover novos meios de tratar a questão da interpretabilidade de bases de regras fuzzy. Com o método Fuzzy-CCM, o balanço entre interpretabilidade e acuidade de regras fuzzy é tratado por meio da definição de contextos formados com um pequeno número de variáveis de entrada e a geração de grupos condicionados por estes contextos. As regras são geradas em um formato diferente, que contêm variáveis lingüísticas e grupos no seu antecedente. Alguns experimentos foram executados usando diferentes domínios de conhecimento a fim de validar a abordagem proposta, comparando os resultados obtidos usando a nova abordagem com os resultados obtidos usando os métodos Wang&Mendel e Fuzzy C-Means. A fundamentação teórica, as vantagens do método, os experimentos e os resultados obtidos são apresentados e discutidos.

Esta dissertação apresenta um sistema de indução de classificadores fuzzy. Ao invés de utilizar a abordagem tradicional de sistemas fuzzy baseados em regras, foi utilizado o modelo de Árvore de Padrões Fuzzy(APF), que é um modelo hierárquico, com uma estrutura baseada em árvores que possuem como nós internos operadores lógicos fuzzy e as folhas são compostas pela associação de termos fuzzy com os atributos de entrada. O classificador foi obtido sintetizando uma árvore para cada classe, esta árvore será uma descrição lógica da classe o que permite analisar e interpretar como é feita a classificação. O método de aprendizado originalmente concebido para a APF foi substituído pela Programação Genética Cartesiana com o intuito de explorar melhor o espaço de busca. O classificador APF foi comparado com as Máquinas de Vetores de Suporte, K-Vizinhos mais próximos, florestas aleatórias e outros métodos Fuzzy-Genéticos em diversas bases de dados do UCI Machine Learning Repository e observou-se que o classificador APF apresenta resultados competitivos. Ele também foi comparado com o método de aprendizado original e obteve resultados comparáveis com árvores mais compactas e com um menor número de avaliações. / This work presents a system for induction of fuzzy classifiers. Instead of the traditional fuzzy based rules, it was used a model called Fuzzy Pattern Trees (FPT), which is a hierarchical tree-based model, having as internal nodes, fuzzy logical operators and the leaves are composed of a combination of fuzzy terms with the input attributes. The classifier was obtained by creating a tree for each class, this tree will be a logic class description which allows the interpretation of the results. The learning method originally designed for FPT was replaced by Cartesian Genetic Programming in order to provide a better exploration of the search space. The FPT classifier was compared against Support Vector Machines, K Nearest Neighbour, Random Forests and others Fuzzy-Genetics methods on several datasets from the UCI Machine Learning Repository and it presented competitive results. It was also compared with Fuzzy Pattern trees generated by the former learning method and presented comparable results with smaller trees and a lower number of functions evaluations.

A Escala Facial de Dor - Revisada (EFD-R) é uma das escalas mais recomendadas na mensuração da intensidade da dor aguda em crianças. A versão original desta escala foi testada em crianças canadenses. O objetivo deste trabalho foi avaliar a validade, interpretação e preferência da versão brasileira da Escala Facial de Dor - Revisada (EFD-R-B), em duas amostras de crianças brasileiras: uma envolvendo dor aguda procedural e outra dor aguda pós-cirúrgica. Na primeira amostra participaram 77 crianças com idades entre 6 e 12 anos, do sexo feminino e masculino, que foram submetidas à coleta de sangue (dor procedural). As crianças estimaram a intensidade da sua dor, antes e após a punção venosa, na EFD-R-B. Na estimação após a punção venosa, a Escala Colorida Analógica (ECA) foi administrada junto com a EFD-R-B e, além disso, as crianças indicaram as faces que expressavam uma dor leve, moderada e severa, a escala que preferiam e o porquê. Na segunda amostra, participaram 53 crianças com idades entre 6 e 12 anos, do sexo feminino e masculino, que tinham sido submetidas a pequenas cirurgias (dor pós-cirúrgica). Nesta amostra, as crianças estimaram, na EFD-R-B e na ECA, a intensidade da dor que estavam sentindo no momento da entrevista. Também indicaram as faces que expressavam uma dor leve, moderada e severa, o limiar de tratamento da dor, a escala que preferiam e o porquê. Na comparação entre as pontuações obtidas na EFD-R-B e na ECA (validade convergente), nas duas amostras, os valores dos coeficientes Kendall\'s tau foram altos e significativos: =0,75 para o grupo de dor procedural e =0,79 para o grupo de dor pós-cirúrgica (p=0,00 nas duas amostras). No grupo de dor procedural, a EFD-R-B refletiu as mudanças na intensidade da dor vivenciada pelas crianças antes e após a punção venosa (validade concorrente): Teste de Wilcoxon z=-6,65; p=0,00. Considerando uma escala de 0 a 10 para a EFD-R-B, a mediana e a amplitude interquartil (AIQ) para as faces indicadas como expressivas de intensidade leve, moderada e severa, foram 2 (2-2), 4 (4-6) e 10 (10-10) respectivamente, no grupo de dor procedural, e 2 (2-2), 6 (4-8) e 10 (10-10) respectivamente, no grupo de dor pós-cirúrgica. Na estimação do limiar de tratamento da dor (grupo de dor pós-cirúrgica), a mediana (AIQ) foi 6 (4-10). No grupo de dor procedural, a EFD-R-B foi a escala preferida por 57,1% das crianças e a ECA por 41,6%; no grupo de dor pós-cirúrgica, a EFD-R-B foi escolhida por 66% das crianças e a ECA por 34%. Estas proporções somente foram significativas no grupo de dor pós-cirúrgica (X²=5,453 p=0,02). Nossos resultados mostram que a EFD-R-B possui propriedades similares à escala original e boa aceitação entre as crianças entrevistadas. A determinação dos valores das diferentes intensidades de dor e do limiar de tratamento da dor, para cada participante, representa uma evidência importante sobre a interpretação da EFD-R. / The Faces Pain Scale Revised (FPS-R) is one of the most recommended tools in measuring the intensity of acute pain in children. The aim of this study was to assess validity, interpretability and preference of the Brazilian version of the FPS-R (FPS-R-B), in two different clinical samples. The first sample contained seventy-seven children, 6 to 12 years old and both sexes, undergoing venipuncture for blood sample (procedural pain). These children estimated their perceived pain intensity in FPS-R-B before and after venipuncture. Furthermore, after venipuncture, children were asked: a) to evaluate the intensity of their needle pain using the Coloured Analogue Scale (CAS), b) to indicate on the Faces scale the intensities representing the mild, moderate and severe pain, and c) to choose the scale they preferred and indicate the reasons for the preference. The second sample included fifty-three children, 6 to 12 years old and both sexes, undergoing minor surgery (postoperative pain). Following surgery, children were asked: a) to provide a rating of their current pain intensity using the FPS-R-B and the CAS, b) to indicate on the Faces scale the intensities representing the mild, moderate and severe pain, c) to estimate, on the FPS-R-B, the intensity of pain that their felt to warrant pharmacologic intervention (pain treatment threshold), and d) to choose the scale they preferred and indicate the reasons for the preference. The degree of concordance between FPS-R-B and CAS ratings (convergent validity), for both samples, was high and statistically significant Kendall\'s tau value was 0.75 for the first sample, and 0.79 for the second sample, (p<0.05) . FPS-R-B reflected the changes in pain intensity before and after venipuncture (concurrent validity): Wilcoxon Test z=- 6.24; p< 0.05. On the 0-10 scale for the FPS-R-B, the median and interquartile range (IQR) of the intensities that represented mild, moderate and severe pain were 2 (2-2), 4 (4-6) e 10 (10-10) respectively, for the first sample, and 2 (2-2), 6 (4-8) e 10 (10-10) respectively, for the second sample. The median and IQR for pain treatment threshold were 6 (4-10). Fifty-seven percent of children in the first sample and 64.8% in the second sample preferred the FPS-R-B. These proportions were statistically significant for the second sample (X²=5,453 p<0,05). Our data show that the FPS-R-B has similar statistical properties to the original. New evidences were presented regarding interpretability of the FPS-R by determining each children\'s treatment threshold and estimate of mild, moderate and severe pain. In this study, the FPS-R-B was preferred by the majority of children.

Enterprising technologies and policies that focus on energy reduction in buildings are paramount to achieving global carbon emissions targets. Energy retrofits, building stock modelling, heating, ventilation, and air conditioning (HVAC) upgrades and demand side management all present high leverage opportunities in this regard. Advances in computing, data science and machine learning can be leveraged to enhance these methods and thus to expedite energy reduction in buildings but challenges such as lack of data, limited model generalizability and reliability and un-reproducible studies have resulted in restricted industry adoption. In this thesis, rigorous and reproducible studies are designed to evaluate the benefits and limitations of state-of-the-art machine learning and statistical techniques for high-impact applications, with an emphasis on addressing the challenges listed above. The scope of this work includes calibration of physics-based building models and supervised deep learning, both of which are used to estimate building properties from real and synthetic data. • Original grey-box methods are developed to characterize physical thermal properties (RC and RK)from real-world measurement data. • The novel application of supervised deep learning for thermal property estimation and HVAC systems identification is shown to achieve state-of-the-art performance (root mean squared error of 0.089 and 87% validation accuracy, respectively). • A rigorous empirical review is conducted to assess which types of gray and black box models are most suitable for practical application. The scope of the review is wider than previous studies, and the conclusions suggest a re-framing of research priorities for future work. • Modern interpretability techniques are used to provide unique insight into the learning behaviour of the black box methods. Overall, this body of work provides a critical appraisal of new and existing data-driven approaches for thermal property estimation in buildings. It provides valuable and novel insight into barriers to widespread adoption of these techniques and suggests pathways forward. Performance benchmarks, open-source model code and a parametrically generated, synthetic dataset are provided to support further research and to encourage industry adoption of the approaches. This lays the necessary groundwork for the accelerated adoption of data-driven models for thermal property identification in buildings. / Graduate

[pt] A série temporal é a representação mais comum para a evoluçãao no tempo de uma variável qualquer. Em um problema de previsão de séries temporais, procura-se ajustar um modelo para obter valores futuros da série, supondo que as informações necessárias para tal se encontram no próprio histórico da série. Como os fenômenos representados pelas séries temporais nem sempre existem de maneira isolada, pode-se enriquecer o modelo com os valores históricos de outras séries temporais relacionadas. A estrutura formada por diversas séries de mesmo intervalo e dimensão ocorrendo paralelamente é denominada série temporal multivariada. Esta dissertação propõe uma metodologia de geração de um Sistema de Inferência Fuzzy (SIF) para previsão de séries temporais multivariadas a partir de dados históricos, com o objetivo de obter bom desempenho tanto em termos de acurácia de previsão como no quesito interpretabilidade da base de regras – com o intuito de extrair conhecimento sobre o relacionamento entre as séries. Para tal, são abordados diversos aspectos relativos ao funcionamento e à construção de um SIF, levando em conta a sua complexidade e claridade semântica. O modelo é avaliado por meio de sua aplicação em séries temporais multivariadas da base completa da competição M3, comparandose a sua acurácia com as dos métodos participantes. Além disso, através de dois estudos de caso com dados reais públicos, suas possibilidades de extração de conhecimento são exploradas por meio de dois estudos de caso construídos a partir de dados reais. Os resultados confirmam a capacidade do AutoMFIS de modelar de maneira satisfatória séries temporais multivariadas e de extrair conhecimento da base de dados. / [en] A time series is the most commonly used representation for the evolution of a given variable over time. In a time series forecasting problem, a model aims at predicting the series future values, assuming that all information needed to do so is contained in the series past behavior. Since the phenomena described by the time series does not always exist in isolation, it is possible to enhance the model with historical data from other related time series. The structure formed by several different time series occurring in parallel, each featuring the same interval and dimension, is called a multivariate time series. This dissertation proposes a methodology for the generation of a Fuzzy Inference System (FIS) for multivariate time series forecasting from historical data, aiming at good performance in both forecasting accuracy and rule base interpretability – in order to extract knowledge about the relationship between the modeled time series. Several aspects related to the operation and construction of such a FIS are investigated regarding complexity and semantic clarity. The model is evaluated by applying it to multivariate time series obtained from the complete M3 competition database and by comparing it to other methods in terms of accuracy. In addition knowledge extraction possibilities are explored through two case studies built from actual data. Results confirm that AutoMFIS is indeed capable of modeling time series behaviors in a satisfactory way and of extractig meaningful knowldege from the databases.