Esse trabalho apresenta uma proposta na área de Educação em Microeletrônica que visa enriquecer práticas de ensino adotadas na área de projetos de circuitos integrados através do uso de máquinas de ensino-aprendizagem (TLM Teaching-Learning Machine) em aulas de laboratórios como instrumentos auxiliares e complementares ao ensino teórico. As TLMs propostas permitem a verificação experimental de conceitos fundamentais em VLSI Design, tais como: polarização de transistores NMOS e PMOS, inversores CMOS, curvas de transferência do inversor CMOS, implementação de diversas portas lógicas CMOS estática e dinâmica usando transistores de passagem ou portas de transmissão (NAND, NOR, AND, OR, XOR, XNOR, MUX, DECODER, Half ADDERs e Full ADDERs), Latches, Flip-flops e células de memória (RAM e ROM). A metodologia usada foi baseada em pesquisa bibliográfica, observações em sala de aula, participação em projetos didáticos, entrevistas com alunos e professores de microeletrônica. As TLMs foram construídas na forma de painéis de papelão de 100 cm x 70 cm com eletrônica embarcada ou conjuntos de módulos de circuito impresso com tamanhos A4 até A10, interligados entre si por meio de conectores, cabos elétricos padronizados e acondicionados em caixas flexíveis de borracha sintética. Considerou-se o uso combinado desses materiais com diferentes técnicas de montagens eletrônicas. No leiaute das TLMs foram considerados aspectos da interação homem-máquina (HMI) e projetos de interações por PREECE (2002), e da transposição didática de CHEVALHARD e JOSHUA (1981). Os resultados efetivos da aprendizagem usando TLMs foram obtidos por meio de uma dinâmica em sala de aula baseada no microensino em ALLEN (1967). / This paper presents a proposal in the area of Education in Microelectronics which aims to enrich the educational practices adopted in the area of integrated circuits design through the use of teaching-learning machines (TLM) in classes, laboratories as auxiliary and complementary instruments to the theoretical ones. The proposed TLMs allow the experimental verification of fundamental concepts in VLSI design, such as: NMOS and PMOS transistors biasing, CMOS inverters, transfer curves of a CMOS inverter, implementation of various static and dynamic CMOS logic using the pass-transistor or transmission gates (NAND, NOR, AND, OR, XOR, XNOR, MUX, DECODER, Half ADDERs and Full ADDERs), Latches, flip-flops and memory cells (RAM and ROM). The used methodology was based on a literature search, observations in the classroom, participation in educational projects, interview of students and professors involved with microelectronics. The TLMs were assembled in the form of paper panels, 100 cm x 70 cm embedded with electronic modules, or sets of printed circuit boards with A4 size up to A10 size, connected with each other through connectors, electrical wires and packed in synthetic rubber flexible boxes. The combined use of these materials with different techniques of electronic assemblies has been very important. The layout of TLMs concerns about the aspects of human-machine interaction (HMI) and design interactions from PREECE (2002), and the didactical transposition from CHEVALHARD and JOSHUA (1981). The effective learning results using TLMs were obtained through a dynamic in classroom based on microteaching from ALLEN (1967).

Nos últimos 30 anos, a área de aprendizagem de máquina desenvolveu-se de forma comparável com a Física no início do século XX. Esse avanço tornou possível a resolução de problemas do mundo real que anteriormente não poderiam ser solucionados por máquinas, devido à dificuldade de modelos puramente estatísticos ajustarem-se de forma satisfatória aos dados de treinamento. Dentre tais avanços, pode-se citar a utilização de técnicas de aprendizagem de máquina na área de Química Medicinal, envolvendo métodos de análise, representação e predição de informação molecular por meio de recursos computacionais. Os dados utilizados no contexto biológico possuem algumas características particulares que podem influenciar no resultado de sua análise. Dentre estas, pode-se citar a complexidade das informações moleculares, o desbalanceamento das classes envolvidas e a existência de dados incompletos ou rotulados de forma incerta. Tais adversidades podem prejudicar o processo de identificação de compostos candidatos a novos fármacos, se não forem tratadas de forma adequada. Neste trabalho, foi abordada uma técnica de aprendizagem de máquina semi-supervisionada capaz de reduzir o impacto causado pelo problema da incerteza na rotulação dos dados, aplicando um método para estimar rótulos mais confiáveis para os compostos químicos existentes no conjunto de treinamento. Na tentativa de evitar os efeitos causados pelo desbalanceamento dos dados, foi incorporada ao processo de estimação de rótulos uma abordagem sensível ao custo, com o objetivo de evitar o viés em benefício da classe majoritária. Após o tratamento do problema da incerteza na rotulação, classificadores baseados em Máquinas de Aprendizado Extremo foram construídos, almejando boa capacidade de aproximação em um tempo de processamento reduzido em relação a outras abordagens de classificação comumente aplicadas. Por fim, o desempenho dos classificadores construídos foi avaliado por meio de análises dos resultados obtidos, confrontando o cenário com os dados originais e outros com as novas rotulações obtidas durante o processo de estimação semi-supervisionado / In the last 30 years, the area of machine learning has developed in a way comparable to Physics in the early twentieth century. This breakthrough has made it possible to solve real-world problems that previously could not be solved by machines because of the difficulty of purely statistical models to fit satisfactorily with training data. Among these advances, one can cite the use of machine learning techniques in the area of Medicinal Chemistry, involving methods for analysing, representing and predicting molecular information through computational resources. The data used in the biological context have some particular characteristics that can influence the result of its analysis. These include the complexity of molecular information, the imbalance of the classes involved, and the existence of incomplete or uncertainly labeled data. If they are not properly treated, such adversities may affect the process of identifying candidate compounds for new drugs. In this work, a semi-supervised machine learning technique was considered to reduce the impact caused by the problem of uncertainty in the data labeling, by applying a method to estimate more reliable labels for the chemical compounds in the training set. In an attempt to reduce the effects caused by data imbalance, a cost-sensitive approach was incorporated to the label estimation process, in order to avoid bias in favor of the majority class. After addressing the uncertainty problem in labeling, classifiers based on Extreme Learning Machines were constructed, aiming for good approximation ability in a reduced processing time in relation to other commonly applied classification approaches. Finally, the performance of the classifiers constructed was evaluated by analyzing the results obtained, comparing the scenario with the original data and others with the new labeling obtained by the semi-supervised estimation process

Esse trabalho apresenta uma proposta na área de Educação em Microeletrônica que visa enriquecer práticas de ensino adotadas na área de projetos de circuitos integrados através do uso de máquinas de ensino-aprendizagem (TLM Teaching-Learning Machine) em aulas de laboratórios como instrumentos auxiliares e complementares ao ensino teórico. As TLMs propostas permitem a verificação experimental de conceitos fundamentais em VLSI Design, tais como: polarização de transistores NMOS e PMOS, inversores CMOS, curvas de transferência do inversor CMOS, implementação de diversas portas lógicas CMOS estática e dinâmica usando transistores de passagem ou portas de transmissão (NAND, NOR, AND, OR, XOR, XNOR, MUX, DECODER, Half ADDERs e Full ADDERs), Latches, Flip-flops e células de memória (RAM e ROM). A metodologia usada foi baseada em pesquisa bibliográfica, observações em sala de aula, participação em projetos didáticos, entrevistas com alunos e professores de microeletrônica. As TLMs foram construídas na forma de painéis de papelão de 100 cm x 70 cm com eletrônica embarcada ou conjuntos de módulos de circuito impresso com tamanhos A4 até A10, interligados entre si por meio de conectores, cabos elétricos padronizados e acondicionados em caixas flexíveis de borracha sintética. Considerou-se o uso combinado desses materiais com diferentes técnicas de montagens eletrônicas. No leiaute das TLMs foram considerados aspectos da interação homem-máquina (HMI) e projetos de interações por PREECE (2002), e da transposição didática de CHEVALHARD e JOSHUA (1981). Os resultados efetivos da aprendizagem usando TLMs foram obtidos por meio de uma dinâmica em sala de aula baseada no microensino em ALLEN (1967). / This paper presents a proposal in the area of Education in Microelectronics which aims to enrich the educational practices adopted in the area of integrated circuits design through the use of teaching-learning machines (TLM) in classes, laboratories as auxiliary and complementary instruments to the theoretical ones. The proposed TLMs allow the experimental verification of fundamental concepts in VLSI design, such as: NMOS and PMOS transistors biasing, CMOS inverters, transfer curves of a CMOS inverter, implementation of various static and dynamic CMOS logic using the pass-transistor or transmission gates (NAND, NOR, AND, OR, XOR, XNOR, MUX, DECODER, Half ADDERs and Full ADDERs), Latches, flip-flops and memory cells (RAM and ROM). The used methodology was based on a literature search, observations in the classroom, participation in educational projects, interview of students and professors involved with microelectronics. The TLMs were assembled in the form of paper panels, 100 cm x 70 cm embedded with electronic modules, or sets of printed circuit boards with A4 size up to A10 size, connected with each other through connectors, electrical wires and packed in synthetic rubber flexible boxes. The combined use of these materials with different techniques of electronic assemblies has been very important. The layout of TLMs concerns about the aspects of human-machine interaction (HMI) and design interactions from PREECE (2002), and the didactical transposition from CHEVALHARD and JOSHUA (1981). The effective learning results using TLMs were obtained through a dynamic in classroom based on microteaching from ALLEN (1967).

Efficient and effective road traffic prediction and management techniques are crucial in intelligent transportation systems. It can positively influence road advancement, safety enhancement, regulation formulation, and route planning to save living things in advance from road traffic accidents. This thesis considers road safety by predicting the number of casualties if an accident occurs using multiple traffic accident attributes. It helps individuals (drivers) or traffic offices to adjust and control their contributions for the occurrence of an accident before emerging it. Three candidate algorithms from different regression fit patterns are proposed and evaluated to conduct the thesis: the bagging, linear, and non-linear fitting patterns. The gradient boosting machines (GBoost) from the bagging, Linearsupport vector regression (LinearSVR) from the linear, and extreme learning machines (ELM) also from the non-linear side are the selected algorithms. RMSE and MAE performance evaluation metrics are applied to evaluate the models. The GBoost achieved a better performance than the other two with a low error rate and minimum prediction interval value for 95% prediction interval. A SHAP (SHapley Additive exPlanations) interpretation technique is applied to interpret each model at the global interpretation level using SHAP’s beeswarm plots. Finally, suggestions for future improvements are presented via the dataset and hyperparameter tuning.

Different architectures of gating networks that aggregate information from multiple modalities and their suitability for decision fusion is investigated. The research question, how does a gating network for decision fusion in multimodal classification problem compare to other alternatives, is answered by a quantitative and inductive reasoning approach. This is done by training different machine learning methods on individual modalities and fusing their predictions forthe final classification using M-MNIST, a new data set with three modalities (image, audio, and text). The gating networks achieve greater classification accuracy when fusing information from all modalities, in contrast to considering only one modality, or without fusion. The gating network potential is demonstrated by training it on modalities with different levels of classification accuracy where it achieves the highest average normalized gain when scoring the highest validation accuracy of the three fusion methods, where the results indicate that the gating network can suppress noise in the data. Moreover, by adding an additional weak modality to the gating network, the classification accuracy is improved, hinting at that there might be an incentive to use many weak modalities instead of a few strong ones. / Olika arkitekturer för gating-nätverk som aggregerar information från flera olika modaliteter undersöks här, liksom deras lämplighet för användning för att förena olika beslutsunderlag. Forskningsfrågan ”Hur bra står sig ett gating- nätverk för att ensa beslutsunderlag i multimodala klassificeringsproblem?” besvaras med ett kvantitativt och induktivt tillvägagångssätt. Olika maskininlärningsmetoder har tränats på singulära modaliteter och sedan ensa deras prediktioner för klassificering i M-MNIST: en ny ansamling data med tre modaliteter (bild, ljud och text). Nätverket uppnår bättre resultat i klassificeringen när information från alla modaliteter används, än när endast en modalitet används (eller utan ensning). Nätverkets potential har kunnat illustreras genom träning på modaliteter med olika nivåer av klassificeringskapacitet. Det får bästa resultat, mätt i högsta genomsnittliga normaliserade ökning, i samband med högsta valideringsresultat av de tre metoderna för ensning. Här indikerar resultaten att gating-nätverket kan undertrycka brus i datat. Genom att lägga till ytterligare en (svag) modalitet till nätverket så kan klassificeringens kvalitet ökas på, vilket antyder att det kan finnas skäl att använda många svaga modaliteter iställer för få starka modaliteter.

La correcta qualificació de risc de crèdit d'un emissor és un factor crític en l’economia actual. Aquest és un punt d’acord entre professionals i acadèmics. Actualment, des dels mitjans de comunicació s’han difós sovint notícies d'impacte provocades per agències de ràting. És per aquest motiu que treball d'anàlisi realitzat per experts financers aporta importants recursos a les empreses de consultoria d'inversió i agències qualificadores. Avui en dia, hi ha molts avenços metodològics i tècnics que permeten donar suport a la tasca que fan els professionals de la qualificació de la qualitat de crèdit dels emissors. Tanmateix encara queden molts buits per completar i àrees a desenvolupar per tal què aquesta tasca sigui tan precisa com cal. D'altra banda, els sistemes d'aprenentatge automàtic basats en funcions nucli, particularment les Support Vector Machines (SVM), han donat bons resultats en problemes de classificació quan les dades no són linealment separables o quan hi ha patrons amb soroll. A més, al usar estructures basades en funcions nucli és possible tractar qualsevol espai de dades, ampliant les possibilitats per trobar relacions entre els patrons, tasca que no resulta fàcil amb tècniques estadístiques convencionals. L’objectiu d'aquesta tesi és examinar les aportacions que s'han fet en la rèplica de ràting, i alhora, examinar diferents alternatives que permetin millorar l'acompliment de la rèplica amb SVM. Per a això, primer s'ha revisat la literatura financera amb la idea d'obtenir una visió general i panoràmica dels models usats per al mesurament del risc de crèdit. S'han revisat les aproximacions de mesurament de risc de crèdit individuals, utilitzades principalment per a la concessió de crèdits bancaris i per l'avaluació individual d'inversions en títols de renda fixa. També s'han revisat models de carteres d'actius, tant aquells proposats des del món acadèmic com els patrocinats per institucions financeres. A més, s'han revisat les aportacions dutes a terme per avaluar el risc de crèdit usant tècniques estadístiques i sistemes d'aprenentatge automàtic. S'ha fet especial èmfasi en aquest últim conjunt de mètodes d'aprenentatge i en el conjunt de metodologies usades per realitzar adequadament la rèplica de ràting. Per millorar l'acompliment de la rèplica, s'ha triat una tècnica de discretització de les variables sota la suposició que, per emetre l'opinió tècnica del ràting de les companyies, els experts financers en forma intuïtiva avaluen les característiques de les empreses en termes intervalars. En aquesta tesi, per fer la rèplica de ràting, s'ha fet servir una mostra de dades de companyies de països desenvolupats. S'han usat diferents tipus de SVM per replicar i s'ha exposat la bondat dels resultats d'aquesta rèplica, comparant-la amb altres dues tècniques estadístiques àmpliament usades en la literatura financera. S'ha concentrat l'atenció de la mesura de la bondat de l'ajust dels models en les taxes d'encert i en la forma en què es distribueixen els errors. D'acord amb els resultats obtinguts es pot sostenir que l'acompliment de les SVM és millor que el de les tècniques estadístiques usades en aquesta tesi, i després de la discretització de les dades d'entrada s'ha mostrat que no es perd informació rellevant en aquest procés. Això contribueix a la idea que els experts financers instintivament realitzen un procés similar de discretització de la informació financera per lliurar la seva opinió creditícia de les companyies qualificades. / La correcta calificación de riesgo crediticio de un emisor es un factor crítico en nuestra actual economía. Profesionales y académicos están de acuerdo en esto, y los medios de comunicación han difundido mediáticamente eventos de impacto provocados por agencias de rating. Por ello, el trabajo de análisis del deudor realizado por expertos financieros conlleva importantes recursos en las empresas de consultoría de inversión y agencias calificadoras. Hoy en día, muchos avances metodológicos y técnicos permiten el apoyo a la labor que hacen los profesionales en de calificación de la calidad crediticia de los emisores. No obstante aún quedan muchos vacíos por completar y áreas que desarrollar para que esta tarea sea todo lo precisa que necesita. Por otra parte, los sistemas de aprendizaje automático basados en funciones núcleo, particularmente las Support Vector Machines (SVM), han dado buenos resultados en problemas de clasificación cuando los datos no son linealmente separables o cuando hay patrones ruidosos. Además, al usar estructuras basadas en funciones núcleo resulta posible tratar cualquier espacio de datos, expandiendo las posibilidades para encontrar relaciones entre los patrones, tarea que no resulta fácil con técnicas estadísticas convencionales. El propósito de esta tesis es examinar los aportes que se han hecho en la réplica de rating, y a la vez, examinar diferentes alternativas que permitan mejorar el desempeño de la réplica con SVM. Para ello, primero se ha revisado la literatura financiera con la idea de obtener una visión general y panorámica de los modelos usados para la medición del riesgo crediticio. Se han revisado las aproximaciones de medición de riesgo crediticio individuales, utilizadas principalmente para la concesión de créditos bancarios y para la evaluación individual de inversiones en títulos de renta fija. También se han revisado modelos de carteras de activos, tanto aquellos propuestos desde el mundo académico como los patrocinados por instituciones financieras. Además, se han revisado los aportes llevados a cabo para evaluar el riesgo crediticio usando técnicas estadísticas y sistemas de aprendizaje automático. Se ha hecho especial énfasis en este último conjunto de métodos de aprendizaje y en el conjunto de metodologías usadas para realizar adecuadamente la réplica de rating. Para mejorar el desempeño de la réplica, se ha elegido una técnica de discretización de las variables bajo la suposición de que, para emitir la opinión técnica del rating de las compañías, los expertos financieros en forma intuitiva evalúan las características de las empresas en términos intervalares. En esta tesis, para realizar la réplica de rating, se ha usado una muestra de datos de compañías de países desarrollados. Se han usado diferentes tipos de SVM para replicar y se ha expuesto la bondad de los resultados de dicha réplica, comparándola con otras dos técnicas estadísticas ampliamente usadas en la literatura financiera. Se ha concentrado la atención de la medición de la bondad del ajuste de los modelos en las tasas de acierto y en la forma en que se distribuyen los errores. De acuerdo con los resultados obtenidos se puede sostener que el desempeño de las SVM es mejor que el de las técnicas estadísticas usadas en esta tesis; y luego de la discretización de los datos de entrada se ha mostrado que no se pierde información relevante en dicho proceso. Esto contribuye a la idea de que los expertos financieros instintivamente realizan un proceso similar de discretización de la información financiera para entregar su opinión crediticia de las compañías calificadas. / Proper credit rating of an issuer is a critical factor in our current economy. Professionals and academics agree on this, and the media have spread impact events caused by rating agencies. Therefore, the analysis performed by the debtor's financial experts has significant resources on investment consulting firms and rating agencies. Nowadays, many methodological and technical exist to support the professional qualification of the credit quality of issuers. However there are still many gaps to complete and areas to develop for this task to be as precise as needed. Moreover, machine learning systems based on core functions, particularly Support Vector Machines (SVM) have been successful in classification problems when the data are not linearly separable or when noisy patterns are used. In addition, by using structures based on kernel functions is possible to treat any data space, expanding the possibilities to find relationships between patterns, a task that is not easy with conventional statistical techniques. The purpose of this thesis is to examine the contributions made in the replica of rating, and, to look at different alternatives to improve the performance of prediction with SVM. To do this, we first reviewed the financial literature and overview the models used to measure credit risk. We reviewed the approaches of individual credit risk measurement, used principally for the lending bank and the individual assessment of investments in fixed income securities. Models based on portfolio of assets have also been revised, both those proposed from academia such as those used by financial institutions. In addition, we have reviewed the contributions carried out to assess credit risk using statistical techniques and machine learning systems. Particular emphasis has been placed on learning methods methodologies used to perform adequately replicate rating. To improve the performance of replication, a discretization technique has been chosen for the variables under the assumption that, for the opinion of the technical rating companies, financial experts intuitively evaluate the performances of companies in intervalar terms. In this thesis, for rating replication, we used a data sample of companies in developed countries. Different types of SVM have been used to replicate and discussed the goodness of the results of the replica, compared with two other statistical techniques widely used in the financial literature. Special attention has been given to measure the goodness of fit of the models in terms of rates of success and how they errors are distributed. According to the results it can be argued that the performance of SVM is better than the statistical techniques used in this thesis. In addition, it has been shown that in the process of discretization of the input data no-relevant information is lost. This contributes to the idea that financial experts instinctively made a similar process of discretization of financial information to deliver their credit opinion of the qualified companies.

<p dir="ltr">Detecting anomalies in spacecraft time-series data is a high priority, especially considering the harshness of the spacecraft operating environment. These anomalies often function as precursors for system failure. Traditionally, the time-series data channels are monitored manually by domain experts, which is time-consuming. Additionally, there are thousands of channels to monitor. Machine learning methods have proven to be useful for automatic anomaly detection, but a unique model must be trained from scratch for each time-series. This thesis proposes three approaches for reducing training costs. First, a transfer learning approach that finetunes a general pre-trained model to reduce training time and the number of unique models required for a given spacecraft. The second and third approaches both use online learning to reduce the amount of training data and time needed to identify anomalies. The second approach leverages an ensemble of extreme learning machines while the third approach uses deep learning models. All three approaches are shown to achieve reasonable anomaly detection performance with reduced training costs.</p><p dir="ltr">Measuring the phenotypes, or observable traits, of a plant enables plant scientists to understand the interaction between the growing environment and the genetic characteristics of a plant. Plant phenotyping is typically done manually, and often involves destructive sampling, making the entire process labor-intensive and difficult to replicate. In this thesis, we use image processing for characterizing two different disease progressions. Tar spot disease can be identified visually as it induces small black circular spots on the leaf surface. We propose using a Mask R-CNN to detect tar spots from RGB images of leaves, thus enabling rapid non-destructive phenotyping of afflicted plants. The second disease, bacteria-induced wilting, is measured using a visual assessment that is often subjective. We design several metrics that can be extracted from RGB images that can be used to generate consistent wilting measurements with a random forest. Both approaches ensure faster, replicable results, enabling accurate, high-throughput analysis to draw conclusions about effective disease treatments and plant breeds.</p>