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En la vida cotidiana, detectar una falacia puede tener importantes implicaciones en distintas situaciones sociales. Descifrar mentiras, puede ser determinante en situaciones que impliquen consecuencias graves o moderadas; como el caso de investigaciones policiales. El trabajo expuesto en las siguientes paginas tiene como fin la realización de un sistema de detección de mentiras que utilice una cámara web como medio único para la detección. Además de esto, se busca realizar la investigación correspondiente a las subáreas relacionadas al problema. Estas son la de detección de mentiras, Deep learning y visión computacional. En este trabajo expuesto, se asumirá al acto de mentir como cualquier acto que busque comunicar información falsa o trastornada, de forma deliberada con la finalidad de engañar a otros. La investigación realizada, se hará presente en el desarrollo de un proyecto cuyo alcance consiste en la creación de una aplicación capaz de detectar si una persona dice la verdad a partir de su reconocimiento facial. Para ello, se utilizarán técnicas de visión computacional y machine learning con el fin de dar otra opción más económica y accesible ante las otras metodologías (polígrafo, ERPs, fMRI) que se basan en analizar el estado cerebral requieren de maquinaria extremadamente costosa y tienden a tener la misma precisión que el uso de polígrafos. / In everyday life, detecting a fallacy can have important implications in different social situations. Deciphering lies can be decisive in situations that involve serious or moderate consequences, as in the case of police investigations. The work presented in the following pages is aimed at the realization of a lie detection system that uses a web camera as the only means for detection. In addition to this, it seeks to carry out the investigation corresponding to the subareas related to the problem. These subareas are lie detection, deep learning, and computer vision. In this exposed work, the act of lying will be assumed as any act that seeks to communicate false or disturbed information, deliberately with the purpose of deceiving others. The research carried out will be present in the development of a project whose scope consists of the creation of an application capable of detecting if a person is telling the truth from their facial recognition. To do this, computer vision and machine learning techniques will be used in order to provide another cheaper and more accessible option compared to other methodologies (polygraph, ERPs, fMRI) that are based on analyzing the brain state, require extremely expensive machinery and tend to have the same precision as the use of polygraphs. / Trabajo de investigación

Road ruts are depressions or grooves worn into a road. Increases in rut depth are highly undesirable due to the heightened risk of hydroplaning. Accurately predicting increases in road rut depth is important for maintenance planning within the Swedish Transport Administration. At the time of writing this paper, the agency utilizes a linear regression model and is developing a feed-forward neural network for road rut predictions. The aim of the study was to evaluate the possibility of using a Recurrent Neural Network to predict road rut. Through design science research, an artefact in the form of a LSTM model was designed, developed, and evaluated.The dataset consisted of multiple-multivariate short time series where research was limited. Case studies were conducted which inspired the conceptual design of the model. The baseline LSTM model proposed in this paper utilizes the full dataset in combination with time-series individualization through an added index feature. Additional features thought to correlate with rut depth was also studied through multiple training set variations. The model was evaluated by calculating the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) for each training set variation. The baseline model predicted rut depth with a MAE of 0.8110 (mm) and a RMSE of 1.124 (mm) outperforming a control set without the added index. The feature with the highest correlation to rut depth was curvature with a MAEof 0.8031 and a RMSE of 1.1093. Initial finding shows that there is a possibility of utilizing an LSTM model trained on multiple-multivariate time series to predict rut depth. Time series individualization through an added index feature yielded better results than control, indicating that it had the desired effect on model performance.

Controlling a high-dimensional dynamical system with continuous state and action spaces in a partially unknown environment like a gas turbine is a challenging problem. So far often hard coded rules based on experts´ knowledge and experience are used. Machine learning techniques, which comprise the field of reinforcement learning, are generally only applied to sub-problems. A reason for this is that most standard RL approaches still fail to produce satisfactory results in those complex environments. Besides, they are rarely data-efficient, a fact which is crucial for most real-world applications, where the available amount of data is limited. In this thesis recurrent neural reinforcement learning approaches to identify and control dynamical systems in discrete time are presented. They form a novel connection between recurrent neural networks (RNN) and reinforcement learning (RL) techniques. RNN are used as they allow for the identification of dynamical systems in form of high-dimensional, non-linear state space models. Also, they have shown to be very data-efficient. In addition, a proof is given for their universal approximation capability of open dynamical systems. Moreover, it is pointed out that they are, in contrast to an often cited statement, well able to capture long-term dependencies. As a first step towards reinforcement learning, it is shown that RNN can well map and reconstruct (partially observable) MDP. In the so-called hybrid RNN approach, the resulting inner state of the network is then used as a basis for standard RL algorithms. The further developed recurrent control neural network combines system identification and determination of an optimal policy in one network. In contrast to most RL methods, it determines the optimal policy directly without making use of a value function. The methods are tested on several standard benchmark problems. In addition, they are applied to different kinds of gas turbine simulations of industrial scale.

Les radiologues utilisent au quotidien des solutions d'imagerie médicale pour le diagnostic. L'amélioration de l'expérience utilisateur est toujours un axe majeur de l'effort continu visant à améliorer la qualité globale et l'ergonomie des produits logiciels. Les applications de monitoring permettent en particulier d'enregistrer les actions successives effectuées par les utilisateurs dans l'interface du logiciel. Ces interactions peuvent être représentées sous forme de séquences d'actions. Sur la base de ces données, ce travail traite de deux sujets industriels : les pannes logicielles et l'ergonomie des logiciels. Ces deux thèmes impliquent d'une part la compréhension des modes d'utilisation, et d'autre part le développement d'outils de prédiction permettant soit d'anticiper les pannes, soit d'adapter dynamiquement l'interface logicielle en fonction des besoins des utilisateurs. Tout d'abord, nous visons à identifier les origines des crashes du logiciel qui sont essentielles afin de pouvoir les corriger. Pour ce faire, nous proposons d'utiliser un test binomial afin de déterminer quel type de pattern est le plus approprié pour représenter les signatures de crash. L'amélioration de l'expérience utilisateur par la personnalisation et l'adaptation des systèmes aux besoins spécifiques de l'utilisateur exige une très bonne connaissance de la façon dont les utilisateurs utilisent le logiciel. Afin de mettre en évidence les tendances d'utilisation, nous proposons de regrouper les sessions similaires. Nous comparons trois types de représentation de session dans différents algorithmes de clustering. La deuxième contribution de cette thèse concerne le suivi dynamique de l'utilisation du logiciel. Nous proposons deux méthodes -- basées sur des représentations différentes des actions d'entrée -- pour répondre à deux problématiques industrielles distinctes : la prédiction de la prochaine action et la détection du risque de crash logiciel. Les deux méthodologies tirent parti de la structure récurrente des réseaux LSTM pour capturer les dépendances entre nos données séquentielles ainsi que leur capacité à traiter potentiellement différents types de représentations d'entrée pour les mêmes données. / Radiologists use medical imaging solutions on a daily basis for diagnosis. Improving user experience is a major line of the continuous effort to enhance the global quality and usability of software products. Monitoring applications enable to record the evolution of various software and system parameters during their use and in particular the successive actions performed by the users in the software interface. These interactions may be represented as sequences of actions. Based on this data, this work deals with two industrial topics: software crashes and software usability. Both topics imply on one hand understanding the patterns of use, and on the other developing prediction tools either to anticipate crashes or to dynamically adapt software interface according to users' needs. First, we aim at identifying crash root causes. It is essential in order to fix the original defects. For this purpose, we propose to use a binomial test to determine which type of patterns is the most appropriate to represent crash signatures. The improvement of software usability through customization and adaptation of systems to each user's specific needs requires a very good knowledge of how users use the software. In order to highlight the trends of use, we propose to group similar sessions into clusters. We compare 3 session representations as inputs of different clustering algorithms. The second contribution of our thesis concerns the dynamical monitoring of software use. We propose two methods -- based on different representations of input actions -- to address two distinct industrial issues: next action prediction and software crash risk detection. Both methodologies take advantage of the recurrent structure of LSTM neural networks to capture dependencies among our sequential data as well as their capacity to potentially handle different types of input representations for the same data.

[ES] Durante los últimos años, los repositorios multimedia en línea se han convertido en fuentes clave de conocimiento gracias al auge de Internet, especialmente en el área de la educación. Instituciones educativas de todo el mundo han dedicado muchos recursos en la búsqueda de nuevos métodos de enseñanza, tanto para mejorar la asimilación de nuevos conocimientos, como para poder llegar a una audiencia más amplia. Como resultado, hoy en día disponemos de diferentes repositorios con clases grabadas que siven como herramientas complementarias en la enseñanza, o incluso pueden asentar una nueva base en la enseñanza a distancia. Sin embargo, deben cumplir con una serie de requisitos para que la experiencia sea totalmente satisfactoria y es aquí donde la transcripción de los materiales juega un papel fundamental. La transcripción posibilita una búsqueda precisa de los materiales en los que el alumno está interesado, se abre la puerta a la traducción automática, a funciones de recomendación, a la generación de resumenes de las charlas y además, el poder hacer llegar el contenido a personas con discapacidades auditivas. No obstante, la generación de estas transcripciones puede resultar muy costosa. Con todo esto en mente, la presente tesis tiene como objetivo proporcionar nuevas herramientas y técnicas que faciliten la transcripción de estos repositorios. En particular, abordamos el desarrollo de un conjunto de herramientas de reconocimiento de automático del habla, con énfasis en las técnicas de aprendizaje profundo que contribuyen a proporcionar transcripciones precisas en casos de estudio reales. Además, se presentan diferentes participaciones en competiciones internacionales donde se demuestra la competitividad del software comparada con otras soluciones. Por otra parte, en aras de mejorar los sistemas de reconocimiento, se propone una nueva técnica de adaptación de estos sistemas al interlocutor basada en el uso Medidas de Confianza. Esto además motivó el desarrollo de técnicas para la mejora en la estimación de este tipo de medidas por medio de Redes Neuronales Recurrentes. Todas las contribuciones presentadas se han probado en diferentes repositorios educativos. De hecho, el toolkit transLectures-UPV es parte de un conjunto de herramientas que sirve para generar transcripciones de clases en diferentes universidades e instituciones españolas y europeas. / [CAT] Durant els últims anys, els repositoris multimèdia en línia s'han convertit en fonts clau de coneixement gràcies a l'expansió d'Internet, especialment en l'àrea de l'educació. Institucions educatives de tot el món han dedicat molts recursos en la recerca de nous mètodes d'ensenyament, tant per millorar l'assimilació de nous coneixements, com per poder arribar a una audiència més àmplia. Com a resultat, avui dia disposem de diferents repositoris amb classes gravades que serveixen com a eines complementàries en l'ensenyament, o fins i tot poden assentar una nova base a l'ensenyament a distància. No obstant això, han de complir amb una sèrie de requisits perquè la experiència siga totalment satisfactòria i és ací on la transcripció dels materials juga un paper fonamental. La transcripció possibilita una recerca precisa dels materials en els quals l'alumne està interessat, s'obri la porta a la traducció automàtica, a funcions de recomanació, a la generació de resums de les xerrades i el poder fer arribar el contingut a persones amb discapacitats auditives. No obstant, la generació d'aquestes transcripcions pot resultar molt costosa. Amb això en ment, la present tesi té com a objectiu proporcionar noves eines i tècniques que faciliten la transcripció d'aquests repositoris. En particular, abordem el desenvolupament d'un conjunt d'eines de reconeixement automàtic de la parla, amb èmfasi en les tècniques d'aprenentatge profund que contribueixen a proporcionar transcripcions precises en casos d'estudi reals. A més, es presenten diferents participacions en competicions internacionals on es demostra la competitivitat del programari comparada amb altres solucions. D'altra banda, per tal de millorar els sistemes de reconeixement, es proposa una nova tècnica d'adaptació d'aquests sistemes a l'interlocutor basada en l'ús de Mesures de Confiança. A més, això va motivar el desenvolupament de tècniques per a la millora en l'estimació d'aquest tipus de mesures per mitjà de Xarxes Neuronals Recurrents. Totes les contribucions presentades s'han provat en diferents repositoris educatius. De fet, el toolkit transLectures-UPV és part d'un conjunt d'eines que serveix per generar transcripcions de classes en diferents universitats i institucions espanyoles i europees. / [EN] During the last years, on-line multimedia repositories have become key knowledge assets thanks to the rise of Internet and especially in the area of education. Educational institutions around the world have devoted big efforts to explore different teaching methods, to improve the transmission of knowledge and to reach a wider audience. As a result, online video lecture repositories are now available and serve as complementary tools that can boost the learning experience to better assimilate new concepts. In order to guarantee the success of these repositories the transcription of each lecture plays a very important role because it constitutes the first step towards the availability of many other features. This transcription allows the searchability of learning materials, enables the translation into another languages, provides recommendation functions, gives the possibility to provide content summaries, guarantees the access to people with hearing disabilities, etc. However, the transcription of these videos is expensive in terms of time and human cost. To this purpose, this thesis aims at providing new tools and techniques that ease the transcription of these repositories. In particular, we address the development of a complete Automatic Speech Recognition Toolkit with an special focus on the Deep Learning techniques that contribute to provide accurate transcriptions in real-world scenarios. This toolkit is tested against many other in different international competitions showing comparable transcription quality. Moreover, a new technique to improve the recognition accuracy has been proposed which makes use of Confidence Measures, and constitutes the spark that motivated the proposal of new Confidence Measures techniques that helped to further improve the transcription quality. To this end, a new speaker-adapted confidence measure approach was proposed for models based on Recurrent Neural Networks. The contributions proposed herein have been tested in real-life scenarios in different educational repositories. In fact, the transLectures-UPV toolkit is part of a set of tools for providing video lecture transcriptions in many different Spanish and European universities and institutions. / Agua Teba, MÁD. (2019). CONTRIBUTIONS TO EFFICIENT AUTOMATIC TRANSCRIPTION OF VIDEO LECTURES [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/130198 / TESIS

Changes to the signal environment of a radar warning receiver (RWR) over time through dataset drift can negatively affect a machine learning (ML) model, deployed for radar emitter classification (REC). The training data comes from a simulator at Saab AB, in the form of pulsed radar in a time-series. In order to investigate this phenomenon on a neural network (NN), this study first implements an underlying classifier (UC) in the form of a deep ensemble (DE), where each ensemble member consists of an NN with two independently trained bidirectional LSTM channels for each of the signal features pulse repetition interval (PRI), pulse width (PW) and carrier frequency (CF). From tests, the UC performs best for REC when using all three features. Because dataset drift can be treated as detecting out-of-distribution (OOD) samples over time, the aim is to reduce NN overconfidence on data from unseen radar emitters in order to enable OOD detection. The method estimates uncertainty with predictive entropy and classifies samples reaching an entropy larger than a threshold as OOD. In the first set of tests, OOD is defined from holding out one feature modulation from the training dataset, and choosing this as the only modulation in the OOD dataset used during testing. With this definition, Stagger and Jitter are most difficult to detect as OOD. Moreover, using DEs with 6 ensemble members and implementing LogitNorm to the architecture improves the OOD detection performance. Furthermore, the OOD detection method performs well for up to 300 emitter classes and predictive entropy outperforms the baseline for almost all tests. Finally, the model performs worse when OOD is simply defined as signals from unseen emitters, because of a precision decrease. In conclusion, the implemented changes managed to reduce the overconfidence for this particular NN, and improve OOD detection for REC.

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[pt] Por conta dos rápidos avanços tecnológicos, percebe-se uma mudança nos hábitos e das necessidades das pessoas. Há uma dependência cada vez maior de aparelhos eletrônicos como smartphones, notebooks etc. Construir baterias com grande capacidade energética é um dos desafios atuais para aumentar a autonomia dos aparelhos eletrônicos. Entretanto, uma alternativa que pode ajudar a manter aparelhos eletrônicos por mais tempo longe das tomadas é o compartilhamento de baterias. Existem na literatura muitos estudos envolvendo o compartilhamento de baterias no contexto de veículos elétricos, porém não são encontradas aplicações em smartphones. Um parâmetro importante a ser monitorado neste contexto é o estado de saúde (SoH). Até o momento, não há um consenso na literatura acerca do melhor modelo para estimar o SoH de baterias devido à falta de métodos bem estabelecidos. Assim, o objetivo geral desta dissertação foi construir um modelo para estimar a curva de estado de saúde, por meio do estado de carga, com vistas a estimar a saúde de baterias de íon-lítio. O modelo proposto foi baseado em redes neurais recorrentes. Para treinar e validar o modelo, foi construído um sistema para a realização de ensaios destrutivos, sendo possível estudar o comportamento de baterias de íon-lítio ao longo de toda vida útil. O modelo proposto foi capaz de estimar o SoH das baterias estudadas com boa exatidão, sob diferentes parâmetros de carga/descarga. O diferencial do modelo são baixa complexidade computacional, mesmo envolvendo modelos de redes neurais, e serem adotados parâmetros de entrada de fácil medição. / [en] Because of the fast technological advances, there is a change in people s habits and needs. There is an increasing dependence on electronic devices such as smartphones, notebooks etc. Building batteries with great energy capacity is one of the current challenges to increase the autonomy of electronic devices. However, an alternative that can help keep electronic devices longer away from sockets is battery swap. There are many studies in the literature involving the sharing of batteries in the context of electric vehicles, but no applications are found in smartphones. An important parameter to be monitored in this context is state of health (SoH). To date, there is no consensus in the literature about the best model for estimating battery SoH due to the lack of well-established methods. Thus, the objective of this dissertation is to build a model to estimate the state of health curve, with a view to classifying the health of lithium-ion batteries, through state of charge curve, for applications involving battery swap aiming to use in smartphones. The proposed model was based on recurrent neural networks. To train and validate the model, a system was built to perform destructive tests, being possible to study the behavior of lithium-ion batteries throughout its useful life. The proposed model was able to estimate the SoH of the batteries studied with good precision, under different charge / discharge parameters. The distinction of the model is low computational complexity, even involving neural network models, and easy-to-measure input parameters are adopted.

In this master thesis, we implement a two-step anomaly detection mechanism for non-recurrent traffic congestions with data collected from public transport buses in Stockholm. We investigate the use of machine learning to model time series data with LSTMs and evaluate the results with a baseline prediction model. The anomaly detection algorithm embodies both collective and contextual expressivity, meaning it is capable of findingcollections of delayed buses and also takes the temporality of the data into account. Results show that the anomaly detection performance benefits from the lower prediction errors produced by the LSTM network. The intersection rule significantly decreases the number of false positives while maintaining the true positive rate at a sufficient level. The performance of the anomaly detection algorithm has been found to depend on the road segment it is applied to, some segments have been identified to be particularly hard whereas other have been identified to be easier than others. The performance of the best performing setup of the anomaly detection mechanism had a true positive rate of 84.3 % and a true negative rate of 96.0 %. / I den här masteruppsatsen implementerar vi en tvåstegsalgoritm för avvikelsedetektering för icke återkommande trafikstockningar. Data är insamlad från kollektivtrafikbussarna i Stockholm. Vi undersöker användningen av maskininlärning för att modellerna tidsseriedata med hjälp av LSTM-nätverk och evaluerar sedan dessa resultat med en grundmodell. Avvikelsedetekteringsalgoritmen inkluderar både kollektiv och kontextuell uttrycksfullhet, vilket innebär att kollektiva förseningar kan hittas och att även temporaliteten hos datan beaktas. Resultaten visar att prestandan hos avvikelsedetekteringen förbättras av mindre prediktionsfel genererade av LSTM-nätverket i jämförelse med grundmodellen. En regel för avvikelser baserad på snittet av två andra regler reducerar märkbart antalet falska positiva medan den höll kvar antalet sanna positiva på en tillräckligt hög nivå. Prestandan hos avvikelsedetekteringsalgoritmen har setts bero av vilken vägsträcka den tillämpas på, där några vägsträckor är svårare medan andra är lättare för avvikelsedetekteringen. Den bästa varianten av algoritmen hittade 84.3 % av alla avvikelser och 96.0 % av all avvikelsefri data blev markerad som normal data.