Accurate and budget-efficient text, image, and video analysis systems powered by the crowd

Crowdsourcing systems empower individuals and companies to outsource labor-intensive tasks that cannot currently be solved by automated methods and are expensive to tackle by domain experts. Crowdsourcing platforms are traditionally used to provide training labels for supervised machine learning algorithms. Crowdsourced tasks are distributed among internet workers who typically have a range of skills and knowledge, differing previous exposure to the task at hand, and biases that may influence their work. This inhomogeneity of the workforce makes the design of accurate and efficient crowdsourcing systems challenging. This dissertation presents solutions to improve existing crowdsourcing systems in terms of accuracy and efficiency. It explores crowdsourcing tasks in two application areas, political discourse and annotation of biomedical and everyday images. The first part of the dissertation investigates how workers' behavioral factors and their unfamiliarity with data can be leveraged by crowdsourcing systems to control quality. Through studies that involve familiar and unfamiliar image content, the thesis demonstrates the benefit of explicitly accounting for a worker's familiarity with the data when designing annotation systems powered by the crowd. The thesis next presents Crowd-O-Meter, a system that automatically predicts the vulnerability of crowd workers to believe \enquote{fake news} in text and video. The second part of the dissertation explores the reversed relationship between machine learning and crowdsourcing by incorporating machine learning techniques for quality control of crowdsourced end products. In particular, it investigates if machine learning can be used to improve the quality of crowdsourced results and also consider budget constraints. The thesis proposes an image analysis system called ICORD that utilizes behavioral cues of the crowd worker, augmented by automated evaluation of image features, to infer the quality of a worker-drawn outline of a cell in a microscope image dynamically. ICORD determines the need to seek additional annotations from other workers in a budget-efficient manner. Next, the thesis proposes a budget-efficient machine learning system that uses fewer workers to analyze easy-to-label data and more workers for data that require extra scrutiny. The system learns a mapping from data features to number of allocated crowd workers for two case studies, sentiment analysis of twitter messages and segmentation of biomedical images. Finally, the thesis uncovers the potential for design of hybrid crowd-algorithm methods by describing an interactive system for cell tracking in time-lapse microscopy videos, based on a prediction model that determines when automated cell tracking algorithms fail and human interaction is needed to ensure accurate tracking

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection

Advanced Deep Learning Methods for Enhancing Information Compliance Checking

The study in this thesis enhances information checking algorithm challenges, such as CAD drawings comliance checking which is time-consuming and error-prone, by focusing on the development and refinement of advanced deep learning algorithms, primarily in the Natural Language Processing (NLP) sphere, as innovative methods for higher accuracy and time-saving solution

Graph Neural Networks for Natural Language Processing: A Survey

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa rich variety of NLP problems that can be best expressed with a graph structure. As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. In this survey, we present a comprehensive overview onGraph Neural Networks(GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder models. We further introducea large number of NLP applications that are exploiting the power of GNNs and summarize thecorresponding benchmark datasets, evaluation metrics, and open-source codes. Finally, we discussvarious outstanding challenges for making the full use of GNNs for NLP as well as future researchdirections. To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.Comment: 127 page

Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey

Several adaptations of Transformers models have been developed in various domains since its breakthrough in Natural Language Processing (NLP). This trend has spread into the field of Music Information Retrieval (MIR), including studies processing music data. However, the practice of leveraging NLP tools for symbolic music data is not novel in MIR. Music has been frequently compared to language, as they share several similarities, including sequential representations of text and music. These analogies are also reflected through similar tasks in MIR and NLP. This survey reviews NLP methods applied to symbolic music generation and information retrieval studies following two axes. We first propose an overview of representations of symbolic music adapted from natural language sequential representations. Such representations are designed by considering the specificities of symbolic music. These representations are then processed by models. Such models, possibly originally developed for text and adapted for symbolic music, are trained on various tasks. We describe these models, in particular deep learning models, through different prisms, highlighting music-specialized mechanisms. We finally present a discussion surrounding the effective use of NLP tools for symbolic music data. This includes technical issues regarding NLP methods and fundamental differences between text and music, which may open several doors for further research into more effectively adapting NLP tools to symbolic MIR.Comment: 36 pages, 5 figures, 4 table

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this paper is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining two key principles of modality heterogeneity and interconnections that have driven subsequent innovations, and propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy

Healthcare data heterogeneity and its contribution to machine learning performance

Tesis por compendio[EN] The data quality assessment has many dimensions, from those so obvious as the data completeness and consistency to other less evident such as the correctness or the ability to represent the target population. In general, it is possible to classify them as those produced by an external effect, and those that are inherent in the data itself. This work will be focused on those inherent to data, such as the temporal and the multisource variability applied to healthcare data repositories. Every process is usually improved over time, and that has a direct impact on the data distribution. Similarly, how a process is executed in different sources may vary due to many factors, such as the diverse interpretation of standard protocols by human beings or different previous experiences of experts. Artificial Intelligence has become one of the most widely extended technological paradigms in almost all the scientific and industrial fields. Advances not only in models but also in hardware have led to their use in almost all areas of science. Although the solved problems using this technology often have the drawback of not being interpretable, or at least not as much as other classical mathematical or statistical techniques. This motivated the emergence of the "explainable artificial intelligence" concept, that study methods to quantify and visualize the training process of models based on machine learning. On the other hand, real systems may often be represented by large networks (graphs), and one of the most relevant features in such networks is the community or clustering structure. Since sociology, biology, or clinical situations could usually be modeled using graphs, community detection algorithms are becoming more and more extended in a biomedical field. In the present doctoral thesis, contributions have been made in the three above mentioned areas. On the one hand, temporal and multisource variability assessment methods based on information geometry were used to detect variability in data distribution that may hinder data reuse and, hence, the conclusions which can be extracted from them. This methodology's usability was proved by a temporal variability analysis to detect data anomalies in the electronic health records of a hospital over 7 years. Besides, it showed that this methodology could have a positive impact if it applied previously to any study. To this end, firstly, we extracted the variables that highest influenced the intensity of headache in migraine patients using machine learning techniques. One of the principal characteristics of machine learning algorithms is its capability of fitting the training set. In those datasets with a small number of observations, the model can be biased by the training sample. The observed variability, after the application of the mentioned methodology and considering as sources the registries of migraine patients with different headache intensity, served as evidence for the truthfulness of the extracted features. Secondly, such an approach was applied to measure the variability among the gray-level histograms of digital mammographies. We demonstrated that the acquisition device produced the observed variability, and after defining an image preprocessing step, the performance of a deep learning model, which modeled a marker of breast cancer risk estimation, increased. Given a dataset containing the answers to a survey formed by psychometric scales, or in other words, questionnaires to measure psychologic factors, such as depression, cope, etcetera, two deep learning architectures that used the data structure were defined. Firstly, we designed a deep learning architecture using the conceptual structure of such psychometric scales. This architecture was trained to model the happiness degree of the participants, improved the performance compared to classical statistical approaches. A second architecture, automatically designed using community detection in graphs, was not only a contribution[ES] El análisis de la calidad de los datos abarca muchas dimensiones, desde aquellas tan obvias como la completitud y la coherencia, hasta otras menos evidentes como la correctitud o la capacidad de representar a la población objetivo. En general, es posible clasificar estas dimensiones como las producidas por un efecto externo y las que son inherentes a los propios datos. Este trabajo se centrará en la evaluación de aquellas inherentes a los datos en repositorios de datos sanitarios, como son la variabilidad temporal y multi-fuente. Los procesos suelen evolucionar con el tiempo, y esto tiene un impacto directo en la distribución de los datos. Análogamente, la subjetividad humana puede influir en la forma en la que un mismo proceso, se ejecuta en diferentes fuentes de datos, influyendo en su cuantificación o recogida. La inteligencia artificial se ha convertido en uno de los paradigmas tecnológicos más extendidos en casi todos los campos científicos e industriales. Los avances, no sólo en los modelos sino también en el hardware, han llevado a su uso en casi todas las áreas de la ciencia. Es cierto que, los problemas resueltos mediante esta tecnología, suelen tener el inconveniente de no ser interpretables, o al menos, no tanto como otras técnicas de matemáticas o de estadística clásica. Esta falta de interpretabilidad, motivó la aparición del concepto de "inteligencia artificial explicable", que estudia métodos para cuantificar y visualizar el proceso de entrenamiento de modelos basados en aprendizaje automático. Por otra parte, los sistemas reales pueden representarse a menudo mediante grandes redes (grafos), y una de las características más relevantes de esas redes, es la estructura de comunidades. Dado que la sociología, la biología o las situaciones clínicas, usualmente pueden modelarse mediante grafos, los algoritmos de detección de comunidades se están extendiendo cada vez más en el ámbito biomédico. En la presente tesis doctoral, se han hecho contribuciones en los tres campos anteriormente mencionados. Por una parte, se han utilizado métodos de evaluación de variabilidad temporal y multi-fuente, basados en geometría de la información, para detectar la variabilidad en la distribución de los datos que pueda dificultar la reutilización de los mismos y, por tanto, las conclusiones que se puedan extraer. Esta metodología demostró ser útil tras ser aplicada a los registros electrónicos sanitarios de un hospital a lo largo de 7 años, donde se detectaron varias anomalías. Además, se demostró el impacto positivo que este análisis podría añadir a cualquier estudio. Para ello, en primer lugar, se utilizaron técnicas de aprendizaje automático para extraer las características más relevantes, a la hora de clasificar la intensidad del dolor de cabeza en pacientes con migraña. Una de las propiedades de los algoritmos de aprendizaje automático es su capacidad de adaptación a los datos de entrenamiento, en bases de datos en los que el número de observaciones es pequeño, el estimador puede estar sesgado por la muestra de entrenamiento. La variabilidad observada, tras la utilización de la metodología y considerando como fuentes, los registros de los pacientes con diferente intensidad del dolor, sirvió como evidencia de la veracidad de las características extraídas. En segundo lugar, se aplicó para medir la variabilidad entre los histogramas de los niveles de gris de mamografías digitales. Se demostró que esta variabilidad estaba producida por el dispositivo de adquisición, y tras la definición de un preproceso de imagen, se mejoró el rendimiento de un modelo de aprendizaje profundo, capaz de estimar un marcador de imagen del riesgo de desarrollar cáncer de mama. Dada una base de datos que recogía las respuestas de una encuesta formada por escalas psicométricas, o lo que es lo mismo cuestionarios que sirven para medir un factor psicológico, tales como depresión, resiliencia, etc., se definieron nuevas arquitecturas de aprendizaje profundo utilizando la estructura de los datos. En primer lugar, se dise˜no una arquitectura, utilizando la estructura conceptual de las citadas escalas psicom´etricas. Dicha arquitectura, que trataba de modelar el grado de felicidad de los participantes, tras ser entrenada, mejor o la precisión en comparación con otros modelos basados en estadística clásica. Una segunda aproximación, en la que la arquitectura se diseño de manera automática empleando detección de comunidades en grafos, no solo fue una contribución de por sí por la automatización del proceso, sino que, además, obtuvo resultados comparables a su predecesora.[CA] L'anàlisi de la qualitat de les dades comprén moltes dimensions, des d'aquelles tan òbvies com la completesa i la coherència, fins a altres menys evidents com la correctitud o la capacitat de representar a la població objectiu. En general, és possible classificar estes dimensions com les produïdes per un efecte extern i les que són inherents a les pròpies dades. Este treball se centrarà en l'avaluació d'aquelles inherents a les dades en reposadors de dades sanitaris, com són la variabilitat temporal i multi-font. Els processos solen evolucionar amb el temps i açò té un impacte directe en la distribució de les dades. Anàlogament, la subjectivitat humana pot influir en la forma en què un mateix procés, s'executa en diferents fonts de dades, influint en la seua quantificació o arreplega. La intel·ligència artificial s'ha convertit en un dels paradigmes tecnològics més estesos en quasi tots els camps científics i industrials. Els avanços, no sols en els models sinó també en el maquinari, han portat al seu ús en quasi totes les àrees de la ciència. És cert que els problemes resolts per mitjà d'esta tecnologia, solen tindre l'inconvenient de no ser interpretables, o almenys, no tant com altres tècniques de matemàtiques o d'estadística clàssica. Esta falta d'interpretabilitat, va motivar l'aparició del concepte de "inteligencia artificial explicable", que estudia mètodes per a quantificar i visualitzar el procés d'entrenament de models basats en aprenentatge automàtic. D'altra banda, els sistemes reals poden representar-se sovint per mitjà de grans xarxes (grafs) i una de les característiques més rellevants d'eixes xarxes, és l'estructura de comunitats. Atés que la sociologia, la biologia o les situacions clíniques, poden modelar-se usualment per mitjà de grafs, els algoritmes de detecció de comunitats s'estan estenent cada vegada més en l'àmbit biomèdic. En la present tesi doctoral, s'han fet contribucions en els tres camps anteriorment mencionats. D'una banda, s'han utilitzat mètodes d'avaluació de variabilitat temporal i multi-font, basats en geometria de la informació, per a detectar la variabilitat en la distribució de les dades que puga dificultar la reutilització dels mateixos i, per tant, les conclusions que es puguen extraure. Esta metodologia va demostrar ser útil després de ser aplicada als registres electrònics sanitaris d'un hospital al llarg de 7 anys, on es van detectar diverses anomalies. A més, es va demostrar l'impacte positiu que esta anàlisi podria afegir a qualsevol estudi. Per a això, en primer lloc, es van utilitzar tècniques d'aprenentatge automàtic per a extraure les característiques més rellevants, a l'hora de classificar la intensitat del mal de cap en pacients amb migranya. Una de les propietats dels algoritmes d'aprenentatge automàtic és la seua capacitat d'adaptació a les dades d'entrenament, en bases de dades en què el nombre d'observacions és xicotet, l'estimador pot estar esbiaixat per la mostra d'entrenament. La variabilitat observada després de la utilització de la metodologia, i considerant com a fonts els registres dels pacients amb diferent intensitat del dolor, va servir com a evidència de la veracitat de les característiques extretes. En segon lloc, es va aplicar per a mesurar la variabilitat entre els histogrames dels nivells de gris de mamografies digitals. Es va demostrar que esta variabilitat estava produïda pel dispositiu d'adquisició i després de la definició d'un preprocés d'imatge, es va millorar el rendiment d'un model d'aprenentatge profund, capaç d'estimar un marcador d'imatge del risc de desenrotllar càncer de mama. Donada una base de dades que arreplegava les respostes d'una enquesta formada per escales psicomètriques, o el que és el mateix qüestionaris que servixen per a mesurar un factor psicològic, com ara depressió, resiliència, etc., es van definir noves arquitectures d'aprenentatge profund utilitzant l’estructura de les dades. En primer lloc, es disseny`a una arquitectura, utilitzant l’estructura conceptual de les esmentades escales psicom`etriques. La dita arquitectura, que tractava de modelar el grau de felicitat dels participants, despr´es de ser entrenada, va millorar la precisió en comparació amb altres models basats en estad´ıstica cl`assica. Una segona aproximació, en la que l’arquitectura es va dissenyar de manera autoàtica emprant detecció de comunitats en grafs, no sols va ser una contribució de per si per l’automatització del procés, sinó que, a més, va obtindre resultats comparables a la seua predecessora.También me gustaría mencionar al Instituto Tecnológico de la Informáica, en especial al grupo de investigación Percepción, Reconocimiento, Aprendizaje e Inteligencia Artificial, no solo por darme la oportunidad de seguir creciendo en el mundo de la ciencia, sino también, por apoyarme en la consecución de mis objetivos personalesPérez Benito, FJ. (2020). Healthcare data heterogeneity and its contribution to machine learning performance [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/154414TESISCompendi