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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence
Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes—particularly, Multi-Instance Learning and classical Machine Learning formulations—to model student behaviour. Besides, Explainable Artificial Intelligence is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2,500 submissions from roughly 90 different students from a programming-related course in a Computer Science degree. The results obtained validate the proposal: the model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioural pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.This work has been partially funded by the “Programa Redes-I3CE de investigacion en docencia universitaria del Instituto de Ciencias de la Educacion (REDES-I3CE-2020-5069)” of the University of Alicante. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+I de la Generalitat Valenciana”
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?
The use of pretrained embeddings has become widespread in modern e-commerce
machine learning (ML) systems. In practice, however, we have encountered
several key issues when using pretrained embedding in a real-world production
system, many of which cannot be fully explained by current knowledge.
Unfortunately, we find that there is a lack of a thorough understanding of how
pre-trained embeddings work, especially their intrinsic properties and
interactions with downstream tasks. Consequently, it becomes challenging to
make interactive and scalable decisions regarding the use of pre-trained
embeddings in practice.
Our investigation leads to two significant discoveries about using pretrained
embeddings in e-commerce applications. Firstly, we find that the design of the
pretraining and downstream models, particularly how they encode and decode
information via embedding vectors, can have a profound impact. Secondly, we
establish a principled perspective of pre-trained embeddings via the lens of
kernel analysis, which can be used to evaluate their predictability,
interactively and scalably. These findings help to address the practical
challenges we faced and offer valuable guidance for successful adoption of
pretrained embeddings in real-world production. Our conclusions are backed by
solid theoretical reasoning, benchmark experiments, as well as online testings
Model Diagnostics meets Forecast Evaluation: Goodness-of-Fit, Calibration, and Related Topics
Principled forecast evaluation and model diagnostics are vital in fitting probabilistic models and forecasting outcomes of interest. A common principle is that fitted or predicted distributions ought to be calibrated, ideally in the sense that the outcome is indistinguishable from a random draw from the posited distribution. Much of this thesis is centered on calibration properties of various types of forecasts.
In the first part of the thesis, a simple algorithm for exact multinomial goodness-of-fit tests is proposed. The algorithm computes exact -values based on various test statistics, such as the log-likelihood ratio and Pearson\u27s chi-square. A thorough analysis shows improvement on extant methods. However, the runtime of the algorithm grows exponentially in the number of categories and hence its use is limited.
In the second part, a framework rooted in probability theory is developed, which gives rise to hierarchies of calibration, and applies to both predictive distributions and stand-alone point forecasts. Based on a general notion of conditional T-calibration, the thesis introduces population versions of T-reliability diagrams and revisits a score decomposition into measures of miscalibration, discrimination, and uncertainty. Stable and efficient estimators of T-reliability diagrams and score components arise via nonparametric isotonic regression and the pool-adjacent-violators algorithm. For in-sample model diagnostics, a universal coefficient of determination is introduced that nests and reinterprets the classical in least squares regression.
In the third part, probabilistic top lists are proposed as a novel type of prediction in classification, which bridges the gap between single-class predictions and predictive distributions. The probabilistic top list functional is elicited by strictly consistent evaluation metrics, based on symmetric proper scoring rules, which admit comparison of various types of predictions
Learning disentangled speech representations
A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody.
The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions.
In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks.
This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically
Estudo da remodelagem reversa miocárdica através da análise proteómica do miocárdio e do líquido pericárdico
Valve replacement remains as the standard therapeutic option for aortic
stenosis patients, aiming at abolishing pressure overload and triggering
myocardial reverse remodeling. However, despite the instant hemodynamic
benefit, not all patients show complete regression of myocardial hypertrophy,
being at higher risk for adverse outcomes, such as heart failure. The current
comprehension of the biological mechanisms underlying an incomplete reverse
remodeling is far from complete. Furthermore, definitive prognostic tools and
ancillary therapies to improve the outcome of the patients undergoing valve
replacement are missing. To help abridge these gaps, a combined myocardial
(phospho)proteomics and pericardial fluid proteomics approach was followed,
taking advantage of human biopsies and pericardial fluid collected during
surgery and whose origin anticipated a wealth of molecular information
contained therein.
From over 1800 and 750 proteins identified, respectively, in the myocardium
and in the pericardial fluid of aortic stenosis patients, a total of 90 dysregulated
proteins were detected. Gene annotation and pathway enrichment analyses,
together with discriminant analysis, are compatible with a scenario of increased
pro-hypertrophic gene expression and protein synthesis, defective ubiquitinproteasome system activity, proclivity to cell death (potentially fed by
complement activity and other extrinsic factors, such as death receptor
activators), acute-phase response, immune system activation and fibrosis.
Specific validation of some targets through immunoblot techniques and
correlation with clinical data pointed to complement C3 β chain, Muscle Ring
Finger protein 1 (MuRF1) and the dual-specificity Tyr-phosphorylation
regulated kinase 1A (DYRK1A) as potential markers of an incomplete
response. In addition, kinase prediction from phosphoproteome data suggests
that the modulation of casein kinase 2, the family of IκB kinases, glycogen
synthase kinase 3 and DYRK1A may help improve the outcome of patients
undergoing valve replacement. Particularly, functional studies with DYRK1A+/-
cardiomyocytes show that this kinase may be an important target to treat
cardiac dysfunction, provided that mutant cells presented a different response
to stretch and reduced ability to develop force (active tension).
This study opens many avenues in post-aortic valve replacement reverse
remodeling research. In the future, gain-of-function and/or loss-of-function
studies with isolated cardiomyocytes or with animal models of aortic bandingdebanding will help disclose the efficacy of targeting the surrogate therapeutic
targets. Besides, clinical studies in larger cohorts will bring definitive proof of
complement C3, MuRF1 and DYRK1A prognostic value.A substituição da válvula aórtica continua a ser a opção terapêutica de
referência para doentes com estenose aórtica e visa a eliminação da
sobrecarga de pressão, desencadeando a remodelagem reversa miocárdica.
Contudo, apesar do benefício hemodinâmico imediato, nem todos os pacientes
apresentam regressão completa da hipertrofia do miocárdio, ficando com maior
risco de eventos adversos, como a insuficiência cardíaca. Atualmente, os
mecanismos biológicos subjacentes a uma remodelagem reversa incompleta
ainda não são claros. Além disso, não dispomos de ferramentas de
prognóstico definitivos nem de terapias auxiliares para melhorar a condição
dos pacientes indicados para substituição da válvula. Para ajudar a resolver
estas lacunas, uma abordagem combinada de (fosfo)proteómica e proteómica
para a caracterização, respetivamente, do miocárdio e do líquido pericárdico
foi seguida, tomando partido de biópsias e líquidos pericárdicos recolhidos em
ambiente cirúrgico.
Das mais de 1800 e 750 proteínas identificadas, respetivamente, no miocárdio
e no líquido pericárdico dos pacientes com estenose aórtica, um total de 90
proteínas desreguladas foram detetadas. As análises de anotação de genes,
de enriquecimento de vias celulares e discriminativa corroboram um cenário de
aumento da expressão de genes pro-hipertróficos e de síntese proteica, um
sistema ubiquitina-proteassoma ineficiente, uma tendência para morte celular
(potencialmente acelerada pela atividade do complemento e por outros fatores
extrínsecos que ativam death receptors), com ativação da resposta de fase
aguda e do sistema imune, assim como da fibrose.
A validação de alguns alvos específicos através de immunoblot e correlação
com dados clínicos apontou para a cadeia β do complemento C3, a Muscle
Ring Finger protein 1 (MuRF1) e a dual-specificity Tyr-phosphoylation
regulated kinase 1A (DYRK1A) como potenciais marcadores de uma resposta
incompleta. Por outro lado, a predição de cinases a partir do fosfoproteoma,
sugere que a modulação da caseína cinase 2, a família de cinases do IκB, a
glicogénio sintase cinase 3 e da DYRK1A pode ajudar a melhorar a condição
dos pacientes indicados para intervenção. Em particular, a avaliação funcional
de cardiomiócitos DYRK1A+/- mostraram que esta cinase pode ser um alvo
importante para tratar a disfunção cardíaca, uma vez que os miócitos mutantes
responderam de forma diferente ao estiramento e mostraram uma menor
capacidade para desenvolver força (tensão ativa).
Este estudo levanta várias hipóteses na investigação da remodelagem reversa.
No futuro, estudos de ganho e/ou perda de função realizados em
cardiomiócitos isolados ou em modelos animais de banding-debanding da
aorta ajudarão a testar a eficácia de modular os potenciais alvos terapêuticos
encontrados. Além disso, estudos clínicos em coortes de maior dimensão
trarão conclusões definitivas quanto ao valor de prognóstico do complemento
C3, MuRF1 e DYRK1A.Programa Doutoral em Biomedicin
Modeling Uncertainty for Reliable Probabilistic Modeling in Deep Learning and Beyond
[ES] Esta tesis se enmarca en la intersección entre las técnicas modernas de Machine Learning, como las Redes Neuronales Profundas, y el modelado probabilístico confiable. En muchas aplicaciones, no solo nos importa la predicción hecha por un modelo (por ejemplo esta imagen de pulmón presenta cáncer) sino también la confianza que tiene el modelo para hacer esta predicción (por ejemplo esta imagen de pulmón presenta cáncer con 67% probabilidad). En tales aplicaciones, el modelo ayuda al tomador de decisiones (en este caso un médico) a tomar la decisión final. Como consecuencia, es necesario que las probabilidades proporcionadas por un modelo reflejen las proporciones reales presentes en el conjunto al que se ha asignado dichas probabilidades; de lo contrario, el modelo es inútil en la práctica. Cuando esto sucede, decimos que un modelo está perfectamente calibrado.
En esta tesis se exploran tres vias para proveer modelos más calibrados. Primero se muestra como calibrar modelos de manera implicita, que son descalibrados por técnicas de aumentación de datos. Se introduce una función de coste que resuelve esta descalibración tomando como partida las ideas derivadas de la toma de decisiones con la regla de Bayes. Segundo, se muestra como calibrar modelos utilizando una etapa de post calibración implementada con una red neuronal Bayesiana. Finalmente, y en base a las limitaciones estudiadas en la red neuronal Bayesiana, que hipotetizamos que se basan en un prior mispecificado, se introduce un nuevo proceso estocástico que sirve como distribución a priori en un problema de inferencia Bayesiana.[CA] Aquesta tesi s'emmarca en la intersecció entre les tècniques modernes de Machine Learning, com ara les Xarxes Neuronals Profundes, i el modelatge probabilístic fiable. En moltes aplicacions, no només ens importa la predicció feta per un model (per ejemplem aquesta imatge de pulmó presenta càncer) sinó també la confiança que té el model per fer aquesta predicció (per exemple aquesta imatge de pulmó presenta càncer amb 67% probabilitat). En aquestes aplicacions, el model ajuda el prenedor de decisions (en aquest cas un metge) a prendre la decisió final. Com a conseqüència, cal que les probabilitats proporcionades per un model reflecteixin les proporcions reals presents en el conjunt a què s'han assignat aquestes probabilitats; altrament, el model és inútil a la pràctica. Quan això passa, diem que un model està perfectament calibrat.
En aquesta tesi s'exploren tres vies per proveir models més calibrats. Primer es mostra com calibrar models de manera implícita, que són descalibrats per tècniques d'augmentació de dades. S'introdueix una funció de cost que resol aquesta descalibració prenent com a partida les idees derivades de la presa de decisions amb la regla de Bayes. Segon, es mostra com calibrar models utilitzant una etapa de post calibratge implementada amb una xarxa neuronal Bayesiana. Finalment, i segons les limitacions estudiades a la xarxa neuronal Bayesiana, que es basen en un prior mispecificat, s'introdueix un nou procés estocàstic que serveix com a distribució a priori en un problema d'inferència Bayesiana.[EN] This thesis is framed at the intersection between modern Machine Learning techniques, such as Deep Neural Networks, and reliable probabilistic modeling. In many machine learning applications, we do not only care about the prediction made by a model (e.g. this lung image presents cancer) but also in how confident is the model in making this prediction (e.g. this lung image presents cancer with 67% probability). In such applications, the model assists the decision-maker (in this case a doctor) towards making the final decision. As a consequence, one needs that the probabilities provided by a model reflects the true underlying set of outcomes, otherwise the model is useless in practice. When this happens, we say that a model is perfectly calibrated.
In this thesis three ways are explored to provide more calibrated models. First, it is shown how to calibrate models implicitly, which are decalibrated by data augmentation techniques. A cost function is introduced that solves this decalibration taking as a starting point the ideas derived from decision making with Bayes' rule. Second, it shows how to calibrate models using a post-calibration stage implemented with a Bayesian neural network. Finally, and based on the limitations studied in the Bayesian neural network, which we hypothesize that came from a mispecified prior, a new stochastic process is introduced that serves as a priori distribution in a Bayesian inference problem.Maroñas Molano, J. (2022). Modeling Uncertainty for Reliable Probabilistic Modeling in Deep Learning and Beyond [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181582TESI
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