Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment

Since convolutional neural networks (CNNs) achieved top performance on the ImageNet task in 2012, deep learning has become the preferred approach to addressing computer vision, natural language processing, speech recognition and bio-informatics tasks. However, despite impressive performance, neural networks tend to make over-confident predictions. Thus, it is necessary to investigate robust, interpretable and tractable estimates of uncertainty in a model's predictions in order to construct safer Machine Learning systems. This is crucial to applications where the cost of an error is high, such as in autonomous vehicle control, high-stakes automatic proficiency assessment and in the medical, financial and legal fields. In the first part of this thesis uncertainty estimation via ensemble and single-model approaches is discussed in detail and a new class of models for uncertainty estimation, called $\textit{Prior Networks}$, is proposed. Prior Networks are able to $\textit{emulate}$ an ensemble of models using a single deterministic neural network, which allows sources of uncertainty to be determined within the same probabilistic framework as in ensemble-based approaches, but with the computational simplicity and ease of training of single-model approaches. Thus, Prior Networks combine the advantages of ensemble and single-model approaches to estimating uncertainty. In this thesis Prior Networks are evaluated on a range classification datasets, where they are shown to outperform baseline approaches, such as Monte-Carlo dropout, on the task of detecting out-of-distribution inputs. In the second part of this thesis deep learning and uncertainty estimation approaches are applied to the area of automatic assessment of non-native spoken language proficiency. Specifically deep-learning based graders and spoken response relevance assessment systems are constructed using data from the BULATS and LinguaSkill exams, provided by Cambridge English Language Assessment. Baseline approaches for uncertainty estimation discussed and evaluated in the first half of the thesis are then applied to these models and assessed on the task of rejecting predictions to be graded by human examiners and detecting misclassifications

Hizkuntza-ulermenari ekarpenak: N-gramen arteko atentzio eta lerrokatzeak antzekotasun eta inferentzia interpretagarrirako.

148 p.Hizkuntzaren Prozesamenduaren bitartez hezkuntzaren alorreko sistemaadimendunak hobetzea posible da, ikasleen eta irakasleen lan-karganabarmenki arinduz. Tesi honetan esaldi-mailako hizkuntza-ulermena aztertueta proposamen berrien bitartez sistema adimendunen hizkuntza-ulermenaareagotzen dugu, sistemei erabiltzailearen esaldiak modu zehatzagoaninterpretatzeko gaitasuna emanez. Esaldiak modu finean interpretatzekogaitasunak feedbacka modu automatikoan sortzeko aukera ematen baitu.Tesi hau garatzeko hizkuntza-ulermenean sakondu dugu antzekotasunsemantikoari eta inferentzia logikoari dagokien ezaugarriak eta sistemakaztertuz. Bereziki, esaldi barneko hitzak multzotan egituratuz eta lerrokatuzesaldiak hobeto modelatu daitezkeela erakutsi dugu. Horretarako, hitz solteaklerrokatzen dituen aurrekarien egoerako neurona-sare sistema batinplementatu eta n-grama arbitrarioak lerrokatzeko moldaketak egin ditugu.Hitzen arteko lerrokatzea aspalditik ezaguna bada ere, tesi honek, lehen aldiz,n-grama arbitrarioak atentzio-mekanismo baten bitartez lerrokatzekoproposamenak plazaratzen ditu.Gainera, esaldien arteko antzekotasunak eta desberdintasunak moduzehatzean identifikatzeko, esaldien interpretagarritasuna areagotzeko etaikasleei feedback zehatza emateko geruza berri bat sortu dugu: iSTS.Antzekotasun semantikoa eta inferentzia logikoa biltzen dituen geruzahorrekin chunkak lerrokatu ditugu, eta ikasleei feedback zehatza emateko gaiizan garela frogatu dugu hezkuntzaren testuinguruko bi ebaluazioeszenariotan.Tesi honekin batera hainbat sistema eta datu-multzo argitaratu diraetorkizunean komunitate zientifikoak ikertzen jarrai dezan

Measuring Semantic Textual Similarity and Automatic Answer Assessment in Dialogue Based Tutoring Systems

This dissertation presents methods and resources proposed to improve onmeasuring semantic textual similarity and their applications in student responseunderstanding in dialogue based Intelligent Tutoring Systems. In order to predict the extent of similarity between given pair of sentences,we have proposed machine learning models using dozens of features, such as thescores calculated using optimal multi-level alignment, vector based compositionalsemantics, and machine translation evaluation methods. Furthermore, we haveproposed models towards adding an interpretation layer on top of similaritymeasurement systems. Our models on predicting and interpreting the semanticsimilarity have been the top performing systems in SemEval (a premier venue for thesemantic evaluation) for the last three years. The correlations between our models\u27predictions and the human judgments were above 0.80 for several datasets while ourmodels being very robust than many other top performing systems. Moreover, wehave proposed Bayesian. We have also proposed a novel Neural Network based word representationmapping approach which allows us to map the vector based representation of a wordfound in one model to the another model where the word representation is missing,effectively pooling together the vocabularies and corresponding representationsacross models. Our experiments show that the model coverage increased by few toseveral times depending on which model\u27s vocabulary is taken as a reference. Also,the transformed representations were well correlated to the native target modelvectors showing that the mapped representations can be used with condence tosubstitute the missing word representations in the target model. models to adapt similarity models across domains. Furthermore, we have proposed methods to improve open-ended answersassessment in dialogue based tutoring systems which is very challenging because ofthe variations in student answers which often are not self contained and need thecontextual information (e.g., dialogue history) in order to better assess theircorrectness. In that, we have proposed Probabilistic Soft Logic (PSL) modelsaugmenting semantic similarity information with other knowledge. To detect intra- and inter-sentential negation scope and focus in tutorialdialogs, we have developed Conditional Random Fields (CRF) models. The resultsindicate that our approach is very effective in detecting negation scope and focus intutorial dialogue context and can be further developed to augment the naturallanguage understanding systems. Additionally, we created resources (datasets, models, and tools) for fosteringresearch in semantic similarity and student response understanding inconversational tutoring systems

The Palgrave Handbook of Digital Russia Studies

This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today

The Palgrave Handbook of Digital Russia Studies

This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today

Digital writing technologies in higher education : theory, research, and practice

This open access book serves as a comprehensive guide to digital writing technology, featuring contributions from over 20 renowned researchers from various disciplines around the world. The book is designed to provide a state-of-the-art synthesis of the developments in digital writing in higher education, making it an essential resource for anyone interested in this rapidly evolving field. In the first part of the book, the authors offer an overview of the impact that digitalization has had on writing, covering more than 25 key technological innovations and their implications for writing practices and pedagogical uses. Drawing on these chapters, the second part of the book explores the theoretical underpinnings of digital writing technology such as writing and learning, writing quality, formulation support, writing and thinking, and writing processes. The authors provide insightful analysis on the impact of these developments and offer valuable insights into the future of writing. Overall, this book provides a cohesive and consistent theoretical view of the new realities of digital writing, complementing existing literature on the digitalization of writing. It is an essential resource for scholars, educators, and practitioners interested in the intersection of technology and writing