Text generation for small data regimes

In Natural Language Processing (NLP), applications trained on downstream tasks for text classification usually require enormous amounts of data to perform well. Neural Network (NN) models are among the applications that can always be trained to produce better results. Yet, a huge factor in improving results is the ability to scale over large datasets. Given that Deep NNs are known to be data hungry, having more training samples can always be beneficial. For a classification model to perform well, it could require thousands or even millions of textual training examples. Transfer learning enables us to leverage knowledge gained from general data collections to perform well on target tasks. In NLP, training language models on large data collections has been shown to achieve great results when tuned to different task-specific datasets Wang et al. (2019, 2018a). However, even with transfer learning, adequate training data remains a condition for training machine learning models. Nonetheless, we show that small textual datasets can be augmented to a degree that is enough to achieve improved classification performance. In this thesis, we make multiple contributions to data augmentation. Firstly, we transform the data generation task into an optimization problem which maximizes the usefulness of the generated output, using Monte Carlo Tree Search (MCTS) as the optimization strategy and incorporating entropy as one of the optimization criteria. Secondly, we propose a language generation approach for targeted data generation with the participation of the training classifier. With a user in the loop, we find that manual annotation of a small proportion of the generated data is enough to boost classification performance. Thirdly, under a self-learning scheme, we replace the user by an automated approach in which the classifier is trained on its own pseudo-labels. Finally, we extend the data generation approach to the knowledge distillation domain, by generating samples that a teacher model can confidently label, but not its student

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

AN ENACTIVE APPROACH TO TECHNOLOGICALLY MEDIATED LEARNING THROUGH PLAY

This thesis investigated the application of enactive principles to the design of classroom technolo- gies for young children’s learning through play. This study identified the attributes of an enactive pedagogy, in order to develop a design framework to accommodate enactive learning processes. From an enactive perspective, the learner is defined as an autonomous agent, capable of adapta- tion via the recursive consumption of self generated meaning within the constraints of a social and material world. Adaptation is the parallel development of mind and body that occurs through inter- action, which renders knowledge contingent on the environment from which it emerged. Parallel development means that action and perception in learning are as critical as thinking. An enactive approach to design therefore aspires to make the physical and social interaction with technology meaningful to the learning objective, rather than an aside to cognitive tasks. The design framework considered in detail the necessary affordances in terms of interaction, activity and context. In a further interpretation of enactive principles, this thesis recognised play and pretence as vehicles for designing and evaluating enactive learning and the embodied use of technology. In answering the research question, the interpreted framework was applied as a novel approach to designing and analysing children’s engagement with technology for learning, and worked towards a paradigm where interaction is part of the learning experience. The aspiration for the framework was to inform the design of interaction modalities to allow users’ to exercise the inherent mechanisms they have for making sense of the world. However, before making the claim to support enactive learning processes, there was a question as to whether technologically mediated realities were suitable environments to apply this framework. Given the emphasis on the physical world and action, it was the intention of the research and design activities to explore whether digital artefacts and spaces were an impoverished reality for enactive learning; or if digital objects and spaces could afford sufficient ’reality’ to be referents in social play behaviours. The project embedded in this research was tasked with creating deployable technologies that could be used in the classroom. Consequently, this framework was applied in practice, whereby the design practice and deployed technologies served as pragmatic tools to investigate the potential for interactive technologies in children’s physical, social and cognitive learning. To understand the context, underpin the design framework, and evaluate the impact of any techno- logical interventions in school life, the design practice was informed by ethnographic methodologies. The design process responded to cascading findings from phased research activities. The initial fieldwork located meaning making activities within the classroom, with a view to to re-appropriating situated and familiar practices. In the next stage of the design practice, this formative analysis determined the objectives of the participatory sessions, which in turn contributed to the creation of technologies suitable for an inquiry of enactive learning. The final technologies used standard school equipment with bespoke software, enabling children to engage with real time compositing and tracking applications installed in the classrooms’ role play spaces. The evaluation of the play space technologies in the wild revealed under certain conditions, there was evidence of embodied presence in the children’s social, physical and affective behaviour - illustrating how mediated realities can extend physical spaces. These findings suggest that the attention to meaningful interaction, a presence in the environment as a result of an active role, and a social presence - as outlined in the design framework - can lead to the emergence of observable enactive learning processes. As the design framework was applied, these principles could be examined and revised. Two notable examples of revisions to the design framework, in light of the applied practice, related to: (1) a key affordance for meaningful action to emerge required opportunities for direct and immediate engagement; and (2) a situated awareness of the self and other inhabitants in the mediated space required support across the spectrum of social interaction. The application of the design framework enabled this investigation to move beyond a theoretical discourse

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available

A task-and-technique centered survey on visual analytics for deep learning model engineering

Although deep neural networks have achieved state-of-the-art performance in several artificial intelligence applications in the past decade, they are still hard to understand. In particular, the features learned by deep networks when determining whether a given input belongs to a specific class are only implicitly described concerning a considerable number of internal model parameters. This makes it harder to construct interpretable hypotheses of what the network is learning and how it is learning both of which are essential when designing and improving a deep model to tackle a particular learning task. This challenge can be addressed by the use of visualization tools that allow machine learning experts to explore which components of a network are learning useful features for a pattern recognition task, and also to identify characteristics of the network that can be changed to improve its performance. We present a review of modern approaches aiming to use visual analytics and information visualization techniques to understand, interpret, and fine-tune deep learning models. For this, we propose a taxonomy of such approaches based on whether they provide tools for visualizing a network's architecture, to facilitate the interpretation and analysis of the training process, or to allow for feature understanding. Next, we detail how these approaches tackle the tasks above for three common deep architectures: deep feedforward networks, convolutional neural networks, and recurrent neural networks. Additionally, we discuss the challenges faced by each network architecture and outline promising topics for future research in visualization techniques for deep learning models. (C) 2018 Elsevier Ltd. All rights reserved.</p

Deep neural networks and data augmentationfor semantic labelling in a dialogue corpus

El presente proyecto estudia y aplica técnicas de Deep Neural Networks y Data Augmentation para el etiquetado semántico en un corpus de diálogo, todo ello en el ámbito del Sentiment Analysis. El objetivo principal es abordar un problema de clasificación de temas utilizando arquitecturas basadas tanto en Convolutional Neural Networks (CNN) como en Recurrent Neural Networks (RNN). Cabe resaltar la comparación del rendimiento de cada modelo proporcionada por el proyecto. Como parte del proyecto se han desarrollado igualmente las herramientas de optimización de hiperparámetros necesarias para obtener unos resultados satisfactorios. Todo ello para clasificar los datos del conjunto de datos del proyecto Europeo EMPHATIC. Más información sobre el proyecto EMPHATIC en www.empathic-project.eu. La memoria del proyecto está realizada en Inglés.Sentiment analysis, also known as opinion mining, refers to the use of Natural LanguageProcessing (NLP), among other techniques, in order to extract and analyze subjective in-formation from text, such as emotions or the topic of a text. These techniques are normallyapplied to reviews or data from social media but, in this project, we will apply these tech-niques to the analysis of coaching dialogues involving senior adults. These dialogues havebeen collected as part of the EMPATHIC project.EMPATHIC is an European project whose goal is to implement a virtual agent designedto help elderly to live a healthy and independent life as they age [1][2]. Within this imple-mentation, a Natural-language Understanding (NLU) component plays the role of clas-sifying the utterance (spoken words) of the user into semantic components. This is amachine learning classification problem where there are multiple classes and a model hasto be taught to classify the text into these classes.Currently, the NLU model implementation is based on seq2seq models (a variant of Re-current Neural Network (RNN) networks). However, convolutional neural networks havebeen also proposed for text classification in different contexts [3][4][5].The main objective of this project will be to address a topic classification problem usingConvolutional Neural Network (CNN) based architectures in order to classify the datafrom the Empathic project’s dataset. Besides that, we will also propose and test a numberof architectures based on RNN in order to provide some comparison of the performancefrom each model

Modularity-based approaches to community detection in multilayer networks with applications toward precision medicine

Networks have become an important tool for the analysis of complex systems across many different disciplines including computer science, biology, chemistry, social sciences, and importantly, cancer medicine. Networks in the real world typically exhibit many forms of higher order organization. The subfield of networks analysis known as community detection aims to provide tools for discovering and interpreting the global structure of a networks-based on the connectivity patterns of its edges. In this thesis, we provide an overview of the methods for community detection in networks with an emphasis on modularity-based approaches. We discuss several caveats and drawbacks of currently available methods. We also review the success that network analyses have had in interpreting large scale 'omics' data in the context of cancer biology. In the second and third chapters, we present CHAMP and multimodbp, two useful community detection tools that seek to overcome several of the deficiencies in modularity-based community detection. In the final chapter, we develop a networks-based significance test for addressing an important question in the field of oncology: are mutations in DNA damage repair genes associated with elevated levels of tumor mutational burden. We apply the tools of network analysis to this question and showcase how this approach yields new insight into the structure of the problem, revealing what we call the TMB Paradox. We close by demonstrating the clinical utility of our findings in predicting patient response to novel immunotherapies.Doctor of Philosoph