10,943 research outputs found

    MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter

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    To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE

    Learning disentangled speech representations

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    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

    Translating erasure: Proposing auto-theory as a practice for artistic enquiry and analysis while comprehending personal grief

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    Erasure as an artistic technique has developed in my moving image work after my father's passing. I export videos into sequences of thousands of images and erase outlines of the targeted objects in each frame. The repetitive and low conscious labour is a way to ease the agony and to grieve my father. Hours compressed into thousands of frames, turning into a glimpse of illusion and leaving a ghostly emptiness on the images. Both its visual presentation and making reflect the life events and encounters I've experienced in the UK and Taiwan in the past years. I consider an artwork embodies interconnected relationships between one's personal impulses and artistic training. As an art student, I have found it challenging to describe such a creative process with conventional academic writing. Within a construct that inclines to present thoughts as reasonable and rational arguments, my personal experiences and the intensity of feeling seem out of place. Within an academic framework, how can I make an argument out of how I have developed the erasure in my artwork to perform the grief, fading memories of a loved one, existential crisis and what's in-between? Through auto-theoretical approaches to writing and making of moving image work, this research aims to build a structure that can express both the intimate and intellectual aspects of an art practice. This writing up process interweaves my personal stories that motivate my artistic expression into art theories. The memories about my late father, my relationship with languages, and my lives between the UK and Taiwan meet with different artists' uses of erasure. As the conversations between the introspections and theoretical analysis accumulate, my writing and moving image work unravel an art journey that encompasses the nuances and struggles I've experienced as an international student. Within the search for an ideal model to illustrate an art practice, this research further generates profound understandings of memory, grief, loss, language, conflicted identities and cultural belonging

    Design principles and architecture of a second language learning chatbot

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    The purpose of this article is to set out the design principles and architecture of a second language (L2) learning voice chatbot. Building on L2 acquisition theories and chatbot research, in this article, we report on a South Korean government-funded longitudinal project in which we designed and developed a chatbot called “Ellie”. Chatbot Ellie has three chat modes, “General Chat,” “Task Chat,” and “Skills”. In the General Chat mode, L2 users can have short talks about personal information, whereas in the Task Chat mode, they can engage in a wide range of problem-solving L2 tasks to achieve task goals by exchanging meanings with Ellie. The Skills mode offers form-focused language practice. Ellie was piloted among 137 Korean high school students, who used Ellie individually or in a group, for seven weeks in their English classes. The quality of the chatbot was investigated in terms of the appropriateness of language level, continuity of conversation, and success in task performance. Based on the results of the pilot, Ellie appears to have considerable potential to become an effective language learning companion for L2 learners, and has implications for the design and developments of future L2 chatbots

    Unraveling the effect of sex on human genetic architecture

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    Sex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes, amongst other factors. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides the genome with a distinct hormonal milieu, differential gene expression, and environmental pressures arising from gender societal roles. This thus poses the possibility of observing gene by sex (GxS) interactions between the sexes that may contribute to some of the phenotypic differences observed. In recent years, there has been growing evidence of GxS, with common genetic variation presenting different effects on males and females. These studies have however been limited in regards to the number of traits studied and/or statistical power. Understanding sex differences in genetic architecture is of great importance as this could lead to improved understanding of potential differences in underlying biological pathways and disease etiology between the sexes and in turn help inform personalised treatments and precision medicine. In this thesis we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We further investigated whether sex-agnostic (non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we studied the potential functional role of sex differences in genetic architecture through sex biased expression quantitative trait loci (eQTL) and gene-level analyses. Overall, this study marks a broad examination of the genetics of sex differences. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Furthermore, our results suggest the need to consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms

    Graphical scaffolding for the learning of data wrangling APIs

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    In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction

    Novel strategies for the modulation and investigation of memories in the hippocampus

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    Disruptions of the memory systems in the brain are linked to the manifestation of many neuropsychiatric diseases such as Alzheimer’s disease, depression, and post-traumatic stress disorder. The limited efficacy of current treatments necessities the development of more effective therapies. Neuromodulation has proven effective in a variety of neurological diseases and could be an attractive solution for memory disorders. However, the application of neuromodulation requires a more detailed understanding of the network dynamics associated with memory formation and recall. In this work, we applied a combination of optical and computational tools in the development of a novel strategy for the modulation of memories, and have expanded its application for interrogation of the hippocampal circuitry underlying memory processing in mice. First, we developed a closed-loop optogenetic stimulation platform to activate neurons implicated in memory processing (engram neurons) with a high temporal resolution. We applied this platform to modulate the activity of engram neurons and assess memory processing with respect to synchronous network activity. The results of our investigation support the proposal that encoding new information and recalling stored memories occur during distinct epochs of hippocampal network-wide oscillations. Having established the high efficacy of the modulation of engram neurons’ activity in a closed-loop fashion, we sought to combine it with two-photon imaging to enable high spatial resolution interrogation of hippocampal circuitry. We developed a behavioral apparatus for head-fixed engram modulation and the assessment of memory recall in immobile animals. Moreover, through the optimization of dual color two-photon imaging, we improved the ability to monitor activity of neurons in the subfields of the hippocampus with cellular specificity. The platform created here will be applied to investigate the effects of engram reactivation on downstream projections targets with high spatial and cell subtype specificity. Following these lines of investigations will enhance our understanding of memory modulation and could lead to novel neuromodulation treatments for neurological disorders associated with memory malfunctioning
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