Measuring Societal Biases in Text Corpora via First-Order Co-occurrence

Text corpora are used to study societal biases, typically through statistical models such as word embeddings. The bias of a word towards a concept is typically estimated using vectors similarity, measuring whether the word and concept words share other words in their contexts. We argue that this second-order relationship introduces unrelated concepts into the measure, which causes an imprecise measurement of the bias. We propose instead to measure bias using the direct normalized co-occurrence associations between the word and the representative concept words, a first-order measure, by reconstructing the co-occurrence estimates inherent in the word embedding models. To study our novel corpus bias measurement method, we calculate the correlation of the gender bias values estimated from the text to the actual gender bias statistics of the U.S. job market, provided by two recent collections. The results show a consistently higher correlation when using the proposed first-order measure with a variety of word embedding models, as well as a more severe degree of bias, especially to female in a few specific occupations

Analysis and Application of Language Models to Human-Generated Textual Content

Social networks are enormous sources of human-generated content. Users continuously create information, useful but hard to detect, extract, and categorize. Language Models (LMs) have always been among the most useful and used approaches to process textual data. Firstly designed as simple unigram models, they improved through the years until the recent release of BERT, a pre-trained Transformer-based model reaching state-of-the-art performances in many heterogeneous benchmark tasks, such as text classification and tagging. In this thesis, I apply LMs to textual content publicly shared on social media. I selected Twitter as the principal source of data for the performed experiments since its users mainly share short and noisy texts. My goal is to build models that generate meaningful representations of users encoding their syntactic and semantic features. Once appropriate embeddings are defined, I compute similarities between users to perform higher-level analyses. Tested tasks include the extraction of emerging knowledge, represented by users similar to a given set of well-known accounts, controversy detection, obtaining controversy scores for topics discussed online, community detection and characterization, clustering similar users and detecting outliers, and stance classification of users and tweets (e.g., political inclination, COVID-19 vaccines position). The obtained results suggest that publicly available data contains delicate information about users, and Language Models can now extract it, threatening users' privacy

General Sentiment Decomposition: opinion mining based on raw Natural Language text

The importance of person-to-person communication about a certain topic (Word of Mouth) is growing day by day, especially for decision-makers. These phenomena can be directly observed in online social networks. For example, the rise of influencers and social media managers. If more people talk about a specific product, then more people are encouraged to buy it and vice versa. Forby, those people usually leave a review for it. Such a review will directly impact the product, and this effect is amplified proportionally to how much the reviewer is considered to be trustworthy by the potential new customer. Furthermore, considering the negative reporting bias, it is easy to understand how customer satisfaction is of absolute interest for a company (as well as citizens' trust is for a politician). Textual data have then proved extremely useful, but they are complex, as the language is. For that, many approaches focus more on producing well-performing classifiers and ignore the highly complex interpretability of their models. Instead, we propose a framework able to produce a good sentiment classifier with a particular focus on the model interpretability. After analyzing the impact of Word of Mouth on earnings and the related psychological aspects, we propose an algorithm to extract the sentiment from a Natural Language text corpus. The combined approach of Neural Networks, characterized by high predictive power but at the cost of complex interpretation (usually considered as black-boxes), with more straightforward and informative models, allows not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. In fact, the General Sentiment Decomposition (GSD) framework that we propose is based on a combination of Threshold-based Naive Bayes (an improved version of the original algorithm), SentiWordNet (an enriched Lexical Database for Sentiment Analysis tasks), and the Words Embeddings features (a high dimensional representation of words) that directly comes from the usage of Neural Networks. Moreover, using the GSD framework, we assess an objective sentiment scoring that improves the results' interpretation in many fields. For example, it is possible to identify specific critical sectors that require intervention to improve the offered services, find the company's strengths (useful for advertising campaigns), and, if time information is present, analyze trends on macro/micro topics. Besides, we have to consider that NL text data can be associated (or not) with a sentiment label, for example: 'positive' or 'negative'. To support further decision-making, we apply the proposed method to labeled (Booking.com, TripAdvisor.com) and unlabelled (Twitter.com) data, analyzing the sentiment of people who discuss a particular issue. In this way, we identify the aspects perceived as critical by the people concerning the "feedback" they publish on the web and quantify how happy (or not) they are about a specific problem. In particular, for Booking.com and TripAdvisor.com, we focus on customer satisfaction, whilst for Twitter.com, the main topic is climate change

Reasoning with Uncertainty in Deep Learning for Safer Medical Image Computing

Deep learning is now ubiquitous in the research field of medical image computing. As such technologies progress towards clinical translation, the question of safety becomes critical. Once deployed, machine learning systems unavoidably face situations where the correct decision or prediction is ambiguous. However, the current methods disproportionately rely on deterministic algorithms, lacking a mechanism to represent and manipulate uncertainty. In safety-critical applications such as medical imaging, reasoning under uncertainty is crucial for developing a reliable decision making system. Probabilistic machine learning provides a natural framework to quantify the degree of uncertainty over different variables of interest, be it the prediction, the model parameters and structures, or the underlying data (images and labels). Probability distributions are used to represent all the uncertain unobserved quantities in a model and how they relate to the data, and probability theory is used as a language to compute and manipulate these distributions. In this thesis, we explore probabilistic modelling as a framework to integrate uncertainty information into deep learning models, and demonstrate its utility in various high-dimensional medical imaging applications. In the process, we make several fundamental enhancements to current methods. We categorise our contributions into three groups according to the types of uncertainties being modelled: (i) predictive; (ii) structural and (iii) human uncertainty. Firstly, we discuss the importance of quantifying predictive uncertainty and understanding its sources for developing a risk-averse and transparent medical image enhancement application. We demonstrate how a measure of predictive uncertainty can be used as a proxy for the predictive accuracy in the absence of ground-truths. Furthermore, assuming the structure of the model is flexible enough for the task, we introduce a way to decompose the predictive uncertainty into its orthogonal sources i.e. aleatoric and parameter uncertainty. We show the potential utility of such decoupling in providing a quantitative “explanations” into the model performance. Secondly, we introduce our recent attempts at learning model structures directly from data. One work proposes a method based on variational inference to learn a posterior distribution over connectivity structures within a neural network architecture for multi-task learning, and share some preliminary results in the MR-only radiotherapy planning application. Another work explores how the training algorithm of decision trees could be extended to grow the architecture of a neural network to adapt to the given availability of data and the complexity of the task. Lastly, we develop methods to model the “measurement noise” (e.g., biases and skill levels) of human annotators, and integrate this information into the learning process of the neural network classifier. In particular, we show that explicitly modelling the uncertainty involved in the annotation process not only leads to an improvement in robustness to label noise, but also yields useful insights into the patterns of errors that characterise individual experts

Combining behavioral and neurolinguistic methodologies to investigate Spanish scalar indefinites among mono- and bilinguals: an event-related potential study

Quantifiers like some, most, many and all are said to form part of a scale on which the relative informativity of each quantifier is weighted against the others. Weak quantifiers like some generate an implicit meaning beyond their literal semantic meaning. For example, some PhD students like writing can be taken to mean that not all PhD students like writing. Such an implied meaning is called a scalar implicature (SI). Researchers have examined SIs in child and adult populations using a variety of methodological designs. While children are more inclined to retrieve lower bounded interpretations than adults concerning SI derivation, particularly without explicit instruction, both groups show considerable variability. A growing body of SI work aimed at explaining this variability has examined implicit brain responses while individuals perform various linguistic as well as cognitive tasks and methodological design factors that may give rise to SI interpretative variation. Some of this research has found that SI derivation is associated with scores on cognitive tests such as the Autism Spectrum Quotient (ASQ), working memory (WM) as cognitive load is increased, and the Systematizing Quotient-Revised (SQ-R) questionnaire, and other task design factors. One implication drawn from this research is that lack of SI derivation among adults in particular may be due to a differences in inherent cognitive or psychological mechanisms, such that higher or lower scores on the above-named tasks are apparently correlated with rates of SI derivation and that methodological design can create issues in data interpretation if confounds are not carefully controlled. The purpose of this dissertation is to further shed light on specific methodological and population (bilingualism) factors that contribute to differential SI derivation, in this case among adult native Spanish speakers and Spanish-English bilinguals. I maintain herein that research must fully consider its own role in how SIs are treated experimentally, whether in design, connections made to cognition, or population choice, before generalizing results. Once such variables have been controlled, we may more fully be able to understand inter-individual variation in SI derivation in healthy populations

Seeing affect: knowledge infrastructures in facial expression recognition systems

Efforts to process and simulate human affect have come to occupy a prominent role in Human-Computer Interaction as well as developments in machine learning systems. Affective computing applications promise to decode human affective experience and provide objective insights into usersʼ affective behaviors, ranging from frustration and boredom to states of clinical relevance such as depression and anxiety. While these projects are often grounded in psychological theories that have been contested both within scholarly and public domains, practitioners have remained largely agnostic to this debate, focusing instead on the development of either applicable technical systems or advancements of the fieldʼs state of the art. I take this controversy as an entry point to investigate the tensions related to the classification of affective behaviors and how practitioners validate these classification choices. This work offers an empirical examination of the discursive and material repertoires ‒ the infrastructures of knowledge ‒ that affective computing practitioners mobilize to legitimize and validate their practice. I build on feminist studies of science and technology to interrogate and challenge the claims of objectivity on which affective computing applications rest. By looking at research practices and commercial developments of Facial Expression Recognition (FER) systems, the findings unpack the interplay of knowledge, vision, and power underpinning the development of machine learning applications of affective computing. The thesis begins with an analysis of historical efforts to quantify affective behaviors and how these are reflected in modern affective computing practice. Here, three main themes emerge that will guide and orient the empirical findings: 1) the role that framings of science and scientific practice play in constructing affective behaviors as “objective” scientific facts, 2) the role of human interpretation and mediation required to make sense of affective data, and 3) the prescriptive and performative dimensions of these quantification efforts. This analysis forms the historical backdrop for the empirical core of the thesis: semi-structured interviews with affective computing practitioners across the academic and industry sectors, including the data annotators labelling the modelsʼ training datasets. My findings reveal the discursive and material strategies that participants adopt to validate affective classification, including forms of boundary work to establish credibility as well as the local and contingent work of human interpretation and standardization involved in the process of making sense of affective data. Here, I show how, despite their professed agnosticism, practitioners must make normative choices in order to ʻseeʼ (and teach machines how to see) affect. I apply the notion of knowledge infrastructures to conceptualize the scaffolding of data practices, norms and routines, psychological theories, and historical and epistemological assumptions that shape practitionersʼ vision and inform FER design. Finally, I return to the problem of agnosticism and its socio-ethical relevance to the broader field of machine learning. Here, I argue that agnosticism can make it difficult to locate the technologyʼs historical and epistemological lineages and, therefore, obscure accountability. I conclude by arguing that both policy and practice would benefit from a nuanced examination of the plurality of visions and forms of knowledge involved in the automation of affect

EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks

Book of abstract of the fifth European conference on Social Networks EUSN 202

Proceedings of the 35th International Workshop on Statistical Modelling : July 20- 24, 2020 Bilbao, Basque Country, Spain

466 p.The InternationalWorkshop on Statistical Modelling (IWSM) is a reference workshop in promoting statistical modelling, applications of Statistics for researchers, academics and industrialist in a broad sense. Unfortunately, the global COVID-19 pandemic has not allowed holding the 35th edition of the IWSM in Bilbao in July 2020. Despite the situation and following the spirit of the Workshop and the Statistical Modelling Society, we are delighted to bring you the proceedings book of extended abstracts

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants