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1. Automatic News Article Generation from Legislative Proceedings: A Phenom-based Approach

   Klimashevskaia, Anastasiia ; Gadgil, Richa ; Gerrity, Thomas ; Khosmood, Foaad ; Gütl, Christian ; Howe, Patrick ( October 2021 , Statistical Language and Speech Processing)

   Espinosa-Anke, Luis ; Martín-Vide, Carlos ; Spasić, Irena (Ed.)

   Algorithmic journalism refers to automatic AI-constructed news stories. There have been successful commercial implementations for news stories in sports, weather, financial reporting and similar domains with highly structured, well defined tabular data sources. Other domains such as local reporting have not seen adoption of algorithmic journalism, and thus no automated reporting systems are available in these categories which can have important implications for the industry. In this paper, we demonstrate a novel approach for producing news stories on government legislative activity, an area that has not widely adopted algorithmic journalism. Our data source is state legislative proceedings, primarily the transcribed speeches and dialogue from floor sessions and committee hearings in US State legislatures. Specifically, we create a library of potential events called phenoms. We systematically analyze the transcripts for the presence of phenoms using a custom partial order planner. Each phenom, if present, contributes some natural language text to the generated article: either stating facts, quoting individuals or summarizing some aspect of the discussion. We evaluate two randomly chosen articles with a user study on Amazon Mechanical Turk with mostly Likert scale questions. Our results indicate a high degree of achievement for accuracy of facts and readability of final content with 13 of 22 users in the first article and 19 of 20 subjects of the second article agreeing or strongly agreeing that the articles included the most important facts of the hearings. Other results strengthen this finding in terms of accuracy, focus and writing quality. 

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2. Dissociable meta‐analytic brain networks contribute to coordinated emotional processing

   https://doi.org/10.1002/hbm.24018  

   Riedel, Michael C. ; Yanes, Julio A. ; Ray, Kimberly L. ; Eickhoff, Simon B. ; Fox, Peter T. ; Sutherland, Matthew T. ; Laird, Angela R. ( February 2018 , Human Brain Mapping)

   Meta‐analytic techniques for mining the neuroimaging literature continue to exert an impact on our conceptualization of functional brain networks contributing to human emotion and cognition. Traditional theories regarding the neurobiological substrates contributing to affective processing are shifting from regional‐ towards more network‐based heuristic frameworks. To elucidate differential brain network involvement linked to distinct aspects of emotion processing, we applied an emergent meta‐analytic clustering approach to the extensive body of affective neuroimaging results archived in the BrainMap database. Specifically, we performed hierarchical clustering on the modeled activation maps from 1,747 experiments in the affective processing domain, resulting in five meta‐analytic groupings of experiments demonstrating whole‐brain recruitment. Behavioral inference analyses conducted for each of these groupings suggested dissociable networks supporting: (1) visual perception within primary and associative visual cortices, (2) auditory perception within primary auditory cortices, (3) attention to emotionally salient information within insular, anterior cingulate, and subcortical regions, (4) appraisal and prediction of emotional events within medial prefrontal and posterior cingulate cortices, and (5) induction of emotional responses within amygdala and fusiform gyri. These meta‐analytic outcomes are consistent with a contemporary psychological model of affective processing in which emotionally salient information from perceived stimuli are integrated with previous experiences to engender a subjective affective response. This study highlights the utility of using emergent meta‐analytic methods to inform and extend psychological theories and suggests that emotions are manifest as the eventual consequence of interactions between large‐scale brain networks.

    

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3. Using Co-Captured Face, Gaze, and Verbal Reactions to Images of Varying Emotional Content for Analysis and Semantic Alignment

   Gangji, Aliya ; Walden, Trevor ; Vaidyanathan, Preethi ; Prud'hommeaux, Emily ; Bailey, Reynold ; Alm, Cecilia O ( January 2017 , The AAAI-17 Workshop on Human-Aware Artificial Intelligence)

   Analyzing different modalities of expression can provide insights into the ways that humans interpret, label, and react to images. Such insights have the potential not only to advance our understanding of how humans coordinate these expressive modalities but also to enhance existing methodologies for common AI tasks such as image annotation and classification. We conducted an experiment that co-captured the facial expressions, eye movements, and spoken language data that observers produce while examining images of varying emotional content and responding to description-oriented vs. affect-oriented questions about those images. We analyzed the facial expressions produced by the observers in order to determine the connection between those expressions and an image's emotional content. We also explored the relationship between the valence of an image and the verbal responses to that image, and how that relationship relates to the nature of the prompt, using low-level lexical features and more complex affective features extracted from the observers' verbal responses. Finally, in order to integrate this multimodal data, we extended an existing bitext alignment framework to create meaningful pairings between narrated observations about images and the image regions indicated by eye movement data. The resulting annotations of image regions with words from observers' responses demonstrate the potential of bitext alignment for multimodal data integration and, from an application perspective, for annotation of open-domain images. In addition, we found that while responses to affect-oriented questions appear useful for image understanding, their holistic nature seems less helpful for image region annotation. 

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4. Audio deepfakes: A survey

   https://doi.org/10.3389/fdata.2022.1001063  

   Khanjani, Zahra ; Watson, Gabrielle ; Janeja, Vandana P. ( January 2023 , Frontiers in Big Data)

   A deepfake is content or material that is synthetically generated or manipulated using artificial intelligence (AI) methods, to be passed off as real and can include audio, video, image, and text synthesis. The key difference between manual editing and deepfakes is that deepfakes are AI generated or AI manipulated and closely resemble authentic artifacts. In some cases, deepfakes can be fabricated using AI-generated content in its entirety. Deepfakes have started to have a major impact on society with more generation mechanisms emerging everyday. This article makes a contribution in understanding the landscape of deepfakes, and their detection and generation methods. We evaluate various categories of deepfakes especially in audio. The purpose of this survey is to provide readers with a deeper understanding of (1) different deepfake categories; (2) how they could be created and detected; (3) more specifically, how audio deepfakes are created and detected in more detail, which is the main focus of this paper. We found that generative adversarial networks (GANs), convolutional neural networks (CNNs), and deep neural networks (DNNs) are common ways of creating and detecting deepfakes. In our evaluation of over 150 methods, we found that the majority of the focus is on video deepfakes, and, in particular, the generation of video deepfakes. We found that for text deepfakes, there are more generation methods but very few robust methods for detection, including fake news detection, which has become a controversial area of research because of the potential heavy overlaps with human generation of fake content. Our study reveals a clear need to research audio deepfakes and particularly detection of audio deepfakes. This survey has been conducted with a different perspective, compared to existing survey papers that mostly focus on just video and image deepfakes. This survey mainly focuses on audio deepfakes that are overlooked in most of the existing surveys. This article's most important contribution is to critically analyze and provide a unique source of audio deepfake research, mostly ranging from 2016 to 2021. To the best of our knowledge, this is the first survey focusing on audio deepfakes generation and detection in English. 

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5. Exploring the Relationship between Quantitative Reasoning Skills and News Habits

   Attaway, Bennett ; Voiklis, John ; Barchas-Lichtenstein, Jena ; Hochberg, Eric ; Hammerman, Jim ; Thomas, Uduak Grace ; LaMarca, Nicole ; Santhanam, Laura ; Parson, Patti ( August 2022 , Numeracy)

   Because people are constantly confronted with numbers and mathematical concepts in the news, we have embarked on a project to create journalism that can support news users’ number skills. But doing so requires understanding (1) journalists’ ability to reason with numbers, (2) other adults’ ability to do so, and (3) the attributes and affordances of news. In this paper, we focus on the relationship between adults’ news habits and their quantitative reasoning skills. We collected data from a sample of 1,200 US adults, testing their ability to interpret statistical results and asking them to report their news habits. The assessment we developed differentiated the skills of adults in our sample and conformed to the theoretical and statistical assumption that such skills are normally distributed in the population overall. We also found that respondents could be clustered into six distinct groups on the basis of news repertoires (overall patterns of usage, including frequency of news use overall and choice of news outlets). As often assumed in the literature on quantitative reasoning, these news repertoires predicted quantitative reasoning skills better than the amount of quantification in the outlets, but they still predicted only a small fraction of the variance. These results may suggest that news habits may play a smaller or less direct role in quantitative reasoning than has previously been assumed. We speculate that the presence (or absence) of quantification in everyday activities – namely work and hobbies – may be a better predictor of adults’ quantitative reasoning, as may additional dimensions of news habits and affective responses to numbers. 

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