Affective responses to music are highly personal. Despite consensus that idiosyncratic factors play a key role in regulating how listeners emotionally respond to music, precisely measuring the marginal effects of these variables has proved challenging. To address this gap, we develop computational methods to measure affective responses to music from over 403M listener comments on a Chinese social music platform. Building on studies from music psychology in systematic and quasi-causal analyses, we test for musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses. Finally, motivated by the social phenomenon known as w\v{a}ng-y\`i-y\'un, we identify influencing factors of platform user self-disclosures, the social support they receive, and notable differences in discloser user activity.

Abstract: The wide spread of fake news is increasingly threatening both individuals and society. Great efforts have been made for automatic fake news detection on a single domain (e.g., politics). However, correlations exist commonly across multiple news domains, and thus it is promising to simultaneously detect fake news of multiple domains. Based on our analysis, we pose two challenges in multi-domain fake news detection: 1) domain shift, caused by the discrepancy among domains in terms of words, emotions, styles, etc. 2) domain labeling incompleteness, stemming from the real-world categorization that only outputs one single domain label, regardless of topic diversity of a news piece. In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M3FEND) to address these two challenges. Specifically, we propose a Domain Memory Bank to enrich domain information which could discover potential domain labels based on seen news pieces and model domain characteristics.

Abstract: In this new digital era, social media has created a severe impact on the lives of people. In recent times, fake news content on social media has become one of the major challenging problems for society. The dissemination of fabricated and false news articles includes multimodal data in the form of text and images. The previous methods have mainly focused on unimodal analysis. Moreover, for multimodal analysis, researchers fail to keep the unique characteristics corresponding to each modality.

The artificial intelligence (AI) in healthcare market size is estimated to be USD 5,983.12 million in 2021 and is expected to witness a CAGR of 47.31% …

Our Queen, no not Elizabeth but Rest In Peace, Beyonce has returned from the heavens to deliver her highly anticipated seventh studio album. With somewhat of a stealth and cryptic release, Renaissance was a serendipitously timed breath of fresh air which was met with critical acclaim. The lead single, "Break My Soul" (which I have personally listened to over 200 times), was accompanied by an extremely stimulating lyric video. Yes, I am a notable Artist, but like everyone else I am huge fan of Beyonce. Her songs are amazing and her visuals are always spectacular.

Specifically, "narrow AI," which executes objective functions using data-trained models and frequently falls into the categories of deep learning …

In honor of Spooktober, here's a real... Well, though there is a very special spooky version of the Emmy's Black Box theme song within, the content of the podcast is not so much one of fear but of hope. I spoke with Paul Pangaro, PhD, President of The American Society for Cybernetics and Visiting Scholar of Computational Design at the Carnegie Mellon School of Architecture (previously a Professor of Practice in Human Computer Interaction). We dived into a bit of history around the term Cybernetics itself, and also how the system of Cybernetics played a role in the development and understanding of computation and, more specifically, artificial intelligence. We also talked about the state of Cybernetics today, and our hopes for Cybernetics in the future. As always, please feel free to tweet my @emmysblackbox with your thoughts/opinions about the podcast.

In this paper, we present a comprehensive analysis of Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. In particular, we firstly propose an inception-based and low footprint ASC model, referred to as the ASC baseline. The proposed ASC baseline is then compared with benchmark and high-complexity network architectures of MobileNetV1, MobileNetV2, VGG16, VGG19, ResNet50V2, ResNet152V2, DenseNet121, DenseNet201, and Xception. Next, we improve the ASC baseline by proposing a novel deep neural network architecture which leverages residual-inception architectures and multiple kernels. Given the novel residual-inception (NRI) model, we further evaluate the trade off between the model complexity and the model accuracy performance. Finally, we evaluate whether sound events occurring in a sound scene recording can help to improve ASC accuracy, then indicate how a sound scene context is well presented by combining both sound scene and sound event information. We conduct extensive experiments on various ASC datasets, including Crowded Scenes, IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 Task 1A and 1B, 2019 Task 1A and 1B, 2020 Task 1A, 2021 Task 1A, 2022 Task 1. The experimental results on several different ASC challenges highlight two main achievements; the first is to propose robust, general, and low complexity ASC systems which are suitable for real-life applications on a wide range of edge devices and mobiles; the second is to propose an effective visualization method for comprehensively presenting a sound scene context.

Norm discovery is important for understanding and reasoning about the acceptable behaviors and potential violations in human communication and interactions. We introduce NormSage, a framework for addressing the novel task of conversation-grounded multi-lingual, multi-cultural norm discovery, based on language model prompting and self-verification. NormSAGE leverages the expressiveness and implicit knowledge of the pretrained GPT-3 language model backbone, to elicit knowledge about norms through directed questions representing the norm discovery task and conversation context. It further addresses the risk of language model hallucination with a self-verification mechanism ensuring that the norms discovered are correct and are substantially grounded to their source conversations. Evaluation results show that our approach discovers significantly more relevant and insightful norms for conversations on-the-fly compared to baselines (>10+% in Likert scale rating). The norms discovered from Chinese conversation are also comparable to the norms discovered from English conversation in terms of insightfulness and correctness (<3% difference). In addition, the culture-specific norms are promising quality, allowing for 80% accuracy in culture pair human identification. Finally, our grounding process in norm discovery self-verification can be extended for instantiating the adherence and violation of any norm for a given conversation on-the-fly, with explainability and transparency. NormSAGE achieves an AUC of 95.4% in grounding, with natural language explanation matching human-written quality.

It wasn't meant to happen like this. Yes, the robots were always going to come for everyone's jobs, but it was the menial ones that were set to go first. Freed from the need to fill out spreadsheets and perform administrative duties, we were all supposed to have extra time to indulge in more creative, fulfilling pursuits. Yet Microsoft Excel still exists while AI algorithms are producing works of art that are both commercially viable and critically respected. An AI artist, Jason Allen, recently caused outrage among old-school digital artists by winning a digital art competition. One of the writers of US publication The Atlantic, Charlie Warzel, provoked the ire of illustrators around the world by choosing to adorn an article about controversial radio host Alex Jones with an AI-generated caricature as opposed to using a stock photo or commissioning a portrait.