The rise of social media platforms has facilitated the formation of echo chambers, which are online spaces where users predominantly encounter viewpoints that reinforce their existing beliefs while excluding dissenting perspectives. This phenomenon significantly hinders information dissemination across communities and fuels societal polarization. Therefore, it is crucial to develop methods for quantifying echo chambers. In this paper, we present the Echo Chamber Score (ECS), a novel metric that assesses the cohesion and separation of user communities by measuring distances between users in the embedding space. In contrast to existing approaches, ECS is able to function without labels for user ideologies and makes no assumptions about the structure of the interaction graph. To facilitate measuring distances between users, we propose EchoGAE, a self-supervised graph autoencoder-based user embedding model that leverages users' posts and the interaction graph to embed them in a manner that reflects their ideological similarity. To assess the effectiveness of ECS, we use a Twitter dataset consisting of four topics - two polarizing and two non-polarizing. Our results showcase ECS's effectiveness as a tool for quantifying echo chambers and shedding light on the dynamics of online discourse.

Existing melody harmonization models have made great progress in improving the quality of generated harmonies, but most of them ignored the emotions beneath the music. Meanwhile, the variability of harmonies generated by previous methods is insufficient. To solve these problems, we propose a novel LSTM-based Hierarchical Variational Auto-Encoder (LHVAE) to investigate the influence of emotional conditions on melody harmonization, while improving the quality of generated harmonies and capturing the abundant variability of chord progressions. Specifically, LHVAE incorporates latent variables and emotional conditions at different levels (piece- and bar-level) to model the global and local music properties. Additionally, we introduce an attention-based melody context vector at each step to better learn the correspondence between melodies and harmonies. Objective experimental results show that our proposed model outperforms other LSTM-based models. Through subjective evaluation, we conclude that only altering the types of chords hardly changes the overall emotion of the music. The qualitative analysis demonstrates the ability of our model to generate variable harmonies.

"Having this technology available means we can literally produce hundreds of videos in a matter of days. Compare that to the months, if not years, that we'd need if we were filming the content in the traditional way," says Braham Djidjelli, Hugosave's co-founder and chief product officer.

Proponents say such practices may help reduce use-of-force incidents. The United Nations Security Council held its first discussion on artificial intelligence (AI) and associated risks, with a number of leaders highlighting the dangerous potential the technology possesses in the wrong hands. "The malicious use of AI systems for terrorist, criminal or state purposes could cause horrific levels of deaths and destruction, widespread trauma and deep psychological damage on an unimaginable scale," U.N. Secretary-General Antonio Guterres said in his remarks at the meeting. "Generative AI has enormous potential for good and evil at scale." "Its creators themselves have warned that much bigger, potentially catastrophic and existential risks lie ahead," he added.

It was just another day in South Park. The kids were making fun of each other on the playground, while the parents were all doing their best to maintain their sanity in the small Colorado town. And then there was me, a tech journalist going door-to-door warning about the impending AI apocalypse. No, I wasn't actually guest starring on the long-running TV series -- I was thrust into an episode entirely produced by the Showrunner AI model from The Simulation, the next iteration of the VR studio Fable. All it took was some audio of my voice (recorded during a call with The Simulation's CEO Edward Saatchi), a picture and a two-sentence prompt to produce the episode.

When Sunshine Henle's mother, Linda, died unexpectedly at the age of 72, Henle, a 42-year-old Floridian, was left with what she describes as a "gaping hole of silence" in her life. Even though Linda had lived in New York, where she worked as a Sunday school teacher, the pair had kept in constant contact through phone calls and texting. "I always knew she was there, no matter what – if I was upset, or if I just needed to talk. She would always respond," says Henle. In November, Linda collapsed in her home and was unable to move. Henle's brother Sam and her sister-in-law Julie took her to urgent care.

Music can evoke various emotions, and with the advancement of technology, it has become more accessible to people. Bangla music, which portrays different human emotions, lacks sufficient research. The authors of this article aim to analyze Bangla songs and classify their moods based on the lyrics. To achieve this, this research has compiled a dataset of 4000 Bangla song lyrics, genres, and used Natural Language Processing and the Bert Algorithm to analyze the data. Among the 4000 songs, 1513 songs are represented for the sad mood, 1362 for the romantic mood, 886 for happiness, and the rest 239 are classified as relaxation. By embedding the lyrics of the songs, the authors have classified the songs into four moods: Happy, Sad, Romantic, and Relaxed. This research is crucial as it enables a multi-class classification of songs' moods, making the music more relatable to people's emotions. The article presents the automated result of the four moods accurately derived from the song lyrics.

In the interdisciplinary field of artificial intelligence (AI) the problem of clear terminology is especially momentous. This paper claims, that AI debates are still characterised by a lack of critical distance to metaphors like 'training', 'learning' or 'deciding'. As consequence, reflections regarding responsibility or potential use-cases are greatly distorted. Yet, if relevant decision-makers are convinced that AI can develop an 'understanding' or properly 'interpret' issues, its regular use for sensitive tasks like deciding about social benefits or judging court cases looms. The chapter argues its claim by analysing central notions of the AI debate and tries to contribute by proposing more fitting terminology and hereby enabling more fruitful debates. It is a conceptual work at the intersection of critical computer science and philosophy of language.

Jazz pianists often uniquely interpret jazz standards. Passages from these interpretations can be viewed as sections of variation. We manually extracted such variations from solo jazz piano performances. The JAZZVAR dataset is a collection of 502 pairs of Variation and Original MIDI segments. Each Variation in the dataset is accompanied by a corresponding Original segment containing the melody and chords from the original jazz standard. Our approach differs from many existing jazz datasets in the music information retrieval (MIR) community, which often focus on improvisation sections within jazz performances. In this paper, we outline the curation process for obtaining and sorting the repertoire, the pipeline for creating the Original and Variation pairs, and our analysis of the dataset. We also introduce a new generative music task, Music Overpainting, and present a baseline Transformer model trained on the JAZZVAR dataset for this task. Other potential applications of our dataset include expressive performance analysis and performer identification.

Online communities develop unique characteristics, establish social norms, and exhibit distinct dynamics among their members. Activity in online communities often results in concrete ``off-line'' actions with a broad societal impact (e.g., political street protests and norms related to sexual misconduct). While community dynamics, information diffusion, and online collaborations have been widely studied in the past two decades, quantitative studies that measure the effectiveness of online communities in promoting their agenda are scarce. In this work, we study the correspondence between the effectiveness of a community, measured by its success level in a competitive online campaign, and the underlying dynamics between its members. To this end, we define a novel task: predicting the success level of online communities in Reddit's r/place - a large-scale distributed experiment that required collaboration between community members. We consider an array of definitions for success level; each is geared toward different aspects of collaborative achievement. We experiment with several hybrid models, combining various types of features. Our models significantly outperform all baseline models over all definitions of `success level'. Analysis of the results and the factors that contribute to the success of coordinated campaigns can provide a better understanding of the resilience or the vulnerability of communities to online social threats such as election interference or anti-science trends. We make all data used for this study publicly available for further research.