Intertextuality is a key concept in literary theory that challenges traditional notions of text, signification or authorship. It views texts as part of a vast intertextual network that is constantly evolving and being reconfigured. This paper argues that the field of computational literary studies is the ideal place to conduct a study of intertextuality since we have now the ability to systematically compare texts with each others. Specifically, we present a work on a corpus of more than 12.000 French fictions from the 18th, 19th and early 20th century. We focus on evaluating the underlying roles of two literary notions, sub-genres and the literary canon in the framing of textuality. The article attempts to operationalize intertextuality using state-of-the-art contextual language models to encode novels and capture features that go beyond simple lexical or thematic approaches. Previous research (Hughes, 2012) supports the existence of a literary "style of a time", and our findings further reinforce this concept. Our findings also suggest that both subgenres and canonicity play a significant role in shaping textual similarities within French fiction. These discoveries point to the importance of considering genre and canon as dynamic forces that influence the evolution and intertextual connections of literary works within specific historical contexts.

In this paper, we introduce the Extreme Metal Vocals Dataset, which comprises a collection of recordings of extreme vocal techniques performed within the realm of heavy metal music. The dataset consists of 760 audio excerpts of 1 second to 30 seconds long, totaling about 100 min of audio material, roughly composed of 60 minutes of distorted voices and 40 minutes of clear voice recordings. These vocal recordings are from 27 different singers and are provided without accompanying musical instruments or post-processing effects. The distortion taxonomy within this dataset encompasses four distinct distortion techniques and three vocal effects, all performed in different pitch ranges. Performance of a state-of-the-art deep learning model is evaluated for two different classification tasks related to vocal techniques, demonstrating the potential of this resource for the audio processing community.

Baldur's Gate 3 is a game about making choices. Encounter an imposing, demonic creature in the depths of a cavernous underground temple and, depending on how the player has created their character, the monster may be convinced to kill off its hellish accompanying soldiers and even banish itself back to the inferno. The enemy might also be defeated more conventionally, with slashes from a sword and blasts of electricity, knocking over barrels of grease and setting the battlefield on fire. Find the player character tasked with retrieving an important item locked away in a well-guarded room and it's possible to sneak in to retrieve it, perhaps lie effectively enough to be granted entry, or, once again, simply turn everything surrounding that protected room into a bloodbath. The Baldur's Gate series began in 1998, created by BioWare, the studio that would go on to make popular role-playing series Mass Effect and Dragon Age.

Inscryption, out now on PC, is unlike anything else released this year--and easily one of the best video games of 2021. But I cannot … no, I MUST not spoil what makes it so good. Instead, I insist that you stop everything, go buy the game, and not return until you've played and finished it on your own Do not--I repeat, DO NOT--keep reading this review. I want you to read this review! I worked so hard on it.

We are going to work on a specific sub-task of NLP called text classification, this is the process of recognizing a pattern in a text and assign it a label. Examples that are used in your day to day life without you even noticing it include spam detection (in your mailbox), sentiment analysis (when you review a product or leave a comment) and tagging customer queries (when you fill in a contact form on a website). What we will try to do is to classify science-fiction books into different subgenres (dystopia, cyberpunk, space opera, …) based on their plot. In the end, we want a model that is able to take a book plot as an input and output the subgenres detected in the text and the confidence of the model that a subgenre is detected. The demonstrator can take up to 1 minute to open because I use a free version of Heroku to host my app, thus it goes to sleep when nobody uses it and it's better for the planet! This kind of algorithms could help an online market place to classify the books they receive to make more performant recommendations or a librarian to organize originally the books by subgenres instead of alphabetically, to create an experience in the library. Data is one of the most important (if not the most important) thing in data science.

Horror films were wildly popular on streaming platforms over the past year, and 2020 saw the horror genre take home its largest share of the box office in modern history.1 In a year where the world was stricken by real horrors, why were many people escaping to worlds full of fictional horrors? As odd as it may sound, the fact that people were more anxious in 2020 may be one reason why horror films were so popular. A look at typical horror fans may provide some clues about the nature of this peculiar phenomenon. For example, horror fans often mention their own anxiety and how horror helps them deal with it.

Prevalent efforts have been put in automatically inferring genres of musical items. Yet, the propose solutions often rely on simplifications and fail to address the diversity and subjectivity of music genres. Accounting for these has, though, many benefits for aligning knowledge sources, integrating data and enriching musical items with tags. Here, we choose a new angle for the genre study by seeking to predict what would be the genres of musical items in a target tag system, knowing the genres assigned to them within source tag systems. We call this a translation task and identify three cases: 1) no common annotated corpus between source and target tag systems exists, 2) such a large corpus exists, 3) only few common annotations exist. We propose the related solutions: a knowledge-based translation modeled as taxonomy mapping, a statistical translation modeled with maximum likelihood logistic regression; a hybrid translation modeled with maximum a posteriori logistic regression with priors given by the knowledge-based translation. During evaluation, the solutions fit well the identified cases and the hybrid translation is systematically the most effective w.r.t. multilabel classification metrics. This is a first attempt to unify genre tag systems by leveraging both representation and interpretation diversity.

When it first emerged more than 30 years ago, cyberpunk was hailed as the most exciting science fiction of the '80s. The subgenre, developed by a handful of younger writers, told stories of the near future, focusing on the collision of youth subcultures, new computer technologies, and global corporate dominance. It was only ever a small part of the total SF field, but cyberpunk received an outsize amount of attention. Since then, its characteristic tropes have become clichés. By 1992, they could be hilariously parodied by Neal Stephenson in Snow Crash (a novel often mistaken as an example of the subgenre it meant to mock). In 1999, the Wachowskis brought cyberpunk to a mass audience with The Matrix.

We consider a particular instance of a common problem in recommender systems: using a database of book reviews to inform user-targeted recommendations. In our dataset, books are categorized into genres and sub-genres. To exploit this nested taxonomy, we use a hierarchical model that enables information pooling across across similar items at many levels within the genre hierarchy. The main challenge in deploying this model is computational: the data sizes are large, and fitting the model at scale using off-the-shelf maximum likelihood procedures is prohibitive. To get around this computational bottleneck, we extend a moment-based fitting procedure proposed for fitting single-level hierarchical models to the general case of arbitrarily deep hierarchies. This extension is an order of magnetite faster than standard maximum likelihood procedures. The fitting method can be deployed beyond recommender systems to general contexts with deeply-nested hierarchical generalized linear mixed models.

The latest trailer for the Ridley Scott-produced horror Morgan quadruples down on one of the biggest current trends in science fiction: Trying, and failing, to create the perfect woman. For as long as we've had computers and servants, we've been dreaming up ways where they could be combined. In the past, we had a variety of subjects and experiences being covered in films and shows about these new forms of intelligence. A.I: Artificial Intelligence looked at whether we could manufacture childhood innocence and love, The Terminator examined the battle between creator and creation, Robin Williams' Bicentennial Man followed one android's journey to become legally human. And of course we've got Blade Runner, which is basically the gold standard of films about artificial intelligence. However, we're starting to see the subgenre become a bit more, well, specific.