Meme clustering is critical for toxicity detection, virality modeling, and typing, but it has received little attention in previous research. Clustering similar Internet memes is challenging due to their multimodality, cultural context, and adaptability. Existing approaches rely on databases, overlook semantics, and struggle to handle diverse dimensions of similarity. This paper introduces a novel method that uses template-based matching with multi-dimensional similarity features, thus eliminating the need for predefined databases and supporting adaptive matching. Memes are clustered using local and global features across similarity categories such as form, visual content, text, and identity. Our combined approach outperforms existing clustering methods, producing more consistent and coherent clusters, while similarity-based feature sets enable adaptability and align with human intuition. We make all supporting code publicly available to support subsequent research.

Accurately modeling user preferences is vital not only for improving recommendation performance but also for enhancing transparency in recommender systems. Conventional user profiling methods, such as averaging item embeddings, often overlook the evolving, nuanced nature of user interests, particularly the interplay between short-term and long-term preferences. In this work, we leverage large language models (LLMs) to generate natural language summaries of users' interaction histories, distinguishing recent behaviors from more persistent tendencies. Our framework not only models temporal user preferences but also produces natural language profiles that can be used to explain recommendations in an interpretable manner. These textual profiles are encoded via a pre-trained model, and an attention mechanism dynamically fuses the short-term and long-term embeddings into a comprehensive user representation. Beyond boosting recommendation accuracy over multiple baselines, our approach naturally supports explainability: the interpretable text summaries and attention weights can be exposed to end users, offering insights into why specific items are suggested. Experiments on real-world datasets underscore both the performance gains and the promise of generating clearer, more transparent justifications for content-based recommendations.

Charles McBee stops by Fox News Saturday Night With Jimmy Failla to give his take on actor John Boyega calling out the "Star Wars" franchise for its overwhelming whiteness. The White House slammed the "radical left" in a social media post Sunday, showing an AI-generated image of President Donald Trump wielding a lightsaber in celebration of May the Fourth, or "Star Wars Day." May 4 has long been regarded as a day to celebrate the iconic movie franchise as fans post on social media "May the Fourth be with you," an offshoot of the memorable Star Wars quote "May the force be with you." On Sunday, the White House took an opportunity to celebrate the popular day with a post on X, while also taking digs at the Trump administration's biggest critics. "Happy May the 4th to all, including the Radical Left Lunatics who are fighting so hard to bring Sith Lords, Murderers, Drug Lords, Dangerous Prisoners, & well known MS-13 Gang Members, back into our Galaxy. You're not the Rebellion--you're the Empire," the White House wrote.

In George Lucas's classic 1980 film'The Empire Strikes Back', hero Han Solo (Harrison Ford) is frozen in carbonite by the evil Darth Vader. The fictional metal hardened around the heroic space smuggler as it cooled – sealing him in a state of'perfect hibernation'. Carbonite is of course a fictional material, consigned to the realms of the Star Wars galaxy far, far away. But according to one scientist, this scene is not completely the stuff of science-fiction. Dr Alex Baker, a chemist at the University of Warwick, thinks humans could potentially be frozen like Solo with a real-life equivalent.

Author William R. Forstchen's bestselling novel "One Second After" – which imagines the devastating effects of an EMP (electromagnetic pulse) strike on the United States – is being adapted into a feature film. The screenplay will be written by renowned sci-fi writer J. Michael Straczynski, with Forstchen himself serving as an executive producer. Fox News Digital spoke with Forstchen about the real-world inspiration behind his work and why he warns that an EMP attack is a looming threat, not just science fiction. "I wanted to write an accurate, a very accurate story of what would happen in a small town in North Carolina if the power went off, and it never came back on," he said. Electromagnetic pulse expert William R. Forstchen speaks at the rally against North Korea on San Francisco's Golden Gate Bridge and Yerba Buena Gardens to support the new Homefront video game on March 2, 2011, in San Francisco, Calif.

Since 2017, the Vespa-maker Piaggio's Fast Forward division has been exploring a unique way to cut down on car trips: Building robots that can follow you around and carry your stuff. We called its original Gita (pronounced "jee-tah," Italian for "trip") robot an attractive rolling porter, but its size and initial 3,250 price made it more of a robo-curio than something you'd actually want to buy. The company followed that up with the Gitamini, a smaller and slightly cheaper 1,850 model, but even that remains niche. Most people just don't have thousands of dollars to spend on a cute follow bot -- but the calculation may be different for Star Wars fans. So it's not a huge surprise to see Piaggio Fast Forward debut a special Star Wars edition robot: the G1T4-M1N1. It's basically just a 2,875 version of the Gitamini (which now sells for 2,475) with Star Wars decals and sound effects.

'The CyberGuy' Kurt Knutsson discusses the creation of a robot in China that can reportedly build cars and do everyday tasks on'Fox & Friends Weekend.' Let's face it, our digital lives get messy. Whether it's thousands of unread emails, random screenshots cluttering your desktop or a downloads folder that's basically a graveyard, the digital gunk adds up fast. But cleaning it all up doesn't have to be overwhelming. With a few smart automations and tools, you can tidy up your tech and keep things running smoothly, without lifting a finger every week. Join the FREE "CyberGuy Report": Get my expert tech tips, critical security alerts and exclusive deals, plus instant access to my free "Ultimate Scam Survival Guide" when you sign up!

"Jeopardy!" host Ken Jennings tells Fox News Digital he wants to know a human is behind any creative projects, not AI. "I'm deeply skeptical of AI," Jennings told Fox News Digital at the TCM Classic Film Festival. "Obviously, these current iterations of LLMs [Large Language Models] would clean Watson's clock at'Jeopardy!' The technology has moved on. I've played with chatbots and'Jeopardy!' clues, and they're very hard to stump," he said.

This paper presents a comprehensive computational framework for analyzing linguistic complexity and socio-cultural trends in hip-hop lyrics. Using a dataset of 3,814 songs from 146 influential artists spanning four decades (1980-2020), we employ natural language processing techniques to quantify multiple dimensions of lyrical complexity. Our analysis reveals a 23.7% increase in vocabulary diversity over the study period, with East Coast artists demonstrating 17.3% higher lexical variation than other regions. Rhyme density increased by 34.2% across all regions, with Midwest artists exhibiting the highest technical complexity (3.04 rhymes per line). Topic modeling identified significant shifts in thematic content, with social justice themes decreasing from 28.5% to 13.8% of content while introspective themes increased from 7.6% to 26.3%. Sentiment analysis demon- strated that lyrics became significantly more negative during sociopolitical crises, with polarity decreasing by 0.31 following major social unrest. Multi-dimensional analysis revealed four dis- tinct stylistic approaches that correlate strongly with geographic origin (r=0.68, p!0.001) and time period (r=0.59, p<0.001). These findings establish quantitative evidence for the evolution of hip- hop as both an art form and a reflection of societal dynamics, providing insights into the interplay between linguistic innovation and cultural context in popular music.

In a real-world corpus, knowledge frequently recurs across documents but often contains inconsistencies due to ambiguous naming, outdated information, or errors, leading to complex interrelationships between contexts. Previous research has shown that language models struggle with these complexities, typically focusing on single factors in isolation. We classify these relationships into four types: distracting, ambiguous, counterfactual, and duplicated. Our analysis reveals that no single approach effectively addresses all these interrelationships simultaneously. Therefore, we introduce Context Organizer (CORG), a framework that organizes multiple contexts into independently processed groups. This design allows the model to efficiently find all relevant answers while ensuring disambiguation. CORG consists of three key components: a graph constructor, a reranker, and an aggregator. Our results demonstrate that CORG balances performance and efficiency effectively, outperforming existing grouping methods and achieving comparable results to more computationally intensive, single-context approaches.