In today's digital world, social media plays a significant role in facilitating communication and content sharing. However, the exponential rise in user-generated content has led to challenges in maintaining a respectful online environment. In some cases, users have taken advantage of anonymity in order to use harmful language, which can negatively affect the user experience and pose serious social problems. Recognizing the limitations of manual moderation, automatic detection systems have been developed to tackle this problem. Nevertheless, several obstacles persist, including the absence of a universal definition for harmful language, inadequate datasets across languages, the need for detailed annotation guideline, and most importantly, a comprehensive framework. This study aims to address these challenges by introducing, for the first time, a detailed framework adaptable to any language. This framework encompasses various aspects of harmful language detection. A key component of the framework is the development of a general and detailed annotation guideline. Additionally, the integration of sentiment analysis represents a novel approach to enhancing harmful language detection. Also, a definition of harmful language based on the review of different related concepts is presented. To demonstrate the effectiveness of the proposed framework, its implementation in a challenging low-resource language is conducted. We collected a Persian dataset and applied the annotation guideline for harmful detection and sentiment analysis. Next, we present baseline experiments utilizing machine and deep learning methods to set benchmarks. Results prove the framework's high performance, achieving an accuracy of 99.4% in offensive language detection and 66.2% in sentiment analysis.

This paper introduces SceneCraft, a Large Language Model (LLM) Agent converting text descriptions into Blender-executable Python scripts which render complex scenes with up to a hundred 3D assets. This process requires complex spatial planning and arrangement. We tackle these challenges through a combination of advanced abstraction, strategic planning, and library learning. SceneCraft first models a scene graph as a blueprint, detailing the spatial relationships among assets in the scene. SceneCraft then writes Python scripts based on this graph, translating relationships into numerical constraints for asset layout. Next, SceneCraft leverages the perceptual strengths of vision-language foundation models like GPT-V to analyze rendered images and iteratively refine the scene. On top of this process, SceneCraft features a library learning mechanism that compiles common script functions into a reusable library, facilitating continuous self-improvement without expensive LLM parameter tuning. Our evaluation demonstrates that SceneCraft surpasses existing LLM-based agents in rendering complex scenes, as shown by its adherence to constraints and favorable human assessments. We also showcase the broader application potential of SceneCraft by reconstructing detailed 3D scenes from the Sintel movie and guiding a video generative model with generated scenes as intermediary control signal.

We have witnessed lately a rapid proliferation of advanced Large Language Models (LLMs) capable of generating high-quality text. While these LLMs have revolutionized text generation across various domains, they also pose significant risks to the information ecosystem, such as the potential for generating convincing propaganda, misinformation, and disinformation at scale. This paper offers a review of AI-generated text forensic systems, an emerging field addressing the challenges of LLM misuses. We present an overview of the existing efforts in AI-generated text forensics by introducing a detailed taxonomy, focusing on three primary pillars: detection, attribution, and characterization. These pillars enable a practical understanding of AI-generated text, from identifying AI-generated content (detection), determining the specific AI model involved (attribution), and grouping the underlying intents of the text (characterization). Furthermore, we explore available resources for AI-generated text forensics research and discuss the evolving challenges and future directions of forensic systems in an AI era.

Recognizing speaking in humans is a central task towards understanding social interactions. Ideally, speaking would be detected from individual voice recordings, as done previously for meeting scenarios. However, individual voice recordings are hard to obtain in the wild, especially in crowded mingling scenarios due to cost, logistics, and privacy concerns. As an alternative, machine learning models trained on video and wearable sensor data make it possible to recognize speech by detecting its related gestures in an unobtrusive, privacy-preserving way. These models themselves should ideally be trained using labels obtained from the speech signal. However, existing mingling datasets do not contain high quality audio recordings. Instead, speaking status annotations have often been inferred by human annotators from video, without validation of this approach against audio-based ground truth. In this paper we revisit no-audio speaking status estimation by presenting the first publicly available multimodal dataset with high-quality individual speech recordings of 33 subjects in a professional networking event. We present three baselines for no-audio speaking status segmentation: a) from video, b) from body acceleration (chest-worn accelerometer), c) from body pose tracks. In all cases we predict a 20Hz binary speaking status signal extracted from the audio, a time resolution not available in previous datasets. In addition to providing the signals and ground truth necessary to evaluate a wide range of speaking status detection methods, the availability of audio in REWIND makes it suitable for cross-modality studies not feasible with previous mingling datasets. Finally, our flexible data consent setup creates new challenges for multimodal systems under missing modalities.

Many a cinephile has asked themselves the question: What if Andrei Tarkovsky's Solaris, the avant-garde 1972 sci-fi classic about a widowed space explorer forced to grapple with his grief while on a mission to a mysterious planet, starred Adam Sandler and Carey Mulligan as a fracturing married couple, alongside Paul Dano as the voice of a giant benevolent space spider? And what if Isabella Rossellini were the leader of Czechoslovakia's space program, which somehow, in this universe's alternate version of political and technological history, was the best-equipped in the world to send a manned mission to the outer reaches of Jupiter? The result of that mashup might be something like Spaceman, an oddball psychological drama from the Swedish director Johan Renck, best known for a long résumé of music videos and lately for helming all five episodes of the acclaimed HBO miniseries Chernobyl. The script, adapted by Colby Day from the 2017 novel Spaceman of Bohemia by Jaroslav Kalfar, leaves many questions unanswered. If the mission of Sandler's character--the depressive, remote, and career-obsessed Jakub--is so significant to humanity's future, it isn't clear why he would have been sent into space all alone.

Maxwell Tani is known for his work on an obituary beat of sorts. A media reporter at Semafor, he always seems to be the first person to break news whenever something terrible happens for journalists at one outlet or another. He's been busy: According to one tabulation, more than 500 journalists were laid off just in January. A scroll through Tani's account on X surfaces a glut of executive memos, couched in corporate-speak, informing staff that they'll soon be laid off--at Business Insider, Engadget, the Messenger, Vice, and the Wall Street Journal. Sometimes he shares the news of an impending layoff before these memos even go out--and before employees have been informed. Slate spoke with Tani about what it's like to document the worst moments on the media beat, and how he feels about his place in the news-about-the-news ecosystem. We also tried to diagnose the ills of the industry--and find bright spots ahead.

This week, Adobe revealed an experimental audio AI tool to join its image-based ones in Photoshop. Described by the company as "an early-stage generative AI music generation and editing tool," Adobe's Project Music GenAI Control can create music (and other audio) from text prompts, which it can then fine-tune in the same interface. Adobe frames the Firefly-based technology as a creative ally that -- unlike generative audio experiments like Google's MusicLM -- goes a step further and skips the hassle of moving the output to external apps like Pro Tools, Logic Pro or GarageBand for editing. "Instead of manually cutting existing music to make intros, outros, and background audio, Project Music GenAI Control could help users to create exactly the pieces they need--solving workflow pain points end-to-end," Adobe wrote in an announcement blog post. The company suggests starting with text inputs like "powerful rock," "happy dance" or "sad jazz" as a foundation.

A sci-fi series has taken the number one spot on IMDB following the release of its second season - despite the show's'disappointing' debut in 2022. The first season of the video game adaptation was deemed a'one-hit' wonder' by viewers who felt the story was written by a'high schooler' and the graphics were'low budget CGI.' But Halo season two, released this month, now sits at number one in IDMB's list of top sci-fi TV series. The Paramount series has 7.2 stars and more than 81,000 votes - overtaking popular shows like Netflix's Stranger Things and Black Mirror. Halo also has an 89 percent on Rotten Tomatoes - a jump from season one's 61 percent rating.

One of the most frustrating things creatives have to deal with is making the same or similar edits over and over again to files. Herein lies a perfect application for artificial intelligence, and Winxvideo AI gives you the assistance you need to save you time and energy. During our Sitewide Sale, you can get it for an extra 20% off at just 23.99 with code ENJOY20 through March 10th only. This one-stop video toolkit contains a host of traditional video conversion tools and is amplified by AI. You can use the built-in AI to convert videos quickly, compress large files, upscale videos/images, stabilize shaky videos, and convert from 24fps to 60/120fps.

The Jake Gyllenhaal-starring "Road House" remake is facing two major hurdles ahead of its release. Jake Gyllenhaal stars in the remake of "Road House," which is facing both a legal battle and boycott from its director. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Patrick Swayze starred in the original, released in 1989. It was successful upon its original release and gained cult status over the years thanks to cable television. According to the filing, Amazon "repeatedly set and emphasized November 10, 2023 as their self-imposed deadline to complete the 2024 Remake -- the very day before Hill's Termination was to take effect on November 11, 2023. "Hill is further informed and believes and based thereon alleges that Defendants went so far as to take extreme measures to try to meet this November 10, 2023 deadline, at considerable additional cost, including by resorting to the use of AI (Artificial Intelligence) during the 2023 strike of the Screen Actor's Guild ("SAG") to replicate the voices of the 2024 Remake's actors for purposes of ADR (Automatic Dialogue Replacement), all in knowing violation of the collective bargaining agreements of both SAG and the Director's Guild of America (DGA) to which Defendants were signatories.