The rapid advancement of language models such as This paper presents a robust ensemble approach GPT (Radford et al., 2019) and BERT (Devlin et al., for detecting AI-generated content, with strong 2019) has increased machine-generated content, performance across both English and multilingual raising significant concerns about misinformation tasks. However, significant opportunities remain and academic integrity. Identifying AI-generated for improving model generalization and addressing text becomes more challenging in multilingual contexts, data imbalance, which will be crucial for future where linguistic diversity adds further complexity advancements in this field. The following sections to model generalization. While existing will discuss the dataset, methodology, results, a approaches perform well in English, their effectiveness detailed analysis of the findings, and conclusions decreases when applied to languages with drawn from this study.

With the rapid growth of online information, the spread of fake news has become a serious social challenge. In this study, we propose a novel detection framework based on Large Language Models (LLMs) to identify and classify fake news by integrating textual statistical features and deep semantic features. Our approach utilizes the contextual understanding capability of the large language model for text analysis and introduces a hybrid attention mechanism to focus on feature combinations that are particularly important for fake news identification. Extensive experiments on the WELFake news dataset show that our model significantly outperforms existing methods, with a 1.5\% improvement in F1 score. In addition, we assess the interpretability of the model through attention heat maps and SHAP values, providing actionable insights for content review strategies. Our framework provides a scalable and efficient solution to deal with the spread of fake news and helps build a more reliable online information ecosystem.

The filmmakers behind The Brutalist, a likely Oscar contender currently being distributed by A24, used AI to alter actor's dialogue and create images used in the film's epilogue, the film's editor Dávid Jancsó shared in an interview with RedShark News. The epic drama follows a fictional Hungarian architect (as played by Adrien Brody) who struggles to make art under the fickle system of American capitalism (and the weirdos that run it). To make Brody and his costar Felicity Jones' Hungarian pronunciation as accurate as possible, Jancsó says the production used AI from a company called Respeecher to alter the actor's speech. Respeecher was able to adjust the actor's vocals to make them match a native Hungarian speaker's pronunciation, though Jancsó says the process didn't do anything you couldn't achieve with traditional dialogue editing. "You can do this in ProTools yourself, but we had so much dialogue in Hungarian that we really needed to speed up the process, otherwise we'd still be in post."

CNN's Jake Tapper warned on Monday that the country was about to enter an "era of deepfakes and all sorts of disinformation" under President Trump, while discussing the Big Tech presence at his inauguration. "We're about to enter an era of deepfakes, and all sorts of misinformation and the degree to which those five gentlemen play a role or do not play a role, will be pivotal in terms of where the American people are four years from now, in terms of understanding what is true and what is false," Tapper said before Trump took the oath of office. Meta CEO Mark Zuckerberg, Tesla founder Elon Musk, Amazon CEO Jeff Bezos, Apple CEO Tim Cook and Google CEO Sundar Pichai were among the tech giants attending the inauguration. Tapper said those five people "control so much of the information that we receive, so much is in their hands when it comes to ascertaining, monitoring, or refusing to monitor what is real, what is not real." CNN's Jake Tapper speaks on CNN on Jan. 12, 2025.

The use of artificial intelligence could become a ferocious battleground during movie awards season, as at least two major contenders were revealed to have used voice-cloning to enhance actors' performances. In an interview with moving-image tech publication Red Shark News, The Brutalist editor Dávid Jancsó said that, in an effort to create Hungarian dialogue so perfect "that not even locals will spot any difference", Jancsó fed lead actors Adrien Brody and Felicity Jones's voices into AI software, as well as his own. In the film, Brody plays Jewish-Hungarian architect László Tóth, who emigrates to the US after the second world war, and Jones is his wife Erzsébet. Jancsó, a Hungarian speaker, said that while Brody's mother was an émigré from Hungary in real life, "coaching" and re-recording via ADR (automated dialogue replacement) with both the original actors and stand-ins "just didn't work". Jancsó said he then employed an AI tool developed by Respeecher, a Ukraine-based company who were previously involved in the "cloning" of the voice of James Earl Jones for the TV series Obi-Wan Kenobi, to add individual sounds and letters to both Brody and Jones's Hungarian-language dialogue.

Existing full text datasets of U.S. public domain newspapers do not recognize the often complex layouts of newspaper scans, and as a result the digitized content scrambles texts from articles, headlines, captions, advertisements, and other layout regions. OCR quality can also be low. This study develops a novel, deep learning pipeline for extracting full article texts from newspaper images and applies it to the nearly 20 million scans in Library of Congress's public domain Chronicling America collection. The pipeline includes layout detection, legibility classification, custom OCR, and association of article texts spanning multiple bounding boxes. To achieve high scalability, it is built with efficient architectures designed for mobile phones.

Score-based diffusion models (SBDMs) have achieved state-of-the-art results in image generation. In this paper, we propose a Non-isotropic Gaussian Diffusion Model (NGDM) for image editing, which requires editing the source image while preserving the image regions irrelevant to the editing task. We construct NGDM by adding independent Gaussian noises with different variances to different image pixels. We propose to reverse the diffusion by designing a sampling method that starts at different time for different pixels for denoising to generate images using the pre-trained isotropic Gaussian diffusion model. Experimental results show that NGDM achieves state-of-the-art performance for image editing tasks, considering the trade-off between the fidelity to the source image and alignment with the desired editing target.

Current definitions of Information Science are inadequate to comprehensively describe the nature of its field of study and for addressing the problems that are arising from intelligent technologies. The ubiquitous rise of artificial intelligence applications and their impact on society demands the field of Information Science acknowledge the socio-technical nature of these technologies. Previous definitions of Information Science over the last six decades have inadequately addressed the environmental, human, and social aspects of these technologies. This perspective piece advocates for an expanded definition of Information Science that fully includes the socio-technical impacts information has on the conduct of research in this field. Proposing an expanded definition of Information Science that includes the socio-technical aspects of this field should stimulate both conversation and widen the interdisciplinary lens necessary to address how intelligent technologies may be incorporated into society and our lives more fairly.

Automating analog and radio-frequency (RF) circuit design using machine learning (ML) significantly reduces the time and effort required for parameter optimization. This study explores supervised ML-based approaches for designing circuit parameters from performance specifications across various circuit types, including homogeneous and heterogeneous designs. By evaluating diverse ML models, from neural networks like transformers to traditional methods like random forests, we identify the best-performing models for each circuit. Our results show that simpler circuits, such as low-noise amplifiers, achieve exceptional accuracy with mean relative errors as low as 0.3% due to their linear parameter-performance relationships. In contrast, complex circuits, like power amplifiers and voltage-controlled oscillators, present challenges due to their non-linear interactions and larger design spaces. For heterogeneous circuits, our approach achieves an 88% reduction in errors with increased training data, with the receiver achieving a mean relative error as low as 0.23%, showcasing the scalability and accuracy of the proposed methodology. Additionally, we provide insights into model strengths, with transformers excelling in capturing non-linear mappings and k-nearest neighbors performing robustly in moderately linear parameter spaces, especially in heterogeneous circuits with larger datasets. This work establishes a foundation for extending ML-driven design automation, enabling more efficient and scalable circuit design workflows.

Personalized news headline generation, aiming at generating user-specific headlines based on readers' preferences, burgeons a recent flourishing research direction. Existing studies generally inject a user interest embedding into an encoderdecoder headline generator to make the output personalized, while the factual consistency of headlines is inadequate to be verified. In this paper, we propose a framework Fact-Preserved Personalized News Headline Generation (short for FPG), to prompt a tradeoff between personalization and consistency. In FPG, the similarity between the candidate news to be exposed and the historical clicked news is used to give different levels of attention to key facts in the candidate news, and the similarity scores help to learn a fact-aware global user embedding. Besides, an additional training procedure based on contrastive learning is devised to further enhance the factual consistency of generated headlines. Extensive experiments conducted on a real-world benchmark PENS validate the superiority of FPG, especially on the tradeoff between personalization and factual consistency.