The rapid adoption of generative AI-powered search engines like ChatGPT, Perplexity, and Gemini is fundamentally reshaping information retrieval, moving from traditional ranked lists to synthesized, citation-backed answers. This shift challenges established Search Engine Optimization (SEO) practices and necessitates a new paradigm, which we term Generative Engine Optimization (GEO). This paper presents a comprehensive comparative analysis of AI Search and traditional web search (Google). Through a series of large-scale, controlled experiments across multiple verticals, languages, and query paraphrases, we quantify critical differences in how these systems source information. Our key findings reveal that AI Search exhibit a systematic and overwhelming bias towards Earned media (third-party, authoritative sources) over Brand-owned and Social content, a stark contrast to Google's more balanced mix. We further demonstrate that AI Search services differ significantly from each other in their domain diversity, freshness, cross-language stability, and sensitivity to phrasing. Based on these empirical results, we formulate a strategic GEO agenda. We provide actionable guidance for practitioners, emphasizing the critical need to: (1) engineer content for machine scannability and justification, (2) dominate earned media to build AI-perceived authority, (3) adopt engine-specific and language-aware strategies, and (4) overcome the inherent "big brand bias" for niche players. Our work provides the foundational empirical analysis and a strategic framework for achieving visibility in the new generative search landscape.

Generative AI paraphrased text can be used for copyright infringement and the AI paraphrased content can deprive substantial revenue from original content creators. Despite this recent surge of malicious use of generative AI, there are few academic publications that research this threat. In this article, we demonstrate the ability of pattern-based similarity detection for AI paraphrased news recognition. We propose an algorithmic scheme, which is not limited to detect whether an article is an AI paraphrase, but, more importantly, to identify that the source of infringement is the ChatGPT. The proposed method is tested with a benchmark dataset specifically created for this task that incorporates real articles from BBC, incorporating a total of 2,224 articles across five different news categories, as well as 2,224 paraphrased articles created with ChatGPT. Results show that our pattern similarity-based method, that makes no use of deep learning, can detect ChatGPT assisted paraphrased articles at percentages 96.23% for accuracy, 96.25% for precision, 96.21% for sensitivity, 96.25% for specificity and 96.23% for F1 score.

Recommender systems often benefit from complex feature embeddings and deep learning algorithms, which deliver sophisticated recommendations that enhance user experience, engagement, and revenue. However, these methods frequently reduce the interpretability and transparency of the system. In this research, we develop a systematic application, adaptation, and evaluation of deletion diagnostics in the recommender setting. The method compares the performance of a model to that of a similar model trained without a specific user or item, allowing us to quantify how that observation influences the recommender, either positively or negatively. To demonstrate its model-agnostic nature, the proposal is applied to both Neural Collaborative Filtering (NCF), a widely used deep learning-based recommender, and Singular Value Decomposition (SVD), a classical collaborative filtering technique. Experiments on the MovieLens and Amazon Reviews datasets provide insights into model behavior and highlight the generality of the approach across different recommendation paradigms.

Mathematical reasoning is critical for tasks such as precise distance and area computations, trajectory estimations, and spatial analysis in unmanned aerial vehicle (UAV) based remote sensing, yet current vision-language models (VLMs) have not been adequately tested in this domain. To address this gap, we introduce AVI-Math, the first benchmark to rigorously evaluate multimodal mathematical reasoning in aerial vehicle imagery, moving beyond simple counting tasks to include domain-specific knowledge in areas such as geometry, logic, and algebra. The dataset comprises 3,773 high-quality vehicle-related questions captured from UAV views, covering 6 mathematical subjects and 20 topics. The data, collected at varying altitudes and from multiple UAV angles, reflects real-world UAV scenarios, ensuring the diversity and complexity of the constructed mathematical problems. In this paper, we benchmark 14 prominent VLMs through a comprehensive evaluation and demonstrate that, despite their success on previous multimodal benchmarks, these models struggle with the reasoning tasks in AVI-Math. Our detailed analysis highlights significant limitations in the mathematical reasoning capabilities of current VLMs and suggests avenues for future research. Furthermore, we explore the use of Chain-of-Thought prompting and fine-tuning techniques, which show promise in addressing the reasoning challenges in AVI-Math. Our findings not only expose the limitations of VLMs in mathematical reasoning but also offer valuable insights for advancing UAV-based trustworthy VLMs in real-world applications. The code, and datasets will be released at https://github.com/VisionXLab/avi-math

This paper proposes a framework for modeling multimodal conversational turn organization via the proposition of correlations between language and interactive gestures, based on analysis as to how pragmatic frames are conceptualized and evoked by communicators. As a means to provide evidence for the analysis, we developed an annotation methodology to enrich a multimodal dataset (annotated for semantic frames) with pragmatic frames modeling conversational turn organization. Although conversational turn organization has been studied by researchers from diverse fields, the specific strategies, especially gestures used by communicators, had not yet been encoded in a dataset that can be used for machine learning. To fill this gap, we enriched the Frame2 dataset with annotations of gestures used for turn organization. The Frame2 dataset features 10 episodes from the Brazilian TV series Pedro Pelo Mundo annotated for semantic frames evoked in both video and text. This dataset allowed us to closely observe how communicators use interactive gestures outside a laboratory, in settings, to our knowledge, not previously recorded in related literature. Our results have confirmed that communicators involved in face-to-face conversation make use of gestures as a tool for passing, taking and keeping conversational turns, and also revealed variations of some gestures that had not been documented before. We propose that the use of these gestures arises from the conceptualization of pragmatic frames, involving mental spaces, blending and conceptual metaphors. In addition, our data demonstrate that the annotation of pragmatic frames contributes to a deeper understanding of human cognition and language.

The large language model (LLM) powered recommendation paradigm has been proposed to address the limitations of traditional recommender systems, which often struggle to handle cold start users or items with new IDs. Despite its effectiveness, this study uncovers that LLM empowered recommender systems are vulnerable to reconstruction attacks that can expose both system and user privacy. To examine this threat, we present the first systematic study on inversion attacks targeting LLM empowered recommender systems, where adversaries attempt to reconstruct original prompts that contain personal preferences, interaction histories, and demographic attributes by exploiting the output logits of recommendation models. We reproduce the vec2text framework and optimize it using our proposed method called Similarity Guided Refinement, enabling more accurate reconstruction of textual prompts from model generated logits. Extensive experiments across two domains (movies and books) and two representative LLM based recommendation models demonstrate that our method achieves high fidelity reconstructions. Specifically, we can recover nearly 65 percent of the user interacted items and correctly infer age and gender in 87 percent of the cases. The experiments also reveal that privacy leakage is largely insensitive to the victim model's performance but highly dependent on domain consistency and prompt complexity. These findings expose critical privacy vulnerabilities in LLM empowered recommender systems.

HGTV's Erin Napier erupts at fans after being slammed for refusing to'celebrate' Charlie Kirk's death Truth about America's murder hotspots... as map reveals surprising cities Trump may send National Guard City of vanishing children: Dark truth behind the THOUSANDS of missing kids in Rust Belt town... and the underworld they are plunged into Monkees musician Bobby Hart who wrote the band's theme and Last Train To Clarksville dies at 86 I didn't air any dirty laundry in public - my conscience is clear, says Prince Harry during visit to Ukraine: Duke reveals he wants to spend more time in the UK in the next year as'the focus really has to be on my dad' FBI tried to hide trans identity of Charlie Kirk suspect's lover after his chilling four-word response to investigators I've been lying to my husband about the thing he loves most. If I come clean, he'll be humiliated: DEAR JANE My HOA from hell fined me $1,000 per day for the pettiest issue imaginable inside my $600k home... then I realized they were spying on me Teen arrested'destroying' Charlie Kirk memorial as chilling copycat fantasy exposed Hollywood insiders lay bare'intimidation' tactics by woke celebrities branded worse than the Ku Klux Klan: 'Everyone is living in fear' NFL fans left in disbelief as Russell Wilson launches'mind blowing' touchdown pass for New York Giants Islanders claim they know the sinister truth about Amelia Earhart... and demand the proof is finally released Urgent warning as toxic fumes on major airlines' flights cause devastating brain injuries I dropped from a size 20 to a size 12 in five months - these'healthy' foods were making me overweight Who are the shortest actresses in Hollywood? Emotional Tucker Carlson reveals'close call' on his life as he breaks silence on Charlie Kirk: 'We're in a civil war' People are just realizing that they're pronouncing the name of America's biggest holiday wrong The three-word phrase to get people to listen'instantly,' according to a public speaking expert Capturing people's attention during a presentation, or in any crowded room, is often half the battle, and one many fail to win. Now, a public speaking expert has shared a three-word phrase he claimed will get people listening to you'instantly.' John Bowe, a speech trainer, said that starting with'Imagine this scenario...' will have the room perk up and pay attention. 'It works every time,' Bowe wrote for CNBC, breaking down how each word is highly engaging.

Grok claimed the location was Trafalgar Square. Grok claimed the location was Trafalgar Square. Musk's Grok AI bot falsely suggests police misrepresented footage of far-right rally in London The Metropolitan police has had to counter false suggestions by the artificial intelligence on Elon Musk's X platform that the force passed off footage from 2020 as being from Saturday's far-right rally in the city. The claim by the chatbot Grok was in answer to an X user's query about where and when footage of police clashing with crowds was filmed. Police seek man who called for Keir Starmer to be'assassinated' at far-right rally Grok, which has had a track record of giving false and misleading answers, replied: "This footage appears to be from an anti-lockdown protest in London's Trafalgar Square on 26 September 2020, during clashes between demonstrators and police over Covid restrictions."

FreeVPN.One Chrome extension with over 100,000 installs secretly captured screenshots of users' browsing sessions including, bank logins and private documents.

Captured in South Africa's Londolozi reserve, freezes the tense moment when a male leopard must leap away after mating to avoid the female's claws. Breakthroughs, discoveries, and DIY tips sent every weekday. Our world is vibrant and colorful, but seeing it in black and white can offer a new perspective. "Our mission is to celebrate the timeless power of black-and-white photography," the competition's organizers said in a statement. "This year's winners remind us that monochrome has the capacity to illuminate not only form and light, but also the most urgent human stories."