Deep neural networks remain highly vulnerable to adversarial examples, and most defenses collapse once gradients can be reliably estimated. We identify gradient consensus--the tendency of randomized transformations to yield aligned gradients--as a key driver of adversarial transferability. Attackers exploit this consensus to construct perturbations that remain effective across transformations. We introduce DRIFT (Divergent Response in Filtered Transformations), a stochastic ensemble of lightweight, learnable filters trained to actively disrupt gradient consensus. Unlike prior randomized defenses that rely on gradient masking, DRIFT enforces gradient dissonance by maximizing divergence in Jacobian-and logit-space responses while preserving natural predictions. Our contributions are threefold: (i) we formalize gradient consensus and provide a theoretical analysis linking consensus to transferability; (ii) we propose a consensus-divergence training strategy combining prediction consistency, Jacobian separation, logit-space separation, and adversarial robustness; and (iii) we show that DRIFT achieves substantial robustness gains on ImageNet across CNNs and Vision Transformers, outperforming state-of-the-art preprocessing, adversarial training, and diffusion-based defenses under adaptive white-box, transfer-based, and gradient-free attacks. DRIFT delivers these improvements with negligible runtime and memory cost, establishing gradient divergence as a practical and generalizable principle for adversarial defense. An adaptive adversary can approximate gradients across these transformations (e.g., via Expectation over Transformation (EoT) (Athalye et al., 2018)) and exploit the consistent directions that emerge, leading to transferable adversarial examples. This vulnerability stems not from insufficient randomness, but from the fact that most stochastic defenses still preserve a coherent, low-variance surrogate gradient landscape. We argue that true robustness requires not masking gradients, but destroying their alignment.

Smart aquaculture systems depend on rich environmental data streams to protect fish welfare, optimize feeding, and reduce energy use. Yet public datasets that describe the air surrounding indoor tanks remain scarce, limiting the development of forecasting and anomaly-detection tools that couple head-space conditions with water-quality dynamics. We therefore introduce AQUAIR, an open-access public dataset that logs six Indoor Environmental Quality (IEQ) variables--air temperature, relative humidity, carbon dioxide, total volatile organic compounds, PM2.5 and PM10--inside a fish aquaculture facility in Amghass, Azrou, Morocco. A single Awair HOME monitor sampled every five minutes from 14 October 2024 to 9 January 2025, producing more than 23,000 time-stamped observations that are fully quality-controlled and publicly archived on Figshare. We describe the sensor placement, ISO-compliant mounting height, calibration checks against reference instruments, and an open-source processing pipeline that normalizes timestamps, interpolates short gaps, and exports analysis-ready tables. Exploratory statistics show stable conditions (median CO2 = 758 ppm; PM2.5 = 12 micrograms/m3) with pronounced feeding-time peaks, offering rich structure for short-horizon forecasting, event detection, and sensor drift studies. AQUAIR thus fills a critical gap in smart aquaculture informatics and provides a reproducible benchmark for data-centric machine learning curricula and environmental sensing research focused on head-space dynamics in recirculating aquaculture systems.

With the rise of generative text-to-speech models, distinguishing between real and synthetic speech has become challenging, especially for Arabic that have received limited research attention. Most spoof detection efforts have focused on English, leaving a significant gap for Arabic and its many dialects. In this work, we introduce the first multi-dialect Arabic spoofed speech dataset. To evaluate the difficulty of the synthesized audio from each model and determine which produces the most challenging samples, we aimed to guide the construction of our final dataset either by merging audios from multiple models or by selecting the best-performing model, we conducted an evaluation pipeline that included training classifiers using two approaches: modern embedding-based methods combined with classifier heads; classical machine learning algorithms applied to MFCC features; and the RawNet2 architecture. The pipeline further incorporated the calculation of Mean Opinion Score based on human ratings, as well as processing both original and synthesized datasets through an Automatic Speech Recognition model to measure the Word Error Rate. Our results demonstrate that FishSpeech outperforms other TTS models in Arabic voice cloning on the Casablanca corpus, producing more realistic and challenging synthetic speech samples. However, relying on a single TTS for dataset creation may limit generalizability.

As large language models (LLMs) expand multilingual capabilities, questions remain about the equity of their performance across languages. While many communities stand to benefit from AI systems, the dominance of English in training data risks disadvantaging non-English speakers. To test the hypothesis that such data disparities may affect model performance, this study compares two monolingual BERT models: one trained and tested entirely on Swahili data, and another on comparable English news data. To simulate how multilingual LLMs process non-English queries through internal translation and abstraction, we translated the Swahili news data into English and evaluated it using the English-trained model. This approach tests the hypothesis by evaluating whether translating Swahili inputs for evaluation on an English model yields better or worse performance compared to training and testing a model entirely in Swahili, thus isolating the effect of language consistency versus cross-lingual abstraction. The results prove that, despite high-quality translation, the native Swahili-trained model performed better than the Swahili-to-English translated model, producing nearly four times fewer errors: 0.36% vs. 1.47% respectively. This gap suggests that translation alone does not bridge representational differences between languages and that models trained in one language may struggle to accurately interpret translated inputs due to imperfect internal knowledge representation, suggesting that native-language training remains important for reliable outcomes. In educational and informational contexts, even small performance gaps may compound inequality. Future research should focus on addressing broader dataset development for underrepresented languages and renewed attention to multilingual model evaluation, ensuring the reinforcing effect of global AI deployment on existing digital divides is reduced.

Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets. To address this gap, we introduce PEARL, a large-scale Arabic multimodal dataset and benchmark explicitly designed for cultural understanding. Constructed through advanced agentic workflows and extensive human-in-the-loop annotations by 37 annotators from across the Arab world, PEARL comprises over 309K multimodal examples spanning ten culturally significant domains covering all Arab countries. We further provide two robust evaluation benchmarks (PEARL and PEARL-LITE) along with a specialized subset (PEARL-X) explicitly developed to assess nuanced cultural variations. Comprehensive evaluations on state-of-the-art open and proprietary LVLMs demonstrate that reasoning-centric instruction alignment substantially improves models' cultural grounding compared to conventional scaling methods. PEARL establishes a foundational resource for advancing culturally-informed multimodal modeling research. All datasets and benchmarks are publicly available.

British blogger Pete North was arrested by Yorkshire police for posting a meme saying 'F--- Hamas' on social media, accused of stirring up racial hatred.

Taylor, your album should be'Life of a Callgirl'. KENNEDY's appalled take on Swift's new record... and its ultra-vivid sex shout outs for Travis the Sasquatch The truth about Keith Urban's guitarist'other woman' Maggie Baugh revealed amid Nicole Kidman divorce How I look like this at 62. I've lost 5 stone fast, 20 years off my biological age and wear size 8... without weight-loss jabs. Hollywood A-listers pay me $50,000 to cure their drug addicted nepo-babies because they can't afford for these secrets to go public Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Fans erupt at Taylor Swift's'dig' at Travis Kelce's ex Kayla Nicole in wild The Life of a Showgirl track Trump dollar coin design released by Treasury... and it's inspired by an iconic political photo Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split Trump appears alongside Melania at dinner hosted by JD Vance and Usha after'disappearance' rumors Top plastic surgeons reveal secrets behind Taylor Swift's'changing' face: 'It is looking very full' I'm a woman with autism... here are the signs you might be masking, even from yourself Cake-faced 90s sitcom star looks unrecognizable as she ditches the heavy eyeshadow for an LA errand run can you guess who?

The University of North Carolina has placed Asian and Middle Eastern Studies professor Dwayne Dixon on leave after his ties to the far-left gun club Redneck Revolt were exposed.

The truth about Keith Urban's guitarist'other woman' Maggie Baugh revealed amid Nicole Kidman divorce Top plastic surgeons reveal secrets behind Taylor Swift's'changing' face: 'It is looking very full' Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection Hollywood A-listers pay me $50,000 to cure their drug addicted nepo-babies because they can't afford for these secrets to go public Trump dollar coin design released by Treasury... and it's inspired by an iconic political photo I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Fans erupt at Taylor Swift's'dig' at Travis Kelce's ex Kayla Nicole in wild The Life of a Showgirl track Taylor, your album should be'Life of a Callgirl'. KENNEDY's appalled take on Swift's new record... and its ultra-vivid sex shout outs for Travis the Sasquatch I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with I'm a woman with autism... here are the signs you might be masking, even from yourself Cake-faced 90s sitcom star looks unrecognizable as she ditches the heavy eyeshadow for an LA errand run can you guess who?

Why is Israel still in southern Lebanon? A war to shape Lebanon's future At least one person has been killed in an Israeli strike in southern Lebanon, according to the state-run National News Agency (NNA), as near-daily attacks by Israel continue despite a November ceasefire. The attack on Monday hit an excavator in the Shamsiyah area of Sohmor in the Bekaa Valley, killing its driver. Footage on social media, verified by Al Jazeera, showed emergency responders carrying the victim away on a stretcher. One drone targeted the town of Aitaroun on Monday afternoon while another bombed a house in Houmin al-Fauqa. No casualties were reported in those attacks.