The football is over, the turkey is picked clean, and the fam is heading home. Now, it's time to shop, shop, shop, and we have the absolute best Black Friday deals of 2024 for you. The WIRED team has been diligently digging to find the bargains worth your while, and we'll be here, working shifts for the next four days, to bring you every deal you need to know about. So grab a beverage, a turkey sandwich, and your wallet or purse. For Black Friday, we cross-reference our buying guide recommendations with the latest sale prices to find the absolute best Black Friday deals on the gadgetry worth owning. An actual person from the WIRED Reviews team has tested every product we list in our deals coverage, and we don't recommend anything we don't like. We always strive to find deals at their best price ever, or very close to it (some match previous discounts, but we have never seen them lower unless stated). Updated November 30: We've checked prices, removed dead deals, and added new ones.

Black Friday deals don't usually stick around, but this one did. It's almost like we knew you were still stuck on Windows 10 or the free version of Windows 11--missing out on the AI assistant, advanced multitasking, and exclusive security features. You're being given a second chance, so don't pass it up: Buy Windows 11 Pro now for 19.97 (reg. Less than 50 codes are left after Black Friday, but there's still time to upgrade with this 92 percent discount. If you're coming from Windows 10, you'll notice the redesigned look first.

In the field of Recommender Systems (RS), neural collaborative filtering represents a significant milestone by combining matrix factorization and deep neural networks to achieve promising results. Traditional methods like matrix factorization often rely on linear models, limiting their capability to capture complex interactions between users, items, and contexts. This limitation becomes particularly evident with high-dimensional datasets due to their inability to capture relationships among users, items, and contextual factors. Unsupervised learning and dimension reduction tasks utilize autoencoders, neural network-based models renowned for their capacity to encode and decode data. Autoencoders learn latent representations of inputs, reducing dataset size while capturing complex patterns and features. In this paper, we introduce a framework that combines neural contextual matrix factorization with autoencoders to predict user ratings for items. We provide a comprehensive overview of the framework's design and implementation. To evaluate its performance, we conduct experiments on various real-world datasets and compare the results against state-of-the-art approaches. We also extend the concept of conformal prediction to prediction rating and introduce a Conformal Prediction Rating (CPR). For RS, we define the nonconformity score, a key concept of conformal prediction, and demonstrate that it satisfies the exchangeability property.

Movie posters are not just decorative; they are meticulously designed to capture the essence of a movie, such as its genre, storyline, and tone/vibe. For decades, movie posters have graced cinema walls, billboards, and now our digital screens as a form of digital posters. Movie genre classification plays a pivotal role in film marketing, audience engagement, and recommendation systems. Previous explorations into movie genre classification have been mostly examined in plot summaries, subtitles, trailers and movie scenes. Movie posters provide a pre-release tantalizing glimpse into a film's key aspects, which can ignite public interest. In this paper, we presented the framework that exploits movie posters from a visual and textual perspective to address the multilabel movie genre classification problem. Firstly, we extracted text from movie posters using an OCR and retrieved the relevant embedding. Next, we introduce a cross-attention-based fusion module to allocate attention weights to visual and textual embedding. In validating our framework, we utilized 13882 posters sourced from the Internet Movie Database (IMDb). The outcomes of the experiments indicate that our model exhibited promising performance and outperformed even some prominent contemporary architectures.

When it comes to new tech, 50 doesn't get you a lot-- except perhaps during Black Friday sales. Surprisingly, quite a few of the smaller electronics and accessories we recommend are currently on sale right now for less than 50. These deals include picks from our guides to accessories guides, portable batteries, budget earbuds and smart speakers. Everything on this list has earned the Engadget nod of approval -- like Anker's Nano charger ( 13), Belkin's AirTag holder ( 15) and PopSocket phone grips ( 15). These picks come from stuff we tested for our reviews and buying guides or from personal use and brands we know to be reputable -- so you don't have to guess whether these Black Friday tech deals are worth your (less than) 50. Amazon Echo Pop (2023) for 18 ( 22 off): Amazon's smallest Echo will fit in any room in your home, so Alexa can add things to your shopping list, set a timer, or answer questions (like "What's a bomb cyclone?" or "Who is Penelope Cruz married to?") from anywhere. Anker Nano Charger 30W USB-C for 13 ( 7 off): This compact 30-watt wall charger is smaller than others of its wattage and can speedily juice up an iPhone or Android handset.

According to my imprecise calculations, there are approximately a zillion speakers on sale for Black Friday. So how can a mere human know which ones are worthy and which will make music sound like it's emanating from a tin can? We've done our part by testing and reviewing dozens of different options and putting the best of the lot into handy buying guides for smart speakers, soundbars and portable Bluetooth speakers. This is an even more rarefied list, made up of the speakers we recommend -- that are also seeing notable discounts. If you need a new way to listen to music, a soundbar to help suss out the dialogue on your TV or a smart speaker to fulfill your demands, read on for the best Black Friday speaker deals. Portable Bluetooth speakers make it easy for you to bring the music where plugs don't reach -- a picnic, the front stoop, an aimless wander along the North Country Trail.

During the trial the court heard how the former East of England Ambulance Service call handler had used stickers on his work ID card to hide his more junior role. Kadolski told the woman he raped that he was a paramedic, and she said he had used pictures of himself wearing a paramedic's uniform on his online dating profile. "I remember his photos in paramedic gear... you wouldn't assume that [emergency workers] would do anything bad," she said. Under the Health Professions Order 2001, it is an offence for a person to use a health title to which they are not entitled, which includes paramedic. However, the victim has called for a new law, similar to what exists for police officers, to specifically stop people being able to pose as a paramedic.

As artificial intelligence (AI) becomes increasingly embedded in daily life, designing intuitive, trustworthy, and emotionally resonant AI-human interfaces has emerged as a critical challenge. This editorial introduces a Special Issue that explores the psychology of AI experience design, focusing on how interfaces can foster seamless collaboration between humans and machines. Drawing on insights from diverse fields (healthcare, consumer technology, workplace dynamics, and cultural sector), the papers in this collection highlight the complexities of trust, transparency, and emotional sensitivity in human-AI interaction. Key themes include designing AI systems that align with user perceptions and expectations, overcoming resistance through transparency and trust, and framing AI capabilities to reduce user anxiety. By synthesizing findings from eight diverse studies, this editorial underscores the need for AI interfaces to balance efficiency with empathy, addressing both functional and emotional dimensions of user experience. Ultimately, it calls for actionable frameworks to bridge research and practice, ensuring that AI systems enhance human lives through thoughtful, human-centered design.

Under stringent privacy constraints, whether federated recommendation systems can achieve group fairness remains an inadequately explored question. Taking gender fairness as a representative issue, we identify three phenomena in federated recommendation systems: performance difference, data imbalance, and preference disparity. We discover that the state-of-the-art methods only focus on the first phenomenon. Consequently, their imposition of inappropriate fairness constraints detrimentally affects the model training. Moreover, due to insufficient sensitive attribute protection of existing works, we can infer the gender of all users with 99.90% accuracy even with the addition of maximal noise. In this work, we propose Privacy-Preserving Orthogonal Aggregation (PPOA), which employs the secure aggregation scheme and quantization technique, to prevent the suppression of minority groups by the majority and preserve the distinct preferences for better group fairness. PPOA can assist different groups in obtaining their respective model aggregation results through a designed orthogonal mapping while keeping their attributes private. Experimental results on three real-world datasets demonstrate that PPOA enhances recommendation effectiveness for both females and males by up to 8.25% and 6.36%, respectively, with a maximum overall improvement of 7.30%, and achieves optimal fairness in most cases. Extensive ablation experiments and visualizations indicate that PPOA successfully maintains preferences for different gender groups.

Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of two-tower models is that they learn a pair-agnostic representation of users and items. In contrast, pair-wise representations either scale poorly due to their quadratic complexity or are too restrictive on the candidate pairs to rank. To address these issues, we introduce Context-based Graph Neural Networks (ContextGNNs), a novel deep learning architecture for link prediction in recommendation systems. The method employs a pair-wise representation technique for familiar items situated within a user's local subgraph, while leveraging two-tower representations to facilitate the recommendation of exploratory items. A final network then predicts how to fuse both pair-wise and two-tower recommendations into a single ranking of items. We demonstrate that ContextGNN is able to adapt to different data characteristics and outperforms existing methods, both traditional and GNN-based, on a diverse set of practical recommendation tasks, improving performance by 20% on average.