The rapid advancement of high-quality image generation models based on AI has generated a deluge of anime illustrations. Recommending illustrations to users within massive data has become a challenging and popular task. However, existing anime recommendation systems have focused on text features but still need to integrate image features. In addition, most multi-modal recommendation research is constrained by tightly coupled datasets, limiting its applicability to anime illustrations. We propose the User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style (UMAIR-FPS) to tackle these gaps. In the feature extract phase, for image features, we are the first to combine image painting style features with semantic features to construct a dual-output image encoder for enhancing representation. For text features, we obtain text embeddings based on fine-tuning Sentence-Transformers by incorporating domain knowledge that composes a variety of domain text pairs from multilingual mappings, entity relationships, and term explanation perspectives, respectively. In the multi-modal fusion phase, we novelly propose a user-aware multi-modal contribution measurement mechanism to weight multi-modal features dynamically according to user features at the interaction level and employ the DCN-V2 module to model bounded-degree multi-modal crosses effectively.

While myoelectric control has recently become a focus of increased research as a possible flexible hands-free input modality, current control approaches are prone to inadvertent false activations in real-world conditions. In this work, a novel myoelectric control paradigm -- on-demand myoelectric control -- is proposed, designed, and evaluated, to reduce the number of unrelated muscle movements that are incorrectly interpreted as input gestures . By leveraging the concept of wake gestures, users were able to switch between a dedicated control mode and a sleep mode, effectively eliminating inadvertent activations during activities of daily living (ADLs). The feasibility of wake gestures was demonstrated in this work through two online ubiquitous EMG control tasks with varying difficulty levels; dismissing an alarm and controlling a robot. The proposed control scheme was able to appropriately ignore almost all non-targeted muscular inputs during ADLs (>99.9%) while maintaining sufficient sensitivity for reliable mode switching during intentional wake gesture elicitation. These results highlight the potential of wake gestures as a critical step towards enabling ubiquitous myoelectric control-based on-demand input for a wide range of applications.

In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets. However, this approach may fail to holistically reflect their effectiveness due to the significant impact of dataset characteristics on algorithm performance. Addressing this deficiency, this paper introduces a novel benchmarking methodology to facilitate a fair and robust comparison of RecSys algorithms, thereby advancing evaluation practices. By utilizing a diverse set of $30$ open datasets, including two introduced in this work, and evaluating $11$ collaborative filtering algorithms across $9$ metrics, we critically examine the influence of dataset characteristics on algorithm performance. We further investigate the feasibility of aggregating outcomes from multiple datasets into a unified ranking. Through rigorous experimental analysis, we validate the reliability of our methodology under the variability of datasets, offering a benchmarking strategy that balances quality and computational demands. This methodology enables a fair yet effective means of evaluating RecSys algorithms, providing valuable guidance for future research endeavors.

For a while, it seemed like the only place to meet potential partners was through an app--Tinder, Hinge, Bumble, etc. But as the apps are trying to monetize their matchmaking--and some users now with a whole decade of striking out under their belts--old-fashioned meet-cutes-in-bars or, say, debutante balls look more and more appealing. If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get benefits like zero ads on any Slate podcast, bonus episodes of shows like Slow Burn and Dear Prudence--and you'll be supporting the work we do here on What Next TBD. Sign up now at slate.com/whatnextplus to help support our work.

When I met my husband, who happens to be white, he told me that he was always seeing women with blonde hair on Tinder and he's not really into blondes. No matter how many times he had swiped left on blondes, the algorithms were always recommending them to him, presumably because pop culture dictates that white men prefer blondes. Luckily for us, the algorithms' tendency to stack blonde women in his swipe deck worked out in our favor because I'm a black woman who, at the time, had blonde hair. In nearly 10 years of swiping through profiles on Tinder, Bumble, Hinge, and OkCupid, I learned that dating apps can provide pathways for finding friendship, adventure, romance, and sometimes, love. But there was one aspect of dating app culture that I couldn't ignore because it was often the first thing matches wanted to talk about: race.

If you're single this Valentine's Day, you might be tempted to download a dating app. But knowing where to start can be a daunting process. From Tinder to Plenty of Fish - and even Singles With Food Allergies - there are thousands of apps to choose from. To help you get started, Ofcom has released new data detailing the most popular platforms among different age groups in Britain. So, whether you're a Gen Z, a Millennial, or even a Silver Fox, use our interactive tool to find out where your peers are looking for love.

Dating expert Cher Gopman shares how to find love in the new year on'Fox & Friends.' Being single on Valentine's Day can be annoying for some people -- but so can dating. And at a time when online dating is the new norm, experts say there are easier ways to drum up love without swiping for it. Dr. Susan Albersis, a psychologist at Cleveland Clinic in Ohio, told Fox News Digital in a statement that online dating is a "double-edged sword." "On one hand, it creates wonderful connections," she said. "The downside is that it can often bruise your self-esteem."

Candice Lim is joined by dating culture researcher Lakshmi Rengarajan and culture writer Kate Lindsay to discuss the past, present and future of dating apps. Online dating has been around since the days of dial-up. But apps like Tinder disrupted the market and changed the way we've dated for the past decade. Recently, there's been several trends emerging, from Gen-Z abandoning the apps to baby boomers finding love later in life. So are we witnessing the death of dating apps or have they integrated themselves so deeply into our lives that we can't live without them?

Nobody can blame you for wanting to continue driving your older car that you've managed to pay off. But you're missing out on convenience and safety features that come standard in newer cars, like backup cameras and hands-free navigation. Fortunately, you can upgrade your older car with this 9″ Wireless Car Display. This touchscreen display is compatible with both Apple CarPlay and Android Auto and even includes a 1080p backup camera. Not only can you enjoy the benefits of accessing your contacts, music, and Maps by touch or activating Siri or Google Assistant voice assistants, but you can also add a backup camera to your car for additional safety.

Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks. Understanding attack tactics helps improve the robustness of RSs. We intend to develop efficient attack methods that use limited resources to generate high-quality fake user profiles to achieve 1) transferability among black-box RSs 2) and imperceptibility among detectors. In order to achieve these goals, we introduce textual reviews of products to enhance the generation quality of the profiles. Specifically, we propose a novel attack framework named R-Trojan, which formulates the attack objectives as an optimization problem and adopts a tailored transformer-based generative adversarial network (GAN) to solve it so that high-quality attack profiles can be produced. Comprehensive experiments on real-world datasets demonstrate that R-Trojan greatly outperforms state-of-the-art attack methods on various victim RSs under black-box settings and show its good imperceptibility.