Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. When we have friends and family with kids over, voice assistants can become a favorite attraction. At best, someone will start another "Baby Shark" round, and you'll sing it for a week. But what if one of those curious kiddos buys toys with your Amazon Echo?

Apple has just three smartwatches in its current lineup: the affordable Apple Watch SE, the advanced Apple Watch Ultra 2 and the flagship Apple Watch Series 9. All three offer fitness tracking, safety features, Siri support and iPhone integration, and all come in carbon-neutral configurations. But there are plenty of differences too, not the least of which is pricing: the Apple Watch SE starts at $250, whereas the Ultra 2 will run you a whopping $799. Internal sensors, displays and battery life vary from model to model, as well. In short, deciding the best Apple Watch for you might be trickier than you think.

There has always been little more to swiping right or left on a person's Tinder profile than if you like how they look. Now, the dating app is finally introducing a range of features that provide a more rounded idea of people, such as profile prompts and basic info tags. Anyone who has used apps like fellow Match Group-owned Hinge or Bumble will likely find many of these updates familiar. Profile prompts, for example, are a long-standing feature on both, with Tinder users now able to share their responses to statements like "The first item on my bucket list is" or the ever-popular "Two truths and a lie." Basic info tags let people share facts such as their zodiac sign, drinking habits and love language.

"I wasn't able to go outside anywhere alone," Ana says. "I had so much anxiety that when I went outside to do errands, I lost consciousness twice. That's when I realized I was very sick." Ana began working for LGBTQ dating app Grindr when she was in her early twenties, one of hundreds of Hondurans hired by US-headquartered outsourcing company PartnerHero to work on the account. Her team was based in San Pedro Sula, Honduras' second city, where they handled tasks ranging from the mundane--tech support emails and billing queries--to the horrifying: user reports of sexual assault, homophobic violence, child sexual abuse, and murder.

Graph Convolution Networks (GCNs) are widely considered state-of-the-art for collaborative filtering. Although several GCN-based methods have been proposed and achieved state-of-the-art performance in various tasks, they can be computationally expensive and time-consuming to train if too many layers are created. However, since the linear GCN model can be interpreted as a differential equation, it is possible to transfer it to an ODE problem. This inspired us to address the computational limitations of GCN-based models by designing a simple and efficient NODE-based model that can skip some GCN layers to reach the final state, thus avoiding the need to create many layers. In this work, we propose a Graph Neural Ordinary Differential Equation-based method for Collaborative Filtering (GODE-CF). This method estimates the final embedding by utilizing the information captured by one or two GCN layers. To validate our approach, we conducted experiments on multiple datasets. The results demonstrate that our model outperforms competitive baselines, including GCN-based models and other state-of-the-art CF methods. Notably, our proposed GODE-CF model has several advantages over traditional GCN-based models. It is simple, efficient, and has a fast training time, making it a practical choice for real-world situations.

Software bots have attracted increasing interest and popularity in both research and society. Their contributions span automation, digital twins, game characters with conscious-like behavior, and social media. However, there is still a lack of intelligent bots that can adapt to the variability and dynamic nature of digital web environments. Unlike human users, they have difficulty understanding and exploiting the affordances across multiple virtual environments. Despite the hype, bots with human user-like cognition do not currently exist. Chatbots, for instance, lack situational awareness on the digital platforms where they operate, preventing them from enacting meaningful and autonomous intelligent behavior similar to human users. In this survey, we aim to explore the role of cognitive architectures in supporting efforts towards engineering software bots with advanced general intelligence. We discuss how cognitive architectures can contribute to creating intelligent software bots. Furthermore, we highlight key architectural recommendations for the future development of autonomous, user-like cognitive bots.

While recommender systems have become an integral component of the Web experience, their heavy reliance on user data raises privacy and security concerns. Substituting user data with synthetic data can address these concerns, but accurately replicating these real-world datasets has been a notoriously challenging problem. Recent advancements in generative AI have demonstrated the impressive capabilities of diffusion models in generating realistic data across various domains. In this work we introduce a Score-based Diffusion Recommendation Module (SDRM), which captures the intricate patterns of real-world datasets required for training highly accurate recommender systems. SDRM allows for the generation of synthetic data that can replace existing datasets to preserve user privacy, or augment existing datasets to address excessive data sparsity. Our method outperforms competing baselines such as generative adversarial networks, variational autoencoders, and recently proposed diffusion models in synthesizing various datasets to replace or augment the original data by an average improvement of 4.30% in Recall@$k$ and 4.65% in NDCG@$k$.

As technological advancements in extended reality (XR) amplify the demand for more XR content, traditional development processes face several challenges: 1) a steep learning curve for inexperienced developers, 2) a disconnect between 2D development environments and 3D user experiences inside headsets, and 3) slow iteration cycles due to context switching between development and testing environments. To address these challenges, we introduce AtomXR, a streamlined, immersive, no-code XR prototyping tool designed to empower both experienced and inexperienced developers in creating applications using natural language, eye-gaze, and touch interactions. AtomXR consists of: 1) AtomScript, a high-level human-interpretable scripting language for rapid prototyping, 2) a natural language interface that integrates LLMs and multimodal inputs for AtomScript generation, and 3) an immersive in-headset authoring environment. Empirical evaluation through two user studies offers insights into natural language-based and immersive prototyping, and shows AtomXR provides significant improvements in speed and user experience compared to traditional systems.

The rise of online social networks has facilitated the evolution of social recommender systems, which incorporate social relations to enhance users' decision-making process. With the great success of Graph Neural Networks in learning node representations, GNN-based social recommendations have been widely studied to model user-item interactions and user-user social relations simultaneously. Despite their great successes, recent studies have shown that these advanced recommender systems are highly vulnerable to adversarial attacks, in which attackers can inject well-designed fake user profiles to disrupt recommendation performances. While most existing studies mainly focus on targeted attacks to promote target items on vanilla recommender systems, untargeted attacks to degrade the overall prediction performance are less explored on social recommendations under a black-box scenario. To perform untargeted attacks on social recommender systems, attackers can construct malicious social relationships for fake users to enhance the attack performance. However, the coordination of social relations and item profiles is challenging for attacking black-box social recommendations. To address this limitation, we first conduct several preliminary studies to demonstrate the effectiveness of cross-community connections and cold-start items in degrading recommendations performance. Specifically, we propose a novel framework Multiattack based on multi-agent reinforcement learning to coordinate the generation of cold-start item profiles and cross-community social relations for conducting untargeted attacks on black-box social recommendations. Comprehensive experiments on various real-world datasets demonstrate the effectiveness of our proposed attacking framework under the black-box setting.

In August, a swarm of hopeless and horny romantics on Reddit disputed the pros and cons of Bumble, the dating app that requires women to make the first move. "Besides barren wastelands like [Plenty of Fish] crawling with bots, scammers, hookers, and psychos, this app has to be the worst," one user posted. Said another, "Lots of fun conversations but ghost city when trying to get a number or plan a date." Other Redditors openly shared how they met their partners on the app, but the consensus was unequivocally clear: Bumble, like the majority of dating apps currently on the market, is bad. "If Bumble is the worst dating app, then what's the best alternative--Tinder, Hinge?" asked one user.