Google is giving its smart-home range a shake-up, and is discontinuing two of its products to replace them with third-party collaborations. That means, after 12 years, it's time to say goodbye to the Nest Protect Smoke & CO Alarm, and it's also ending production of the Nest x Yale Lock, a smart lock that debuted in 2018. The Nest Protect's replacement comes from First Alert, a well-established player in the smoke detector space. Arriving in the coming months for 130 and available for preorder now, the First Alert Smart Smoke & Carbon Monoxide Alarm will offer safety voice alerts, safety checkups, and the ability to silence alarms from the app. It'll connect with existing Nest Protect devices, so if you have one, you can still install the First Alert system in another spot, and if a fire is detected, both units will sound the alarm. Just like the Protect, it can be set up and controlled through the Google Home app.

There has been vast literature that studies Conversational Agents (CAs) in facilitating older adults' health. The vast and diverse studies warrants a comprehensive review that concludes the main findings and proposes research directions for future studies, while few literature review did it from human-computer interaction (HCI) perspective. In this study, we present a survey of existing studies on CAs for older adults' health. Through a systematic review of 72 papers, this work reviewed previously studied older adults' characteristics and analyzed participants' experiences and expectations of CAs for health. We found that (1) Past research has an increasing interest on chatbots and voice assistants and applied CA as multiple roles in older adults' health. (2) Older adults mainly showed low acceptance CAs for health due to various reasons, such as unstable effects, harm to independence, and privacy concerns. (3) Older adults expect CAs to be able to support multiple functions, to communicate using natural language, to be personalized, and to allow users full control. We also discuss the implications based on the findings.

In this paper, we reproduce the experimental results presented in our previous work titled "Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems," which was published in the proceedings of the 31st ACM International Conference on Multimedia. This paper aims to validate the effectiveness of our proposed method and help others reproduce our experimental results. We provide detailed descriptions of our preprocessed datasets, source code structure, configuration file settings, experimental environment, and reproduced experimental results.

If you're looking for something with more elegance and sophistication, however, you should replace the switches in your walls. Besides, the most common drawback of relying on smart bulbs with conventional switches is that someone inevitably turns the switch off. Your expensive smart bulb is now a dumb bulb that can't be controlled with voice commands or be included in any lighting automations you've set up. Don't worry, it's an easy DIY project. Be aware, however, that most–but certainly not all–smart controls depend on the presence of a neutral wire in the box.

Group decision-making is becoming increasingly common in areas such as education, dining, travel, and finance, where collaborative choices must balance diverse individual preferences. While conventional recommender systems are effective in personalization, they fall short in group settings due to their inability to manage conflicting preferences, contextual factors, and multiple evaluation criteria. This study presents the development of a Context-Aware Multi-Criteria Group Recommender System (CA-MCGRS) designed to address these challenges by integrating contextual factors and multiple criteria to enhance recommendation accuracy. By leveraging a Multi-Head Attention mechanism, our model dynamically weighs the importance of different features. Experiments conducted on an educational dataset with varied ratings and contextual variables demonstrate that CA-MCGRS consistently outperforms other approaches across four scenarios. Our findings underscore the importance of incorporating context and multi-criteria evaluations to improve group recommendations, offering valuable insights for developing more effective group recommender systems.

Celebrity matchmaker Alessandra Conti told Fox News Digital that Garner and Affleck are incredible co-parents. Gwyneth Paltrow and Nikki Glaser are spilling the tea when it comes to their connections to Ben Affleck. During a recent episode of Paltrow's "Goop Podcast," the duo openly discussed Glaser's past history of using Raya, an exclusive dating app. While discussing her 2025 Golden Globe Awards opening monologue in which she joked about Affleck yelling the titles of movies "after he orgasms," Glaser said, "When I used to be on Raya and [Ben] would come across, [I would give him a] very concentrated check mark'yes' and, like, never [got] it back." GWYNETH PALTROW SAYS BEN AFFLECK WAS'EXCELLENT' IN BED COMPARED TO BRAD PITT Nikki Glaser told Gwyneth Paltrow she once tried to hook up with the actress' ex, Ben Affleck.

The navigation app Waze is dropping Google Assistant support for iPhones, citing "ongoing difficulties" with integrating the service. The company says it plans on replacing it with an "enhanced voice integration solution" at some point in the future. Google Assistant will still work for Android users. This is happening a full year after iPhone users began reporting issues related to Google Assistant, with many people noting that voice commands were totally broken. Waze says that it has "not been working as intended for over a year" and that it would rather "phase out Google Assistant on iOS" instead of "patching a feature that has faced ongoing difficulties." As previously stated, Google Assistant for Waze will continue to work on Android phones.

As artificial intelligence becomes more and more ingrained in daily life, we present a novel system that uses deep learning for music recommendation and emotion-based detection. Through the use of facial recognition and the DeepFace framework, our method analyses human emotions in real-time and then plays music that reflects the mood it has discovered. The system uses a webcam to take pictures, analyses the most common facial expression, and then pulls a playlist from local storage that corresponds to the mood it has detected. An engaging and customised experience is ensured by allowing users to manually change the song selection via a dropdown menu or navigation buttons. By continuously looping over the playlist, the technology guarantees continuity. The objective of our system is to improve emotional well-being through music therapy by offering a responsive and automated music-selection experience.

"Greetings, Lovers, Legends, and Gods of Desire!" read the Partiful invite for the pre-Valentine's Day gathering. On this night, we will surrender to his playful whims." Then, sternly and in all caps: "YOU MUST BE PRE-APPROVED TO GET IN." A couple of days later, a text blast came in; the planners of this in-person dating meetup for singles were budgeting for 200 attendees, but more than 1,000 people applied, so there'd be a venue change. RSVPs closed at 3 pm sharp the day of the event. Then, at night, Barbarossa Lounge in San Francisco's Financial District welcomed the lucky guests who managed to get their names on the list. The event, Love in the Stars, was hosted by local event promoter Spice King and the online platform Paloma, which describes itself as a dating-oriented members club. Per the invitation's instructions, attendees dressed to signal their status; the singles wore a dash of red to make themselves identifiable as the ones looking for love. Their non-single supporters wore a splash of white or gold to signal they were already spoken for. Within an hour, there was no room to move. Small talk and awkward flirting filled every inch of the dark bar, with the question "So, do you like working in tech?" bouncing around at the same tempo as the clubby beats. Welcome to Silicon Valley's in-person dating scene. These regular events are only accessible to those already in the know. They feature pre-vetted guest lists; invite-only gatherings at villas in Hillsborough, one of the wealthiest towns in California; WhatsApp groups that gather monthly in apartments around town; and private parties with secret locations promising Stanford alumni and "creatives" in attendance. In an area that's notoriously tough on daters, at a time when dating app fatigue is at an all-time high, the appetite for ways to find love face-to-face is growing into a frenzy. "We have all collectively realized that dating apps are the worst," says Allie Hoffman, the founder of the two-year-old organization The Feels, a nationwide in-person dating event series with a strong presence in San Francisco. "There is no intention around how depleting, bot-y, ghosty, breadcrumb-y, gaslight-y and fishy they are.

With the widespread application of automatic speech recognition (ASR) systems, their vulnerability to adversarial attacks has been extensively studied. However, most existing adversarial examples are generated on specific individual models, resulting in a lack of transferability. In real-world scenarios, attackers often cannot access detailed information about the target model, making query-based attacks unfeasible. To address this challenge, we propose a technique called Acoustic Representation Optimization that aligns adversarial perturbations with low-level acoustic characteristics derived from speech representation models. Rather than relying on model-specific, higher-layer abstractions, our approach leverages fundamental acoustic representations that remain consistent across diverse ASR architectures. By enforcing an acoustic representation loss to guide perturbations toward these robust, lower-level representations, we enhance the cross-model transferability of adversarial examples without degrading audio quality. Our method is plug-and-play and can be integrated with any existing attack methods. We evaluate our approach on three modern ASR models, and the experimental results demonstrate that our method significantly improves the transferability of adversarial examples generated by previous methods while preserving the audio quality.