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.

Smart LED light bulbs are one of the easiest ways to get into the IoT space. These smart lighting solutions let you control your home's illumination from your phone and other connected devices, and in addition to that practicality, they also inject some fun into your space. Color-changing bulbs have a plethora of RGB options for you to customize the lighting mood for your next movie night, date night or game day, or you can opt for cozy warm white light when you need to unwind at the end of a long day. It goes without saying that many of these smart LED light bulbs work with Amazon's Alexa and the Google Assistant, so if you already have a smart home setup in the works, you can find one that fits into your chosen ecosystem. And arguably the best thing about these devices is that they can fit into any budget; affordable and advanced options have flooded the space over the past few years. We've tested out a bunch of smart lights over the years, and these are our current favorites. If you've done any research into smart lights, you've probably come across Philips Hue bulbs.

Recent advancements in AI-driven conversational agents have exhibited immense potential of AI applications. Effective response generation is crucial to the success of these agents. While extensive research has focused on leveraging multiple auxiliary data sources (e.g., knowledge bases and personas) to enhance response generation, existing methods often struggle to efficiently extract relevant information from these sources. There are still clear limitations in the ability to combine versatile conversational capabilities with adherence to known facts and adaptation to large variations in user preferences and belief systems, which continues to hinder the wide adoption of conversational AI tools. This paper introduces a novel method, Conversational Agent for Multi-Source Auxiliary Context with Sparse and Symmetric Latent Interactions ( CoMAC), for conversation generation, which employs specialized encoding streams and post-fusion grounding networks for multiple data sources to identify relevant persona and knowledge information for the conversation. CoMAC also leverages a novel text similarity metric that allows bi-directional information sharing among multiple sources and focuses on a selective subset of meaningful words. Our experiments show that CoMAC improves the relevant persona and knowledge prediction accuracies and response generation quality significantly over two state-of-the-art methods.