Music genre classification is a critical component of music recommendation systems, generation algorithms, and cultural analytics. In this work, we present an innovative model for classifying music genres using attention-based temporal signature modeling. By processing spectrogram sequences through Convolutional Neural Networks (CNNs) and multi-head attention layers, our approach captures the most temporally significant moments within each piece, crafting a unique "signature" for genre identification. This temporal focus not only enhances classification accuracy but also reveals insights into genre-specific characteristics that can be intuitively mapped to listener perceptions. Our findings offer potential applications in personalized music recommendation systems by highlighting cross-genre similarities and distinctiveness, aligning closely with human musical intuition. This work bridges the gap between technical classification tasks and the nuanced, human experience of genre.

The development of large language models (LLMs) has significantly enhanced the capabilities of multimodal LLMs (MLLMs) as general assistants. However, lack of user-specific knowledge still restricts their application in human's daily life. In this paper, we introduce the Retrieval Augmented Personalization (RAP) framework for MLLMs' personalization. Starting from a general MLLM, we turn it into a personalized assistant in three steps. (a) Remember: We design a key-value database to store user-related information, e.g., user's name, avatar and other attributes. (b) Retrieve: When the user initiates a conversation, RAP will retrieve relevant information from the database using a multimodal retriever. (c) Generate: The input query and retrieved concepts' information are fed into MLLMs to generate personalized, knowledge-augmented responses. Unlike previous methods, RAP allows real-time concept editing via updating the external database. To further improve generation quality and alignment with user-specific information, we design a pipeline for data collection and create a specialized dataset for personalized training of MLLMs. Based on the dataset, we train a series of MLLMs as personalized multimodal assistants. By pretraining on large-scale dataset, RAP-MLLMs can generalize to infinite visual concepts without additional finetuning. Our models demonstrate outstanding flexibility and generation quality across a variety of tasks, such as personalized image captioning, question answering and visual recognition. The code, data and models are available at https://github.com/Hoar012/RAP-MLLM.

Significant "social ruptures" between people who think artificial intelligence systems are conscious and those who insist the technology feels nothing are looming, a leading philosopher has said. The comments, from Jonathan Birch, a professor of philosophy at the London School of Economics, come as governments prepare to gather this week in San Francisco to accelerate the creation of guardrails to tackle the most severe risks of AI. Last week, a transatlantic group of academics predicted that the dawn of consciousness in AI systems is likely by 2035 and one has now said this could result in "subcultures that view each other as making huge mistakes" about whether computer programmes are owed similar welfare rights as humans or animals. Birch said he was "worried about major societal splits", as people differ over whether AI systems are actually capable of feelings such as pain and joy. The debate about the consequence of sentience in AI has echoes of science fiction films, such as Steven Spielberg's AI (2001) and Spike Jonze's Her (2013), in which humans grapple with the feeling of AIs. AI safety bodies from the US, UK and other nations will meet tech companies this week to develop stronger safety frameworks as the technology rapidly advances.

If you struggle to hear what's being said in the movies and shows you're watching, just know you're not alone. Whether your hearing is less than ideal, or the sound mixing could be better, or you're trying to watch and listen to something without disturbing the rest of the household, there are a lot of reasons why dialog might be hard to pick out. The good news is that there are quite a few ways to fix the problem so you don't have to put up with missing out on dialog, which is a crucial part of understanding and enjoying what's onscreen. These are the options you can try, depending on the devices and apps you're using for streaming. Your first port of call should be the apps you're using to watch whatever it is you're watching.

This paper explores the growing impact of AI and NLP in bank marketing, highlighting their evolving roles in enhancing marketing strategies, improving customer engagement, and creating value within this sector. While AI and NLP have been widely studied in general marketing, there is a notable gap in understanding their specific applications and potential within the banking sector. This research addresses this specific gap by providing a systematic review and strategic analysis of AI and NLP applications in bank marketing, focusing on their integration across the customer journey and operational excellence. Employing the PRISMA methodology, this study systematically reviews existing literature to assess the current landscape of AI and NLP in bank marketing. Additionally, it incorporates semantic mapping using Sentence Transformers and UMAP for strategic gap analysis to identify underexplored areas and opportunities for future research. The systematic review reveals limited research specifically focused on NLP applications in bank marketing. The strategic gap analysis identifies key areas where NLP can further enhance marketing strategies, including customer-centric applications like acquisition, retention, and personalized engagement, offering valuable insights for both academic research and practical implementation. This research contributes to the field of bank marketing by mapping the current state of AI and NLP applications and identifying strategic gaps. The findings provide actionable insights for developing NLP-driven growth and innovation frameworks and highlight the role of NLP in improving operational efficiency and regulatory compliance. This work has broader implications for enhancing customer experience, profitability, and innovation in the banking industry.

Information-Seeking Dialogue (ISD) agents aim to provide accurate responses to user queries. While proficient in directly addressing user queries, these agents, as well as LLMs in general, predominantly exhibit reactive behavior, lacking the ability to generate proactive responses that actively engage users in sustained conversations. However, existing definitions of proactive dialogue in this context do not focus on how each response actively engages the user and sustains the conversation. Hence, we present a new definition of proactivity that focuses on enhancing the `proactiveness' of each generated response via the introduction of new information related to the initial query. To this end, we construct a proactive dialogue dataset comprising 2,000 single-turn conversations, and introduce several automatic metrics to evaluate response `proactiveness' which achieved high correlation with human annotation. Additionally, we introduce two innovative Chain-of-Thought (CoT) prompts, the 3-step CoT and the 3-in-1 CoT prompts, which consistently outperform standard prompts by up to 90% in the zero-shot setting.

High annotation costs from hiring or crowdsourcing complicate the creation of large, high-quality datasets needed for training reliable text classifiers. Recent research suggests using Large Language Models (LLMs) to automate the annotation process, reducing these costs while maintaining data quality. LLMs have shown promising results in annotating downstream tasks like hate speech detection and political framing. Building on the success in these areas, this study investigates whether LLMs are viable for annotating the complex task of media bias detection and whether a downstream media bias classifier can be trained on such data. We create annolexical, the first large-scale dataset for media bias classification with over 48000 synthetically annotated examples. Our classifier, fine-tuned on this dataset, surpasses all of the annotator LLMs by 5-9 percent in Matthews Correlation Coefficient (MCC) and performs close to or outperforms the model trained on human-labeled data when evaluated on two media bias benchmark datasets (BABE and BASIL). This study demonstrates how our approach significantly reduces the cost of dataset creation in the media bias domain and, by extension, the development of classifiers, while our subsequent behavioral stress-testing reveals some of its current limitations and trade-offs.

We may earn revenue from the products available on this page and participate in affiliate programs. The line between home and office has never been blurrier, so getting someone you love a gift that helps them cut down on their list of chores, improve their productivity, or help them relax has become incredibly thoughtful. This can mean gifting something practical, like a more comfortable office chair or an extravagantly large 4K TV. Many of these gifts are appropriate for both homeowners and renters, too. If you live with the person you're gifting these home upgrades to, you also benefit, which is a nice holiday bonus. One of the keys to a happy home during the holidays (or any other time) is a smart home, and a smart home needs a fast, consistent connection to the Internet. Eero's latest Wi-Fi routers support the latest wireless standard (Wi-Fi 6E), supporting speeds of up to 2.3 Gbps. For reference, Netflix recommends just 15Mbps to stream video in 4K, which is only .006% of the routers' total potential bandwidth. If you're shopping for someone who pays for a fast internet connection but finds their devices don't get the speeds promised--or they have places in their home where their wireless connection is spotty--this is the optimal solution. Amazon says this two-pack of Eero routers can cover an area of up to 4,000 sq. Every home or office needs an all-in-one printer for when a document needs to be scanned, printed, or copied.

For years now, an online shop called Super Yaki has been selling T-shirts and hats printed with the message "Judy Greer should've been the lead." That there is a market for such merch is a testament to just how beloved an actress Greer is, despite her reputation for always playing the sidekick rather than the main character. This month, though, all those T-shirt wearers' wishes have come true, sort of: The 49-year-old receives top billing in a movie that debuted on more than 3,000 screens last week. If you're wondering why you haven't heard of it, here comes the catch: Greer's lead role is in a Christian family movie from the son of the guy who co-wrote the Left Behind books. Greer plays a mother who takes on the challenge of directing her church's annual Christmas play in The Best Christmas Pageant Ever, directed by Dallas Jenkins, creator of Christian miniseries The Chosen, and based on the 1972 children's book of the same name.

EXCLUSIVE - Author and filmmaker Justine Bateman expressed optimism for the country following President-elect Donald Trump's historic victory, saying it felt like a cloud had been lifted. I feel great, in fact," Bateman told Fox News Digital in an interview. "I feel like there was this kind of suffocating cloud that was kind of over us… Regular people who had questions about decisions that were being made were threatened subtly or obviously into silence. And I feel like that's been broken, that sort of suppression has been kind of broken." Bateman, best known for playing Mallory Keaton on the hit 1980s sitcom "Family Ties," recently went viral for referring to the last four years as being "a very un-American period" for free expression and that only "permitted positions" were accepted by the powers that be. "My belief is that everyone should be free to live their life exactly how they want to live it.