Last year, a Tennessee-based artist named Kelly McKernan noticed that their name was being used with increasing frequency in A.I.-driven image generation. McKernan makes paintings that often feature nymphlike female figures in an acid-colored style that blends Art Nouveau and science fiction. A list published in August, by a Web site called Metaverse Post, suggested "Kelly McKernan" as a term to feed an A.I. generator in order to create "Lord of the Rings"-style art. Hundreds of other artists were similarly listed according to what their works evoked: anime, modernism, "Star Wars." On the Discord chat that runs an A.I. generator called Midjourney, McKernan discovered that users had included their name more than twelve thousand times in public prompts.

Breezeline, the nation's eighth-largest cable operator, has launched Business Stream TV, a new, cloud-based video service designed to meet the needs of small and medium-sized businesses, their employees and customers. Business Stream TV uses advanced IPTV (Internet Protocol TV) technology to seamlessly integrate live TV, including local channels, streaming services like Netflix, Amazon Prime Video and YouTube, as well as popular sports apps, music services and more. Partners with ClusterPower to Unleash the Power of Artificial Intelligence Because Stream TV is cloud-based, it uses a sleek, 4" x 4" device, which saves space and is more energy efficient than traditional video equipment. Content from multiple sources is fully integrated into the Business Stream TV platform, which means it doesn't require switching between inputs or require multiple remotes. "Business Stream TV, when combined with Breezeline Business Internet, provides unmatched control and functionality for businesses and their teams," said Heather McCallion, Vice President of Products and Programming for Breezeline.

This work addresses the cross-corpora generalization issue for the low-resourced spoken language identification (LID) problem. We have conducted the experiments in the context of Indian LID and identified strikingly poor cross-corpora generalization due to corpora-dependent non-lingual biases. Our contribution to this work is twofold. First, we propose domain diversification, which diversifies the limited training data using different audio data augmentation methods. We then propose the concept of maximally diversity-aware cascaded augmentations and optimize the augmentation fold-factor for effective diversification of the training data. Second, we introduce the idea of domain generalization considering the augmentation methods as pseudo-domains. Towards this, we investigate both domain-invariant and domain-aware approaches. Our LID system is based on the state-of-the-art emphasized channel attention, propagation, and aggregation based time delay neural network (ECAPA-TDNN) architecture. We have conducted extensive experiments with three widely used corpora for Indian LID research. In addition, we conduct a final blind evaluation of our proposed methods on the Indian subset of VoxLingua107 corpus collected in the wild. Our experiments demonstrate that the proposed domain diversification is more promising over commonly used simple augmentation methods. The study also reveals that domain generalization is a more effective solution than domain diversification. We also notice that domain-aware learning performs better for same-corpora LID, whereas domain-invariant learning is more suitable for cross-corpora generalization. Compared to basic ECAPA-TDNN, its proposed domain-invariant extensions improve the cross-corpora EER up to 5.23%. In contrast, the proposed domain-aware extensions also improve performance for same-corpora test scenarios.

Recently, symbolic music generation with deep learning techniques has witnessed steady improvements. Most works on this topic focus on MIDI representations, but less attention has been paid to symbolic music generation using guitar tablatures (tabs) which can be used to encode multiple instruments. Tabs include information on expressive techniques and fingerings for fretted string instruments in addition to rhythm and pitch. In this work, we use the DadaGP dataset for guitar tab music generation, a corpus of over 26k songs in GuitarPro and token formats. We introduce methods to condition a Transformer-XL deep learning model to generate guitar tabs (GTR-CTRL) based on desired instrumentation (inst-CTRL) and genre (genre-CTRL). Special control tokens are appended at the beginning of each song in the training corpus. We assess the performance of the model with and without conditioning. We propose instrument presence metrics to assess the inst-CTRL model's response to a given instrumentation prompt. We trained a BERT model for downstream genre classification and used it to assess the results obtained with the genre-CTRL model. Statistical analyses evidence significant differences between the conditioned and unconditioned models. Overall, results indicate that the GTR-CTRL methods provide more flexibility and control for guitar-focused symbolic music generation than an unconditioned model.

Controversy is widespread online. Previous studies mainly define controversy based on vague assumptions of its relation to sentiment such as hate speech and offensive words. This paper introduces the first question-answering dataset that defines content controversy by user perception, i.e., votes from plenty of users. It contains nearly 10K questions, and each question has a best answer and a most controversial answer. Experimental results reveal that controversy detection in question answering is essential and challenging, and there is no strong correlation between controversy and sentiment tasks.

The remarkable success of deep learning is built on a large amount of labeled data. Data annotation in real-world scenarios often suffers from annotation ambiguity. To address annotation ambiguity, partial label learning allows multiple candidate labels to be annotated for each training instance, which can be widely used in web mining (Luo & Orabona, 2010), automatic image annotations (Zeng et al., 2013; Chen et al., 2018), ecoinformatics (Liu & Dietterich, 2012), and crowdsourcing (Gong et al., 2018). For example, a movie clip may contain several characters talking to each other, with some of them appearing in a screenshot. Although we can obtain scripts and dialogues that indicate the names of the characters, we cannot directly confirm the real name of each face in the screenshot (see Figure 7(a)). A similar scenario arises for recognizing faces from news images, where we can obtain the names of the people from the news descriptions but cannot establish a one-to-one correspondence with the face images (see Figure 7(b)). Partial label learning problem also appears in crowdsourcing, where each instance may be given multiple labels by different annotators. However, some labels may be incorrect or biased due to differences in expertise or cultural background of different annotators, so it is necessary to find the most appropriate label for each instance from candidate labels (see Figure 7(c)).

A long-standing challenge for search and conversational assistants is query intention detection in ambiguous queries. Asking clarifying questions in conversational search has been widely studied and considered an effective solution to resolve query ambiguity. Existing work have explored various approaches for clarifying question ranking and generation. However, due to the lack of real conversational search data, they have to use artificial datasets for training, which limits their generalizability to real-world search scenarios. As a result, the industry has shown reluctance to implement them in reality, further suspending the availability of real conversational search interaction data. The above dilemma can be formulated as a cold start problem of clarifying question generation and conversational search in general. Furthermore, even if we do have large-scale conversational logs, it is not realistic to gather training data that can comprehensively cover all possible queries and topics in open-domain search scenarios. The risk of fitting bias when training a clarifying question retrieval/generation model on incomprehensive dataset is thus another important challenge. In this work, we innovatively explore generating clarifying questions in a zero-shot setting to overcome the cold start problem and we propose a constrained clarifying question generation system which uses both question templates and query facets to guide the effective and precise question generation. The experiment results show that our method outperforms existing state-of-the-art zero-shot baselines by a large margin. Human annotations to our model outputs also indicate our method generates 25.2\% more natural questions, 18.1\% more useful questions, 6.1\% less unnatural and 4\% less useless questions.

In recent years, recommender systems have advanced rapidly, where embedding learning for users and items plays a critical role. A standard method learns a unique embedding vector for each user and item. However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions on their own; and 2) it may incur unbearably high memory costs when the number of users and items scales up. Existing approaches either can only address one of the limitations or have flawed overall performances. In this paper, we propose Clustered Embedding Learning (CEL) as an integrated solution to these two problems. CEL is a plug-and-play embedding learning framework that can be combined with any differentiable feature interaction model. It is capable of achieving improved performance, especially for cold users and items, with reduced memory cost. CEL enables automatic and dynamic clustering of users and items in a top-down fashion, where clustered entities jointly learn a shared embedding. The accelerated version of CEL has an optimal time complexity, which supports efficient online updates. Theoretically, we prove the identifiability and the existence of a unique optimal number of clusters for CEL in the context of nonnegative matrix factorization. Empirically, we validate the effectiveness of CEL on three public datasets and one business dataset, showing its consistently superior performance against current state-of-the-art methods. In particular, when incorporating CEL into the business model, it brings an improvement of $+0.6\%$ in AUC, which translates into a significant revenue gain; meanwhile, the size of the embedding table gets $2650$ times smaller.

The evaluation of recommender systems from a practical perspective is a topic of ongoing discourse within the research community. While many current evaluation methods reduce performance to a single value metric as an easy way to compare models, it relies on the assumption that the methods' performance remains constant over time. In this study, we examine this assumption and propose the Cross-Validation Thought Time (CVTT) technique as a more comprehensive evaluation method, focusing on model performance over time. By utilizing the proposed technique, we conduct an in-depth analysis of the performance of popular RecSys algorithms. Our findings indicate that (1) the performance of the recommenders varies over time for all reviewed datasets, (2) using simple evaluation approaches can lead to a substantial decrease in performance in real-world evaluation scenarios, and (3) excessive data usage can lead to suboptimal results.

BARCELONA, SPAIN - FEBRUARY 28: A visitor enjoys a Virtual Reality experience at the SK telecom ... [ ] booth on day 1 of the GSMA Mobile World Congress on February 28, 2022 in Barcelona, Spain. A pattern we see in many industries is that technology changes alongside culture and society, and this remains true for entertainment. The most obvious to consumers has been the shift from terrestrial television to streaming services like Netflix NFLX and Amazon AMZN Prime. The greater flexibility allowed consumers to decouple themselves from an entertainment schedule, being dictated by broadcast times. OTT platforms are aligned with the changing patterns of casualized work and study that focus on individual freedom.