Tensor ring (TR) decomposition has recently received increased attention due to its superior expressive performance for high-order tensors. However, the applicability of traditional TR decomposition algorithms to real-world applications is hindered by prevalent large data sizes, missing entries, and corruption with outliers. In this work, we propose a scalable and robust TR decomposition algorithm capable of handling large-scale tensor data with missing entries and gross corruptions. We first develop a novel auto-weighted steepest descent method that can adaptively fill the missing entries and identify the outliers during the decomposition process. Further, taking advantage of the tensor ring model, we develop a novel fast Gram matrix computation (FGMC) approach and a randomized subtensor sketching (RStS) strategy which yield significant reduction in storage and computational complexity. Experimental results demonstrate that the proposed method outperforms existing TR decomposition methods in the presence of outliers, and runs significantly faster than existing robust tensor completion algorithms.

The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks. Our team experimented with different ways to combat class imbalance throughout the tasks using data augmentation and loss alteration techniques. We tackled the challenge by utilising ensembles of Transformer models trained on different datasets, which are tested to find the balance between performance and interpretability. This solution ranked us in the top 40\% of teams for each of the tracks.

Being one of the most spoken languages globally, Bengali portrays large diversity in dialects and prosodic features, which demands ASR frameworks to be robust towards distribution shifts. For example, islamic religious sermons in Bengali are delivered with a tonality that is significantly different from regular speech. Our training dataset is collected via massively online crowdsourcing campaigns which resulted in 1177.94 hours collected and curated from 22, 645 native Bengali speakers from South Asia. Our test dataset comprises 23.03 hours of speech collected and manually annotated from 17 different sources, e.g., Bengali TV drama, Audiobook, Talk show, Online class, and Islamic sermons to name a few. OOD-Speech is jointly the largest publicly available speech dataset, as well as the first out-ofdistribution Figure 1: t-Stochastic Neighbor Embeddings [6] of Geneva ASR benchmarking dataset for Bengali.

Current methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a posteriori. This is not ideal because in a realworld zero-shot scenario of practical relevance, no labelled data is available. Thus, we propose a simple yet effective method for screening reasonable prompt templates in zero-shot text classification: Perplexity Selection (Perplection). We hypothesize that language discrepancy can be used to measure the efficacy of prompt templates, and thereby develop a substantiated perplexity-based scheme allowing for forecasting the performance of prompt templates in advance. Experiments show that our method leads to improved prediction performance in a realistic zero-shot setting, eliminating the need for any labelled examples.

Log Off Movement CEO Emma Lembke and teacher Matt Miles discuss the impact of artificial intelligence on kids on "The Story." The Irish Times was forced to retract a story it ran that criticized Irish women for using fake tans after it learned the story was allegedly submitted by someone using artificial intelligence to write it. The May 11 op-ed, "Irish women's obsession with fake tan is problematic," argued that women who use fake tans mock people with naturally dark skin. The author of the article was said to be Adriana Acosta-Cortez, a 29-year-old Ecuadorian health worker from the Dublin area. "It was a breach of the trust between the Irish Times and its readers, and we are genuinely sorry," Ruadhán Mac Cormaic said in a statement, according to a report from The Guardian.

From Armageddon to the Day After Tomorrow, there have been plenty of Hollywood movies about how our world might end. But if there is to be a global apocalypse, what might be to blame for wiping out all life on Earth? A wandering black hole, giant asteroid impact and nuclear war could all trigger such disaster, as could the rise of killer robots or the reversal of our planet's magnetic field. Many of these might seem far-fetched but with the Doomsday Clock being placed at a record 90 seconds to midnight this year – and scientists warning that humanity's continued existence is at greater risk than ever before – the threat is now all to real. So how exactly would these devastating possibilities come about? End of days: Ff there is to be a global apocalypse, what might be to blame for wiping out all life on Earth?

Another challenge: Forming an AI regulatory body on a global scale would require significant funding. "We need money," he said. "We need some philanthropists probably to get us started." "It's still a very long road," Marcus told Fox News. "It's a big ask, but I think the time for it is right."

This paper introduces Cro-FiReDa, a sentiment-annotated dataset for Croatian in the domain of movie reviews. The dataset, which contains over 10,000 sentences, has been annotated at the sentence level. In addition to presenting the overall annotation process, we also present benchmark results based on the transformer-based fine-tuning approach

PsychoGenics CEO Emer Leahy of Paramus, New Jersey, explains how the first potential AI-discovered treatment for schizophrenia was developed through machine learning. Fox News Digital spoke with her. Imagine a world where "interspecies communication" isn't the stuff of sci-fi fantasies - instead, a reality where humans can chit-chat with their furry, feathery, and scaly friends. This is where AI swoops in like a superhero, with researchers using algorithms to decipher animal vocalizations, movements, and even facial expressions. The Earth Species Project, a non-profit organization dedicated to decoding animal communication, is at the forefront of this groundbreaking research.

A film production company was targeted by fraudsters using a voice'clone' of Benedict Cumberbatch created by AI, the company revealed to DailyMail.com. An eerily convincing Benedict Cumberbatch phoned the company to discuss a film deal, says Bob William, screenwriter and director at Peabody Films, a company based in Malaga, Spain. The AI Cumberbatch was '100 percent the voice', says Mr William, adding that the company was convinced it was the real actor at first. When'Cumberbatch' and his agent refused to meet in-person, they realized the ruse, saving themselves from losing money. But, many others have fallen for similar scams, as AI is opening the door for new tools that bad actors can use to steal money from unsuspecting people.