In various real-world applications such as machine translation, sentiment analysis, and question answering, a pivotal role is played by NLP models, facilitating efficient communication and decision-making processes in domains ranging from healthcare to finance. However, a significant challenge is posed to the robustness of these natural language processing models by text adversarial attacks. These attacks involve the deliberate manipulation of input text to mislead the predictions of the model while maintaining human interpretability. Despite the remarkable performance achieved by state-of-the-art models like BERT in various natural language processing tasks, they are found to remain vulnerable to adversarial perturbations in the input text. In addressing the vulnerability of text classifiers to adversarial attacks, three distinct attack mechanisms are explored in this paper using the victim model BERT: BERT-on-BERT attack, PWWS attack, and Fraud Bargain's Attack (FBA). Leveraging the IMDB, AG News, and SST2 datasets, a thorough comparative analysis is conducted to assess the effectiveness of these attacks on the BERT classifier model. It is revealed by the analysis that PWWS emerges as the most potent adversary, consistently outperforming other methods across multiple evaluation scenarios, thereby emphasizing its efficacy in generating adversarial examples for text classification. Through comprehensive experimentation, the performance of these attacks is assessed and the findings indicate that the PWWS attack outperforms others, demonstrating lower runtime, higher accuracy, and favorable semantic similarity scores. The key insight of this paper lies in the assessment of the relative performances of three prevalent state-of-the-art attack mechanisms.

While recent progress in video-text retrieval has been driven by the exploration of powerful model architectures and training strategies, the representation learning ability of video-text retrieval models is still limited due to low-quality and scarce training data annotations. To address this issue, we present a novel video-text learning paradigm, HaVTR, which augments video and text data to learn more generalized features. Specifically, we first adopt a simple augmentation method, which generates self-similar data by randomly duplicating or dropping subwords and frames. In addition, inspired by the recent advancement in visual and language generative models, we propose a more powerful augmentation method through textual paraphrasing and video stylization using large language models (LLMs) and visual generative models (VGMs). Further, to bring richer information into video and text, we propose a hallucination-based augmentation method, where we use LLMs and VGMs to generate and add new relevant information to the original data. Benefiting from the enriched data, extensive experiments on several video-text retrieval benchmarks demonstrate the superiority of HaVTR over existing methods.

This is the second time Godzilla and King Kong have made a film appearance together in recent times with 2021's Godzilla vs Kong being the first instalment. Both films were directed by Adam Wingard. Godzilla x Kong made back its budget of 135m in the first weekend when it took in 195m at cinemas, according to figures from Box Office Mojo. In total, it has sold 209m in tickets so far and has scored a very respectable 92 percent Rotten Tomatoes audience rating. The origins of Godzilla go back 70 years to the first 1954 film release in Tokyo, Japan – Gojira, directed by Ishiro Honda.

The emergence of large language models (LLMs) has revolutionized the capabilities of text comprehension and generation. Multi-modal generation attracts great attention from both the industry and academia, but there is little work on personalized generation, which has important applications such as recommender systems. This paper proposes the first method for personalized multimodal generation using LLMs, showcases its applications and validates its performance via an extensive experimental study on two datasets. The proposed method, Personalized Multimodal Generation (PMG for short) first converts user behaviors (e.g., clicks in recommender systems or conversations with a virtual assistant) into natural language to facilitate LLM understanding and extract user preference descriptions. Such user preferences are then fed into a generator, such as a multimodal LLM or diffusion model, to produce personalized content. To capture user preferences comprehensively and accurately, we propose to let the LLM output a combination of explicit keywords and implicit embeddings to represent user preferences. Then the combination of keywords and embeddings are used as prompts to condition the generator. We optimize a weighted sum of the accuracy and preference scores so that the generated content has a good balance between them. Compared to a baseline method without personalization, PMG has a significant improvement on personalization for up to 8% in terms of LPIPS while retaining the accuracy of generation.

Introduction: Music generation is a complex task that has received significant attention in recent years, and deep learning techniques have shown promising results in this field. Objectives: While extensive work has been carried out on generating Piano and other Western music, there is limited research on generating classical Indian music due to the scarcity of Indian music in machine-encoded formats. In this technical paper, methods for generating classical Indian music, specifically tabla music, is proposed. Initially, this paper explores piano music generation using deep learning architectures. Then the fundamentals are extended to generating tabla music. Methods: Tabla music in waveform (.wav) files are pre-processed using the librosa library in Python. A novel Bi-LSTM with an Attention approach and a transformer model are trained on the extracted features and labels. Results: The models are then used to predict the next sequences of tabla music. A loss of 4.042 and MAE of 1.0814 are achieved with the Bi-LSTM model. With the transformer model, a loss of 55.9278 and MAE of 3.5173 are obtained for tabla music generation. Conclusion: The resulting music embodies a harmonious fusion of novelty and familiarity, pushing the limits of music composition to new horizons.

Memes are one of the most popular types of content used in an online disinformation campaign. They are primarily effective on social media platforms since they can easily reach many users. Memes in a disinformation campaign achieve their goal of influencing the users through several rhetorical and psychological techniques, such as causal oversimplification, name-calling, and smear. The SemEval 2024 Task 4 \textit{Multilingual Detection of Persuasion Technique in Memes} on identifying such techniques in the memes is divided across three sub-tasks: ($\mathbf{1}$) Hierarchical multi-label classification using only textual content of the meme, ($\mathbf{2}$) Hierarchical multi-label classification using both, textual and visual content of the meme and ($\mathbf{3}$) Binary classification of whether the meme contains a persuasion technique or not using it's textual and visual content. This paper proposes an ensemble of Class Definition Prediction (CDP) and hyperbolic embeddings-based approaches for this task. We enhance meme classification accuracy and comprehensiveness by integrating HypEmo's hierarchical label embeddings (Chen et al., 2023) and a multi-task learning framework for emotion prediction. We achieve a hierarchical F1-score of 0.60, 0.67, and 0.48 on the respective sub-tasks.

Elon Musk says Tesla is raising compensation for its AI engineers, saying OpenAI is "aggressively recruiting" them. 'CRAZIEST TALENT WAR': Tesla CEO Elon Musk said the electric vehicle giant is giving its artificial intelligence engineers a raise as the automaker tries to fend off poaching efforts by ChatGPT creator OpenAI. COSTLY GAME: More and more sports bettors appear to be turning to artificial intelligence to help counter the notoriously unpredictable tournament, which is often referred to as March Madness. LEISURE TIME: Billionaire investor and New York Mets owner Steve Cohen said in a Wednesday appearance on CNBC's "Squawk Box," that he believes that the majority of workers will eventually have a four-day work week and three-day weekend, which will expand opportunities for individuals to engage in leisurely pursuits. FIGHT AGAINST AI: Comedian George Carlin's estate has agreed to a settlement with the media company it sued earlier this year over the use of artificial intelligence.

Faking a political endorsement in Taiwan ahead of its crucial January election, sharing memes to amplify outrage over Japan's disposal of nuclear wastewater, and spreading conspiracy theories that claim the U.S. government was behind Hawaii's wildfire and Kentucky's train derailment last year. These are just some of the ways that China's influence operations have ramped up their use of artificial intelligence to sow disinformation and stoke discord worldwide over the last seven months, according to a new report released Friday by Microsoft Threat Intelligence. Microsoft has observed notable trends from state-backed actors, the report said, "that demonstrate not only doubling down on familiar targets, but also attempts to use more sophisticated influence techniques to achieve their goals." In particular, Chinese influence actors "experimented with new media" and "continued to refine AI-generated or AI-enhanced content." Among the operations highlighted in the report was a "a notable uptick in content featuring Taiwanese political figures ahead of the January 13 presidential and legislative elections."

China will attempt to disrupt elections in the US, South Korea and India this year with artificial intelligence-generated content after making a dry run with the presidential poll in Taiwan, Microsoft has warned. The US tech firm said it expected Chinese state-backed cyber groups to target high-profile elections in 2024, with North Korea also involved, according to a report by the company's threat intelligence team published on Friday. "As populations in India, South Korea and the United States head to the polls, we are likely to see Chinese cyber and influence actors, and to some extent North Korean cyber actors, work toward targeting these elections," the report reads. Microsoft said that "at a minimum" China will create and distribute through social media AI-generated content that "benefits their positions in these high-profile elections". The company added that the impact of AI-made content was minor but warned that could change.

This article investigates how AI-generated content can disrupt central revenue streams of the creative industries, in particular the collection of dividends from intellectual property (IP) rights. It reviews the IP and copyright questions related to the input and output of generative AI systems. A systematic method is proposed to assess whether AI-generated outputs, especially images, infringe previous copyrights, using a similarity metric (CLIP) between images against historical copyright rulings. An examination (economic and technical feasibility) of previously proposed compensation frameworks reveals their financial implications for creatives and IP holders. Lastly, we propose a novel IP framework for compensation of artists and IP holders based on their published "licensed AIs" as a new medium and asset from which to collect AI royalties.