Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal representation that effectively captures the interplay between visual and textual features through intermediate fusion is critical. Conventional fusion techniques are unable to attend to the modality-specific features effectively. Moreover, most studies exclusively concentrated on English and overlooked other low-resource languages. This paper proposes a context-aware attention framework for multimodal hateful content detection and assesses it for both English and non-English languages. The proposed approach incorporates an attention layer to meaningfully align the visual and textual features. This alignment enables selective focus on modality-specific features before fusing them. We evaluate the proposed approach on two benchmark hateful meme datasets, viz. MUTE (Bengali code-mixed) and MultiOFF (English). Evaluation results demonstrate our proposed approach's effectiveness with F1-scores of $69.7$% and $70.3$% for the MUTE and MultiOFF datasets. The scores show approximately $2.5$% and $3.2$% performance improvement over the state-of-the-art systems on these datasets. Our implementation is available at https://github.com/eftekhar-hossain/Bengali-Hateful-Memes.

Target encoding is an effective technique to deliver better performance for conventional machine learning methods, and recently, for deep neural networks as well. However, the existing target encoding approaches require significant increase in the learning capacity, thus demand higher computation power and more training data. In this paper, we present a novel and efficient target encoding scheme, MUTE to improve both generalizability and robustness of a target model by understanding the inter-class characteristics of a target dataset. By extracting the confusion level between the target classes in a dataset, MUTE strategically optimizes the Hamming distances among target encoding. Such optimized target encoding offers higher classification strength for neural network models with negligible computation overhead and without increasing the model size. When MUTE is applied to the popular image classification networks and datasets, our experimental results show that MUTE offers better generalization and defense against the noises and adversarial attacks over the existing solutions.

Amazon has its own lineup of smart home devices, including a plethora of Alexa-enabled speakers that not only bring music into your home but can easily run all of your smart gadgets, too. We took a close look at the smallest of the bunch, the budget-friendly Echo Dot, which has a lot to offer despite its petite size. The Echo Dot is small, compact, and easily fits into your decor. The Echo Dot is the smaller sibling of Amazon's popular midrange smart speaker, the Echo. This third-generation model still rocks the circa-2017 design upgrade that's slightly more rotund than the model that came before, but it still won't take up a ton of room.

Has Netflix's sizeable investment in original science-fiction movies been a bust? By one popular metric, Rotten Tomatoes, the answer would seem to be: Categorically. Since 2017's Okja, a feisty ecological fairy tale by Korean filmmaker Bong Joon-ho, Netflix has put out seven back-to-back stinkers, their average "freshness" score rounding up to 30 percent. Only one of the seven can be called unwatchable: Duncan Jones' Mute, an overlong and sexually confused nightclub noir that trips over itself to imagine a neon-colored vision of future Berlin peopled by the likes of a mustachioed Paul Rudd. This is terribly sad, considering the director's first two films, Moon and Source Code, were the exact opposite--careful, contained stories that played out in modest settings.

Netflix is the place where dreams come true. Or, in the case of Duncan Jones' Mute, where they go to die. Jones has been working on the film, a noir-inflected science-fiction thriller set in a dystopian future Berlin, since even before he made his feature debut with 2009's Moon--and talking it up ever since. But it seemed destined to be one of those projects that was forever just around the corner, always the movie Jones was making after the next one. Even an attempt to more cost-effectively turn the story into a graphic novel fizzled, although Jones released an image from the illustrated work in progress in 2013.

February will hopefully be rewarding for sci-fi fans who subscribe to Netflix. In addition to Altered Carbon's debut this week, later in the month we'll finally get a peek at director Duncan Jones' Mute. The movie has been floating around for about as long as Jones has been a filmmaker (he previously directed Moon, Source Code and Warcraft), and now we finally have a look at it. The movie follows a mute bartender caught up in a jam, and features as much neon, mustachioed Paul Rudd and as many flying cars as you'd hope. If this was enough to stoke your curiosity, the movie premieres February 23rd.

Duncan Jones' next movie won't be coming to theaters -- it's going straight to streaming. The Moon and Warcraft director has revealed that his long-in-the-making sci-fi film noire, Mute, will premiere on Netflix February 23rd. The movie is set in a future Berlin where a mute bartender (played by Alexander Skarsgård) has to trust a pair of American surgeons (led by Paul Rudd) as he tracks down a disappeared woman. There's no trailer yet, but in many ways the effort taken to release the movie is the hook -- Netflix is giving Jones a chance that might not have come up through conventional formats. As Jones noted, Mute is his "Don Quixote." It was supposed to be his first movie (he had a first draft in 2003), but it got pushed back for a number of reasons.