The past decade has witnessed the unprecedented march of technology and the opportunities, dangers, and disruptions that accompany it. In the last 4-5 years, a synthetic media technology (that uses machine learning techniques and is created by generative adversarial networks – GANs) known as deep fakes, has revolutionised the ways that digital media can be altered. The ability of state and non-state actors to generate, forge, and manipulate media has created clickbait headlines and fake news, 'terrorised women' by substituting faces to create fake porn, and abetted the spread of misinformation and disinformation. An opinion piece in the Washington Post has called this worrying trend of mass-scale manipulation the "democratisation of forgery".

Research about recommender systems emerges over the last decade and comprises valuable services to increase different companies' revenue. Several approaches exist in handling paper recommender systems. While most existing recommender systems rely either on a content-based approach or a collaborative approach, there are hybrid approaches that can improve recommendation accuracy using a combination of both approaches. Even though many algorithms are proposed using such methods, it is still necessary for further improvement. In this paper, we propose a recommender system method using a graph-based model associated with the similarity of users' ratings, in combination with users' demographic and location information. By utilizing the advantages of Autoencoder feature extraction, we extract new features based on all combined attributes. Using the new set of features for clustering users, our proposed approach (GHRS) has gained a significant improvement, which dominates other methods' performance in the cold-start problem. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms many existing recommendation algorithms on recommendation accuracy.

The newest gospel music artist is basically virtual, made from artificial intelligence. The digital artist is named J.C. and the song is titled, Biblical Love. Relevant Magazine reports it's the first gospel song ever written by an algorithm, recorded by an algorithm, and performed by an algorithm. Created by Marquis Boone Enterprises in Atlanta, Georgia, J.C. is not a flesh and blood performer. The song, according to a company press release, is "about unquantifiable love beyond description or measure – a love of biblical proportions that transcends to everyone and everything!" Tyler Huckabee, Relevant's senior editor explains in his article that it's not new technology.

The past decade has witnessed the unprecedented march of technology and the opportunities, dangers, and disruptions that accompany it. In the last 4-5 years, a synthetic media technology (that uses machine learning techniques and is created by generative adversarial networks – GANs) known as deep fakes, has revolutionised the ways that digital media can be altered. The ability of state and non-state actors to generate, forge, and manipulate media has created clickbait headlines and fake news, 'terrorised women' by substituting faces to create fake porn, and abetted the spread of misinformation and disinformation. An opinion piece in the Washington Post has called this worrying trend of mass-scale manipulation the "democratisation of forgery". In the last 4-5 years, a synthetic media technology known as deep fakes, has revolutionised the ways that digital media can be altered.

Ask Delphi is not a physical robot with a solid body. It's actually just a software that was supposed to let users ask tricky questions that do …

The Tom Hanks' vehicle "Finch" is "Cast Away" revisited. Instead of a young(ish) Hanks stranded on a desert island, we have an old Hanks stranded in a post-apocalyptic world, where a solar flare has destroyed the ozone layer. The UV rays are deadly. The temperature in direct sunlight is 150 degrees, and the empty, dust-covered and windswept streets are littered with desiccated corpses.. Hanks plays the eponymous Finch Weinberg, a tech genius, who lives in St. Louis, Mo., in a warehouse with his real dog Goodyear and his R2D2-like robo-dog Dewy. Finch can only go outside if he wears a spacesuit-like outfit with a space helmet and cooling device attached.

DJI is best known for drones, but it's possibly the most inventive camera company right now. After unveiling the outrageous full-frame Ronin 4K camera/gimbal last month, it has now launched the $2,200 Mavic 3 drone with not just one, but two innovative camera systems. As rumored, there are two models in the Mavic 3 family, the Standard and Cine models, along with a "Fly More" combo that bundles more accessories. The main difference is that the Mavic 3 Cine has a built-in 1TB SSD and supports Apple ProRes 422 HQ video recording -- highly desirable for professional film productions. The latter is also considerably more expensive, as I'll discuss shortly.

Source separation models either work on the spectrogram or waveform domain. In this work, we show how to perform end-to-end hybrid source separation, letting the model decide which domain is best suited for each source, and even combining both. The proposed hybrid version of the Demucs architecture won the Music Demixing Challenge 2021 organized by Sony. This architecture also comes with additional improvements, such as compressed residual branches, local attention or singular value regularization. Overall, a 1.4 dB improvement of the Signal-To-Distortion (SDR) was observed across all sources as measured on the MusDB HQ dataset, an improvement confirmed by human subjective evaluation, with an overall quality rated at 2.83 out of 5 (2.36 for the non hybrid Demucs), and absence of contamination at 3.04 (against 2.37 for the non hybrid Demucs and 2.44 for the second ranking model submitted at the competition).

Automatic detection of click-bait and incongruent news headlines is crucial to maintaining the reliability of the Web and has raised much research attention. However, most existing methods perform poorly when news headlines contain contextually important cardinal values, such as a quantity or an amount. In this work, we focus on this particular case and propose a neural attention based solution, which uses a novel cardinal Part of Speech (POS) tag pattern based hierarchical attention network, namely POSHAN, to learn effective representations of sentences in a news article. In addition, we investigate a novel cardinal phrase guided attention, which uses word embeddings of the contextually-important cardinal value and neighbouring words. In the experiments conducted on two publicly available datasets, we observe that the proposed methodgives appropriate significance to cardinal values and outperforms all the baselines. An ablation study of POSHAN shows that the cardinal POS-tag pattern-based hierarchical attention is very effective for the cases in which headlines contain cardinal values.

This paper introduces SocialVec, a general framework for eliciting social world knowledge from social networks, and applies this framework to Twitter. SocialVec learns low-dimensional embeddings of popular accounts, which represent entities of general interest, based on their co-occurrences patterns within the accounts followed by individual users, thus modeling entity similarity in socio-demographic terms. Similar to word embeddings, which facilitate tasks that involve text processing, we expect social entity embeddings to benefit tasks of social flavor. We have learned social embeddings for roughly 200,000 popular accounts from a sample of the Twitter network that includes more than 1.3 million users and the accounts that they follow, and evaluate the resulting embeddings on two different tasks. The first task involves the automatic inference of personal traits of users from their social media profiles. In another study, we exploit SocialVec embeddings for gauging the political bias of news sources in Twitter. In both cases, we prove SocialVec embeddings to be advantageous compared with existing entity embedding schemes. We will make the SocialVec entity embeddings publicly available to support further exploration of social world knowledge as reflected in Twitter.