I'm watching myself on YouTube saying things I would never say. These inventions trigger rage, but also optimism. I t was my blue shirt, a present from my sister-in-law, that gave it all away. It made me think of Yakov Petrovich Golyadkin, the lowly bureaucrat in Fyodor Dostoevsky's novella The Double, a disconcerting study of the fragmented self within a vast, impersonal feudal system. It all started with a message from an esteemed colleague congratulating me on a video talk on some geopolitical theme.

Many security and network applications require having large datasets to train the machine learning models. Limited data access is a well-known problem in the security domain. Recent studies have shown the potential of Transformer models to enlarge the size of data by synthesizing new samples, but the synthesized samples don't improve the models over the real data. To address this issue, we design an efficient transformer-based model as a generative framework to generate time-series data, that can be used to boost the performance of existing and new ML workflows. Our new transformer model achieves the SOTA results. We style our model to be generalizable and work across different datasets, and produce high-quality samples.

--In the evolving domain of Human Activity Recognition (HAR) using Internet of Things (IoT) devices, there is an emerging interest in employing Deep Generative Models (DGMs) to address data scarcity, enhance data quality, and improve classification metrics scores. Among these types of models, Generative Adversarial Networks (GANs) have arisen as a powerful tool for generating synthetic data that mimic real-world scenarios with high fidelity. However, Human Gesture Recognition (HGR), a subset of HAR, particularly in healthcare applications, using time series data such as allergic gestures, remains highly unexplored. In this paper, we examine and evaluate the performance of two GANs in the generation of synthetic gesture motion data that compose a part of an open-source benchmark dataset. The data is related to the disease identification domain and healthcare, specifically to allergic rhinitis. We also focus on these AI models' performance in terms of fidelity, diversity, and privacy. Furthermore, we examine the scenario if the synthetic data can substitute real data, in training scenarios and how well models trained on synthetic data can be generalized for the allergic rhinitis gestures. In our work, these gestures are related to 6-axes accelerometer and gyroscope data, serving as multi-variate time series instances, and retrieved from smart wearable devices. T o the best of our knowledge, this study is the first to explore the feasibility of synthesizing motion gestures for allergic rhinitis from wearable IoT device data using Generative Adversarial Networks (GANs) and testing their impact on the generalization of gesture recognition systems. It is worth noting that, even if our method has been applied to a specific category of gestures, it is designed to be generalized and can be deployed also to other motion data in the HGR domain.

The streets of London, New York, Dublin and Toronto have been full of thousands of people hoping to try and win celebrity lookalike contests in recent weeks. The phenomenon started last month, when Timothee Chalamet doppelgangers flocked to New York to try and win 50 for their resemblance to the Dune star. Since then, we've seen a Paul Mescal contest in Dublin, a Harry Styles contest in London, and even a Dev Patel contest in San Francisco. Amid the madness, you might be asking yourself - could I ever win a celebrity lookalike contest? Thankfully, help is at hand to answer this question, in the form of an app called Star by Face.

In an Intelligence Committee hearing with representatives from Google, Apple, and Meta on Wednesday, senators stressed that foreign influence is far from a solved problem. Top officials from Google, Apple, and Meta testified Wednesday before the United States Senate Intelligence Committee about each of their company's ongoing efforts to identify and disrupt foreign influence campaigns ahead of the country's November elections . The hearing, chaired by Senator Mark Warner of Virginia, served largely to impress upon the companies the need for more extensive safeguards against the disinformation campaigns being funded by foreign entities with an eye on influencing US politics. "This is really our effort to try to urge you guys to do more. To alert the public that this has not gone away," Warner said.

In recent weeks, as so-called cheapfake video clips suggesting President Joe Biden is unfit for office have gone viral on social media, a Kremlin-affiliated disinformation network has been promoting a parody music video featuring Biden wearing a diaper and being pushed around in a wheelchair. The video is called "Bye, Bye Biden" and has been viewed more than 5 million times on X since it was first promoted in the middle of May. It depicts Biden as senile, wearing a hearing aid, and taking a lot of medication. It also shows him giving money to a character who seems to represent illegal migrants while denying money to US citizens until they change their costume to mimic the Ukrainian flag. Another scene shows Biden opening the front door of a family home that features a Confederate flag on the wall and allowing migrants to come in and take over. Finally, the video contains references to stolen election conspiracies pushed by former president Donald Trump.

It's hard to find something pithy to say about 2023, a year of dissonant extremes, when wildfires devoured Canadian forests, Twitter withered into X, the Titan submersible imploded into infamy, Silicon Valley's power players rejoiced over the rise of generative AI, scientists cheered Crispr treatment breakthroughs, peace activists became terrorist-attack victims, and the world despaired over the thousands of children killed in Gaza. It is, frequently, a painful one. Appropriate, then, that this was a year for unwieldy, searching, big-swing books. Doorstoppers and sagas rose to the moment, providing insight into an increasingly inscrutable world even when they couldn't provide comfort. As always, this is an idiosyncratic, incomplete, and subjective list, the result of one person's avid but disorganized reading schedule.

A PAC affiliated with Miami Mayor Francis Suarez's presidential campaign has rolled out an AI version of the candidate to answer voter questions. A Republican 2024 candidate is deploying a digital doppelganger created with artificial intelligence, and his political allies are hoping the clone could be a first-of-his-kind "campaign surrogate" that could one day even do media interviews. A fundraising group affiliated with Miami Mayor Francis Suarez's presidential campaign has rolled out an audio and video lookalike of the conservative leader that answers voter questions about his political stances. "We wanted to do something that was going to set him apart from the rest of the crew running for president," SOS America PAC spokesman Chapin Fay told Fox News Digital. "You know, we have to. All the candidates are trying to find their lane and differentiate themselves, and I think this is one way that we can do that for Mayor Suarez."

A common problem when forecasting rare events, such as recessions, is limited data availability. Recent advancements in deep learning and generative adversarial networks (GANs) make it possible to produce high-fidelity synthetic data in large quantities. This paper uses a model called DoppelGANger, a GAN tailored to producing synthetic time series data, to generate synthetic Treasury yield time series and associated recession indicators. It is then shown that short-range forecasting performance for Treasury yields is improved for models trained on synthetic data relative to models trained only on real data. Finally, synthetic recession conditions are produced and used to train classification models to predict the probability of a future recession. It is shown that training models on synthetic recessions can improve a model's ability to predict future recessions over a model trained only on real data.

From Amy Adams and Isla Fisher to Liam Neeson and Ralph Fiennes, many celebrities are regularly mistaken for one another, despite being unrelated. Now, a study has revealed that these famous faces don't just look alike – they also likely have very similar DNA. Researchers from the Josep Carreras Leukaemia Research Institute in Barcelona have revealed that strong facial similarity is associated with shared genetic variants. 'These results will have future implications in forensic medicine - reconstructing the criminal's face from DNA - and in genetic diagnosis - the photo of the patient's face will already give you clues as to which genome he or she has,' said Dr Manel Esteller, senior author of the study. In 2015, researchers revealed that the chance of finding your doppelganger is one in a trillion. Teghan Lucas, a student at the University of Adelaide, conducted the study using a large database of face and body measurements from almost 4,000 individuals, combined with mathematical equations.