Just about every year since 1952, Queen Elizabeth II of the United Kingdom has delivered a Christmas address to the masses, and 2020 will be no different. Shortly after she gives her remarks, however, British broadcaster Channel 4 will air an "alternative message" from the Queen, brought to life by deepfake software and an actress with a pseudo-regal affect. "On the BBC, I haven't always been able to speak plainly and from the heart," the "Queen" said in a promo posted to the broadcaster's Twitter. "So I'm grateful to Channel 4 for giving me the opportunity to say whatever I like without anyone putting words in my mouth." There's relatively little risk that anyone would look at Channel 4's deepfake and regard it as a genuine message from the Queen.

Channel 4 has sparked controversy and debate with a deepfake video of the Queen as an alternative to her traditional festive broadcast, to be aired on Christmas Day. The broadcaster will show a five-minute video in which a digitally altered version of the Queen shares her reflections on the year, including the departure of Prince Harry and Meghan Markle as senior royals and the Duke of York's involvement with the disgraced financier Jeffrey Epstein. The deepfake Queen, voiced by the actor Debra Stephenson, can also be seen performing a dance routine from social media platform TikTok. Channel 4 said the broadcast was intended to give a "stark warning" about the threat of fake news in the digital era, with its director of programmes, Ian Katz, describing the video as a "a powerful reminder that we can no longer trust our own eyes". Some experts suggested the broadcast might make the public think deepfake technology was more commonly used than is the case.

The far-reaching SolarWinds hack has hit not only federal agencies such as the Department of the Treasury, but computer systems for local U.S. governments as well. The far-reaching SolarWinds hack has hit not only federal agencies such as the Department of the Treasury, but computer systems for local U.S. governments as well. A U.S. cybersecurity agency said Wednesday that the far-reaching attack into the IT management company SolarWinds discovered earlier this month has not only affected key federal agencies, but also computer systems used by state and local governments. The hackers attached malware to a software update for SolarWinds' Orion system, which is used by many federal agencies and thousands of companies worldwide to monitor their computer networks. The hack infected several computer systems within the U.S. government, including at the departments of Treasury, Commerce, and Energy.

Around five percent of papers from the conference were on graphs so lots to discuss. A new paper (with authors from every major big tech), was recently published showing how one can attack language models like GPT-2 and extract information verbatim like personal identifiable information from just by querying the model. The information extracted derived from the models' training data that was based on scraped internet info. This is a big problem especially when you train a language model on a private custom dataset. Looks like Booking.com wants a new recommendation engine and they are offering up their dataset of over 1 million anonymized hotel reservations to get you in the game.

This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. The smart-testing ideas presented here are motivated by active learning and multi-armed bandit techniques in machine learning. Our active sampling method works in conjunction with quarantine policies, can handle different objectives, is dynamic and adaptive in the sense that it continually adapts to changes in real-time data. The bandit algorithm uses contact tracing, location-based sampling and random sampling in order to select specific individuals to test. Using a data-driven agent-based model simulating New York City we show that the algorithm samples individuals to test in a manner that rapidly traces infected individuals. Experiments also suggest that smart-testing can significantly reduce the death rates as compared to current methods such as testing symptomatic individuals with or without contact tracing.

Binary "YES-NO" notions of process compliance are not very helpful to managers for assessing the operational performance of their company because a large number of cases fall in the grey area of partial compliance. Hence, it is necessary to have ways to quantify partial compliance in terms of metrics and be able to classify actual cases by assigning a numeric value of compliance to them. In this paper, we formulate an evaluation framework to quantify the level of compliance of business processes across different levels of abstraction (such as task, trace and process level) and across multiple dimensions of each task(such as temporal, monetary, role-, data-, and quality-related) to provide managers more useful information about their operations and to help them improve their decision making processes. Our approach can also add social value by making social services provided by local, state and federal governments more flexible and improving the lives of citizens.

Efficient sampling of complex data distributions can be achieved using trained invertible flows (IF), where the model distribution is generated by pushing a simple base distribution through multiple non-linear bijective transformations. However, the iterative nature of the transformations in IFs can limit the approximation to the target distribution. In this paper we seek to mitigate this by implementing RBM-Flow, an IF model whose base distribution is a Restricted Boltzmann Machine (RBM) with a continuous smoothing applied. We show that by using RBM-Flow we are able to improve the quality of samples generated, quantified by the Inception Scores (IS) and Frechet Inception Distance (FID), over baseline models with the same IF transformations, but with less expressive base distributions. Furthermore, we also obtain D-Flow, an IF model with uncorrelated discrete latent variables. We show that D-Flow achieves similar likelihoods and FID/IS scores to those of a typical IF with Gaussian base variables, but with the additional benefit that global features are meaningfully encoded as discrete labels in the latent space.

Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways. We build on existing literature that uses prognostic scores from a predictive model to increase the efficiency of treatment effect estimates via covariate adjustment. Here we go further, utilizing a Bayesian framework that combines prognostic covariate adjustment with an empirical prior distribution learned from the predictive performances of the prognostic model on past trials. The Bayesian approach interpolates between prognostic covariate adjustment with strict type I error control when the prior is diffuse, and a single-arm trial when the prior is sharply peaked. This method is shown theoretically to offer a substantial increase in statistical power, while limiting the type I error rate under reasonable conditions. We demonstrate the utility of our method in simulations and with an analysis of a past Alzheimer's disease clinical trial.

Around one in six children steal money from their parents to pay for video game loot boxes – in-game'treasure chests' that award players random virtual prizes. In a survey of British teen and young adult gamers, Gambling Health Alliance (GHA) found 15 per cent had taken money from parents without permission to buy loot boxes. Overall, one in ten – 11 per cent – had used their parents' credit or debit cards to fund their loot box purchases, while 9 per cent had borrowed money they couldn't repay for the addictive in-game feature. Three young gamers' loot box buying habits resulted in their families having to re-mortgage their homes to cover the costs, according to the study. GHA is currently putting pressure on the UK government to class loot boxes in video games as a form of gambling.

The growth of myriad cyber-threats continues to accelerate, yet the stream of new and effective cyber-defense technologies has grown much more slowly. The gap between threat and defense has widened, as our adversaries deploy increasingly sophisticated attack technology and engage in cyber-crime with unprecedented power, resources, and global reach. We are in an escalating asymmetric cyber environment that calls for immediate action. The extension of cyber-attacks into the socio-techno realm and the use of cyber as an information influence and disinformation vector will continue to undermine our confidence in systems. The unknown is a growing threat in our cyber information systems.