It's been a few years since Mr. Robot dropped its mind-bending final episode, and now series creator Sam Esmail is back with a film that explores similar themes of cyber security and paranoia. Leave the World Behind is a dual-release Netflix film that hits theaters on November 22 and the streaming service on December 8. Here's a new trailer to wet your whistle for the cyber-apocalypse thriller. Leave the World Behind stars Julia Roberts, Ethan Hawke, Mahershala Ali and Kevin Bacon, which is a whole lot of star power. As for the plot, this is an Esmail joint, so it's likely best to go in fresh, but Netflix did drop a tantalizingly simple story premise: a "family's vacation is upended when two strangers arrive at night, seeking refuge from a cyberattack that grows more terrifying by the minute." The synopsis calls to mind M. Night Shyamalan's Knock at the Cabin, and both films do have something in common--They're based on books.

NATO Secretary General Jens Stoltenberg shares why its important for America to stay in the fight between Russia and Ukraine on One Nation. NATO is testing new sea drones that can use artificial intelligence to detect suspicious activity near underwater infrastructure. Fourteen members of the NATO alliance, along with Sweden, have teamed up for multiple exercises over 12 days off the cost of Portugal to test underwater sea drones that have real-time ability to send "a deterrence signal to the enemy, be it Russia or somebody else," said Lt. Gen. Hans-Werner Wiermann, head of NATO's cell for protecting undersea infrastructure, according to a report from Bloomberg. The exercises, dubbed Dynamic Messenger 23 and Robotic Experimentation and Prototyping with Maritime Unmanned Systems (REPMUS 23), will bring together over 2,000 civilian amid military personnel with a focus on integrating maritime unmanned systems into the alliance's operations and test new technologies that are currently under development. NATO personnel test new underwater drone technology during Dynamic Messenger 23 and REPMUS 23 exercises.

The National Security Agency (NSA) has launched a dedicated artificial intelligence security center. This apparently follows the increased government use of algorithms and AI systems, related to defense and intelligent systems. The security center aims to protect these systems from theft and sabotage, as well as safeguard the country from external AI-based threats. The NSA's outgoing director, General Paul Nakasone, says the division will operate within the existing Cybersecurity Collaboration Center. This entity works with private industry and international partners to protect the US from cyberattacks from China, Russia and other countries with active malware and hacking campaigns.

It's logical for humans to feel anxious about artificial intelligence. After all, the news is constantly reeling off job after job at which the technology seems to outperform us. But humans aren't yet headed for all-out replacement. And if you do suffer from so-called AI anxiety, there are ways to alleviate your fears and even reframe them into a motivating force for good. In one recent example of generative AI's achievements, AI programs outscored the average human in tasks requiring originality, as judged by human reviewers.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. The current infatuation with artificial intelligence is indicative of the level of competence of those who are in the headlights of a fast-moving, still unidentified, flying object. The headlines range from "Mitigating the risk of extinction from AI" (through the European Commission) to promising a world free of disease (cancer, in particular), and unlimited prosperity. No more need for lawyers (thank God!), no more need for doctors, not to say truck drivers, and Hollywood screenwriters. AI is all over, most of the time in stealth mode – and pretty successful in every form of surveillance (there are so many).

Ukraine said its air defence systems shot down 16 of about 30 drones launched by Russia on Sunday. Authorities said civilian infrastructure and grain storage warehouses were damaged in the Cherkasy region as well as the southern Mykolaiv and eastern Dnipropetrovsk regions. Russia's defence ministry said its forces' air defences in eastern Ukraine had intercepted five United States-made HIMARS shells, an air-launched JDAM bomb and 37 Ukrainian drones. Kyiv began a counteroffensive in June to retake Ukrainian land occupied by Russia since it launched its full-scale invasion of the country in February 2022. Russia's defence ministry said it shot down six Ukrainian drones over Russian regions and two Ukrainian missiles over Crimea, which Moscow annexed from Ukraine in 2014.

Authorship identification has proven unsettlingly effective in inferring the identity of the author of an unsigned document, even when sensitive personal information has been carefully omitted. In the digital era, individuals leave a lasting digital footprint through their written content, whether it is posted on social media, stored on their employer's computers, or located elsewhere. When individuals need to communicate publicly yet wish to remain anonymous, there is little available to protect them from unwanted authorship identification. This unprecedented threat to privacy is evident in scenarios such as whistle-blowing. Proposed defenses against authorship identification attacks primarily aim to obfuscate one's writing style, thereby making it unlinkable to their pre-existing writing, while concurrently preserving the original meaning and grammatical integrity. The presented work offers a comprehensive review of the advancements in this research area spanning over the past two decades and beyond. It emphasizes the methodological frameworks of modification and generation-based strategies devised to evade authorship identification attacks, highlighting joint efforts from the differential privacy community. Limitations of current research are discussed, with a spotlight on open challenges and potential research avenues.

The division of one physical 5G communications infrastructure into several virtual network slices with distinct characteristics such as bandwidth, latency, reliability, security, and service quality is known as 5G network slicing. Each slice is a separate logical network that meets the requirements of specific services or use cases, such as virtual reality, gaming, autonomous vehicles, or industrial automation. The network slice can be adjusted dynamically to meet the changing demands of the service, resulting in a more cost-effective and efficient approach to delivering diverse services and applications over a shared infrastructure. This paper assesses various machine learning techniques, including the logistic regression model, linear discriminant model, k-nearest neighbor's model, decision tree model, random forest model, SVC BernoulliNB model, and GaussianNB model, to investigate the accuracy and precision of each model on detecting network slices. The report also gives an overview of 5G network slicing.

In recent years, the increasing threat of devastating wildfires has underscored the need for effective prescribed fire management. Process-based computer simulations have traditionally been employed to plan prescribed fires for wildfire prevention. However, even simplified process models like QUIC-Fire are too compute-intensive to be used for real-time decision-making, especially when weather conditions change rapidly. Traditional ML methods used for fire modeling offer computational speedup but struggle with physically inconsistent predictions, biased predictions due to class imbalance, biased estimates for fire spread metrics (e.g., burned area, rate of spread), and generalizability in out-of-distribution wind conditions. This paper introduces a novel machine learning (ML) framework that enables rapid emulation of prescribed fires while addressing these concerns. By incorporating domain knowledge, the proposed method helps reduce physical inconsistencies in fuel density estimates in data-scarce scenarios. To overcome the majority class bias in predictions, we leverage pre-existing source domain data to augment training data and learn the spread of fire more effectively. Finally, we overcome the problem of biased estimation of fire spread metrics by incorporating a hierarchical modeling structure to capture the interdependence in fuel density and burned area. Notably, improvement in fire metric (e.g., burned area) estimates offered by our framework makes it useful for fire managers, who often rely on these fire metric estimates to make decisions about prescribed burn management. Furthermore, our framework exhibits better generalization capabilities than the other ML-based fire modeling methods across diverse wind conditions and ignition patterns.

Timely and accurate assessment of hurricane-induced building damage is crucial for effective post-hurricane response and recovery efforts. Recently, remote sensing technologies provide large-scale optical or Interferometric Synthetic Aperture Radar (InSAR) imagery data immediately after a disastrous event, which can be readily used to conduct rapid building damage assessment. Compared to optical satellite imageries, the Synthetic Aperture Radar can penetrate cloud cover and provide more complete spatial coverage of damaged zones in various weather conditions. However, these InSAR imageries often contain highly noisy and mixed signals induced by co-occurring or co-located building damage, flood, flood/wind-induced vegetation changes, as well as anthropogenic activities, making it challenging to extract accurate building damage information. In this paper, we introduced an approach for rapid post-hurricane building damage detection from InSAR imagery. This approach encoded complex causal dependencies among wind, flood, building damage, and InSAR imagery using a holistic causal Bayesian network. Based on the causal Bayesian network, we further jointly inferred the large-scale unobserved building damage by fusing the information from InSAR imagery with prior physical models of flood and wind, without the need for ground truth labels. Furthermore, we validated our estimation results in a real-world devastating hurricane -- the 2022 Hurricane Ian. We gathered and annotated building damage ground truth data in Lee County, Florida, and compared the introduced method's estimation results with the ground truth and benchmarked it against state-of-the-art models to assess the effectiveness of our proposed method. Results show that our method achieves rapid and accurate detection of building damage, with significantly reduced processing time compared to traditional manual inspection methods.