Recently, there is an emerging interest in adversarially training a classifier with a rejection option (also known as a selective classifier) for boosting adversarial robustness. While rejection can incur a cost in many applications, existing studies typically associate zero cost with rejecting perturbed inputs, which can result in the rejection of numerous slightly-perturbed inputs that could be correctly classified. In this work, we study adversarially-robust classification with rejection in the stratified rejection setting, where the rejection cost is modeled by rejection loss functions monotonically non-increasing in the perturbation magnitude. We theoretically analyze the stratified rejection setting and propose a novel defense method -- Adversarial Training with Consistent Prediction-based Rejection (CPR) -- for building a robust selective classifier. Experiments on image datasets demonstrate that the proposed method significantly outperforms existing methods under strong adaptive attacks. For instance, on CIFAR-10, CPR reduces the total robust loss (for different rejection losses) by at least 7.3% under both seen and unseen attacks.

Over the past few decades, Industrial Control Systems (ICSs) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge. Although there are a few commonly used datasets, they may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. This paper presents the 'ICS-Flow' dataset, which offers network data and process state variables logs for supervised and unsupervised ML-based IDS assessment. The network data includes normal and anomalous network packets and flows captured from simulated ICS components and emulated networks. The anomalies were injected into the system through various attack techniques commonly used by hackers to modify network traffic and compromise ICSs. We also proposed open-source tools, `ICSFlowGenerator' for generating network flow parameters from Raw network packets. The final dataset comprises over 25,000,000 raw network packets, network flow records, and process variable logs. The paper describes the methodology used to collect and label the dataset and provides a detailed data analysis. Finally, we implement several ML models, including the decision tree, random forest, and artificial neural network to detect anomalies and attacks, demonstrating that our dataset can be used effectively for training intrusion detection ML models.

3D spatial perception is the problem of building and maintaining an actionable and persistent representation of the environment in real-time using sensor data and prior knowledge. Despite the fast-paced progress in robot perception, most existing methods either build purely geometric maps (as in traditional SLAM) or flat metric-semantic maps that do not scale to large environments or large dictionaries of semantic labels. The first part of this paper is concerned with representations: we show that scalable representations for spatial perception need to be hierarchical in nature. Hierarchical representations are efficient to store, and lead to layered graphs with small treewidth, which enable provably efficient inference. We then introduce an example of hierarchical representation for indoor environments, namely a 3D scene graph, and discuss its structure and properties. The second part of the paper focuses on algorithms to incrementally construct a 3D scene graph as the robot explores the environment. Our algorithms combine 3D geometry, topology (to cluster the places into rooms), and geometric deep learning (e.g., to classify the type of rooms the robot is moving across). The third part of the paper focuses on algorithms to maintain and correct 3D scene graphs during long-term operation. We propose hierarchical descriptors for loop closure detection and describe how to correct a scene graph in response to loop closures, by solving a 3D scene graph optimization problem. We conclude the paper by combining the proposed perception algorithms into Hydra, a real-time spatial perception system that builds a 3D scene graph from visual-inertial data in real-time. We showcase Hydra's performance in photo-realistic simulations and real data collected by a Clearpath Jackal robots and a Unitree A1 robot. We release an open-source implementation of Hydra at https://github.com/MIT-SPARK/Hydra.

As Autonomous Systems (AS) become more ubiquitous in society, more responsible for our safety and our interaction with them more frequent, it is essential that they are trustworthy. Assessing the trustworthiness of AS is a mandatory challenge for the verification and development community. This will require appropriate standards and suitable metrics that may serve to objectively and comparatively judge trustworthiness of AS across the broad range of current and future applications. The meta-expression `trustworthiness' is examined in the context of AS capturing the relevant qualities that comprise this term in the literature. Recent developments in standards and frameworks that support assurance of autonomous systems are reviewed. A list of key challenges are identified for the community and we present an outline of a process that can be used as a trustworthiness assessment framework for AS.

Sen. John Kennedy, R-La., joined'America's Newsroom' to discuss the significance of Sen. Feinstein's absence amid the push to confirm Biden's judicial nominees. OpenAI CEO Sam Altman will testify before Congress next week, a major step for lawmakers seeking to understand and regulate the fast-moving industry of artificial intelligence. Altman's company is the developer behind ChatGPT, the AI chatbot that captured the nation's attention late last year, with Americans using it for everything from medical advice to cheating on homework. The AI guru will appear before the Senate Judiciary subcommittee on privacy, technology, and the law on Tuesday. "Artificial intelligence will be transformative in ways we can't even imagine, with implications for Americans' elections, jobs, and security," Sen. Josh Hawley, R-Mo., said in a statement ahead of the hearing.

House Democrats and Republicans will hold a dinner at the Capitol next week with Sam Altman, the CEO of OpenAI, which developed the popular artificial intelligence chatbot ChatGPT, according to an invitation obtained by NBC News. The closed-door, members-only event, planned for Monday night after House votes, comes as Washington tries to figure out how, if at all, to create rules for and regulate the rapidly moving AI industry. The bipartisan dinner is hosted by GOP Conference Vice Chairman Mike Johnson, R-La., and Democratic Caucus Vice Chairman Ted Lieu, D-Calif., who made headlines this year when he introduced a resolution written by ChatGPT that calls on Congress to regulate AI. The goal of the Altman dinner is to "educate members," said Lieu, who shared the invitation with NBC News. More than 50 lawmakers have RSVP'd to the dinner, he said.

Scammers are texting victims and stealing their information by posing as legitimate businesses or agencies. CyberGuy explains how to stay safe. Remember the good ol' days when our biggest worry was accidentally pocket-dialing someone? Well, times have changed, and so has technology. We now have these nifty AI systems that can do everything from making restaurant reservations to driving our cars.

'ABSURD CLAIMS' - White House slams Comer, accuses GOP of conducting'evidence-free' probe into Biden family. DRAWING A LINE - California Gov. Gavin Newsom declines to back reparations checks, says slavery's legacy about'more than cash payments.' STRIVING FOR SEATS - Pair of Democrats silent on 2024 election plans as GOP seeks Senate control. 'GREATEST WEAPON' - Political left will use AI to fight against religious faith and truth, expert claims. 'RUN TOWARD DANGER' – As a former FBI special agent, here's why I want you to Back the Blue.

The Aerospace Corporation President and CEO Steve Isakowitz said he anticipates the future of space exploration and defense will include AI-controlled satellites and permanent living on the surface of the Moon and Mars. Speaking with Fox News Digital at the Milken Global Conference on May 4, Isakowitz noted that NASA has been using artificial intelligence (AI) for many years in Mars rovers because of the time it takes to communicate back and forth with Earth. The rover needed to know where to go and how to do so safely to combat the delay. Today, with the expansion in capabilities of AI and smaller, more affordable computer chips, advanced AI tech can now be packed into the satellites orbiting Earth. "I do think we're entering an age where we're going to have hyper-intelligence satellites, satellites that will not just be dumb cameras that are looking at the Earth and just filming everything, but you could tell it what to look for. So, don't just take pictures of the Pacific Ocean. Look for these kinds of tankers or look for these kinds of ships or look for these kind of warships or these kind of airplanes where you actually have the satellite. Know what it's looking at that has the intelligence to know if it doesn't feel well," Isakowitz said.

Fox News contributor Joe Concha joins "Fox & Friends First" to discuss Elon Musk's warning that AI could threaten elections and his concerns on the declining birth rate. The top lawyer for OpenAI, the company that developed ChatGPT, argued that the best way to regulate artificial intelligence is not to start with government mandated rules and regulations but to allow the companies themselves to set standards that ensure AI is used safely and responsibly. OpenAI General Counsel Jason Kwon made that argument during a Tuesday panel discussion in Washington, D.C., which was hosted by BSA/The Software Alliance, even as he acknowledged that AI is developing so quickly that it can often lead to unexpected results that companies quickly need to rein in. Still, when asked what his message to policymakers was, Kwon recommended voluntary, industry-led standards for AI, calling for a tactic that many companies in most industries tend to favor over government mandates. The top lawyer at OpenAI, run by CEO Sam Altman, above, said this week that the company recommends voluntary industry standards to regulate AI, not government mandates.