Deep Learning (DL) Models for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), while delivering improved performance, have been shown to be quite vulnerable to adversarial attacks. Existing works improve robustness by training models on adversarial samples. However, by focusing mostly on attacks that manipulate images randomly, they neglect the real-world feasibility of such attacks. In this paper, we propose FACTUAL, a novel Contrastive Learning framework for Adversarial Training and robust SAR classification. FACTUAL consists of two components: (1) Differing from existing works, a novel perturbation scheme that incorporates realistic physical adversarial attacks (such as OTSA) to build a supervised adversarial pre-training network. This network utilizes class labels for clustering clean and perturbed images together into a more informative feature space. (2) A linear classifier cascaded after the encoder to use the computed representations to predict the target labels. By pre-training and fine-tuning our model on both clean and adversarial samples, we show that our model achieves high prediction accuracy on both cases. Our model achieves 99.7% accuracy on clean samples, and 89.6% on perturbed samples, both outperforming previous state-of-the-art methods.

Coolant leaks, space debris collisions and unplanned engine thrusts are just some of the unexpected challenges astronauts aboard the International Space Station must overcome. NASA is investigating an object that a Florida resident says came from space and plummeted into his home last month. Alejandro Otero said a piece of equipment from the International Space Station hit his Naples home and posted photos on X in response to an astronomer who was tracking where and when the equipment entered Earth's atmosphere. Otero was on vacation but said the object caused significant damage and nearly stuck his son, local outlet WINK News first reported. "My son was home when the piece tore through the roof with a loud crash that could be heard on our security cameras as well," Otero told Fox News.

ESPN personality and OutKick's Clay Travis talk about who the pundit will vote for in the 2024 presidential election. ESPN star Stephen A. Smith snapped back at former Democrat presidential nominee Hillary Clinton, who told voters to "get over yourselves" when asked about Americans dreading a Trump-Biden rematch this November. Clinton made her declaration in an appearance on Monday's "The Tonight Show." She suggested it wasn't a hard choice to make for voters because "one is old, and effective, and compassionate, has a heart and really cares about people. And one is old and has been charged with 91 felonies."

Rep. Mike Waltz, R-Fla., and Hoover Institution senior fellow Victor Davis Hanson react to military leaders testifying during House hearing on President Biden's Afghanistan withdrawal on'Hannity.' President Biden's condemnation of the Israeli airstrike that killed seven food aid workers in Gaza earlier this week isn't sitting well with some critics, who called the president's reaction "obvious hypocrisy." Biden responded after the World Central Kitchen (WCK) nonprofit, founded by celebrity chef Jose Andres, announced Tuesday that it was pausing all its operations in Gaza after seven of its food aid workers – including a dual U.S.-Canadian citizen -- were killed by an "unforgivable" Israeli airstrike. "I am outraged and heartbroken by the deaths of seven humanitarian workers from World Central Kitchen, including one American, in Gaza yesterday," Biden wrote in a statement. "They were providing food to hungry civilians in the middle of a war. They were brave and selfless. Their deaths are a tragedy."

The Israeli military's bombing campaign in Gaza used a previously undisclosed AI-powered database that at one stage identified 37,000 potential targets based on their apparent links to Hamas, according to intelligence sources involved in the war. In addition to talking about their use of the AI system, called Lavender, the intelligence sources claim that Israeli military officials permitted large numbers of Palestinian civilians to be killed, particularly during the early weeks and months of the conflict. Their unusually candid testimony provides a rare glimpse into the first-hand experiences of Israeli intelligence officials who have been using machine-learning systems to help identify targets during the six-month war. Israel's use of powerful AI systems in its war on Hamas has entered uncharted territory for advanced warfare, raising a host of legal and moral questions, and transforming the relationship between military personnel and machines. "This is unparalleled, in my memory," said one intelligence officer who used Lavender, adding that they had more faith in a "statistical mechanism" than a grieving soldier.

For almost a decade now, scholarship in and beyond the ACM FAccT community has been focusing on novel and innovative ways and methodologies to audit the functioning of algorithmic systems. Over the years, this research idea and technical project has matured enough to become a regulatory mandate. Today, the Digital Services Act (DSA) and the Online Safety Act (OSA) have established the framework within which technology corporations and (traditional) auditors will develop the `practice' of algorithmic auditing thereby presaging how this `ecosystem' will develop. In this paper, we systematically review the auditing provisions in the DSA and the OSA in light of observations from the emerging industry of algorithmic auditing. Who is likely to occupy this space? What are some political and ethical tensions that are likely to arise? How are the mandates of `independent auditing' or `the evaluation of the societal context of an algorithmic function' likely to play out in practice? By shaping the picture of the emerging political economy of algorithmic auditing, we draw attention to strategies and cultures of traditional auditors that risk eroding important regulatory pillars of the DSA and the OSA. Importantly, we warn that ambitious research ideas and technical projects of/for algorithmic auditing may end up crashed by the standardising grip of traditional auditors and/or diluted within a complex web of (sub-)contractual arrangements, diverse portfolios, and tight timelines.

Mitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems. Organizations that develop and deploy frontier systems have significant access to such information. By reporting safety-critical information to actors in government, industry, and civil society, these organizations could improve visibility into new and emerging risks posed by frontier systems. Equipped with this information, developers could make better informed decisions on risk management, while policymakers could design more targeted and robust regulatory infrastructure. We outline the key features of responsible reporting and propose mechanisms for implementing them in practice. Evaluate current models for novel risks (including risks discovered by other organizations) Update model safeguards and risk mitigations Developer Other developers (e.g., revise scaling policy, security practices) Documents and Evaluates information Consult with domain experts in government reports safety and decides on (e.g., experts in national security, public health) information response plan Solicit additional information from developer Government actor (e.g., design decisions, organizational processes) Request or conduct further safety evaluations (incl. in collaboration with independent auditors) Domain experts in

As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data, so as to improve analysis performance and reduce biases in subsequent machine learning tasks. Given the lack of ground truth in most cases of crowdsourcing, we refer to data quality as annotators' consistency and credibility. Unlike the simple scenarios where Kappa coefficient and intraclass correlation coefficient usually can apply, online crowdsourcing requires dealing with more complex situations. We introduce a systematic method for evaluating data quality and detecting spamming threats via variance decomposition, and we classify spammers into three categories based on their different behavioral patterns. A spammer index is proposed to assess entire data consistency and two metrics are developed to measure crowd worker's credibility by utilizing the Markov chain and generalized random effects models. Furthermore, we showcase the practicality of our techniques and their advantages by applying them on a face verification task with both simulation and real-world data collected from two crowdsourcing platforms.

Software vulnerabilities are a challenge in cybersecurity. Manual security patches are often difficult and slow to be deployed, while new vulnerabilities are created. Binary code vulnerability detection is less studied and more complex compared to source code, and this has important practical implications. Deep learning has become an efficient and powerful tool in the security domain, where it provides end-to-end and accurate prediction. Modern deep learning approaches learn the program semantics through sequence and graph neural networks, using various intermediate representation of programs, such as abstract syntax trees (AST) or control flow graphs (CFG). Due to the complex nature of program execution, the output of an execution depends on the many program states and inputs. Also, a CFG generated from static analysis can be an overestimation of the true program flow. Moreover, the size of programs often does not allow a graph neural network with fixed layers to aggregate global information. To address these issues, we propose DeepEXE, an agent-based implicit neural network that mimics the execution path of a program. We use reinforcement learning to enhance the branching decision at every program state transition and create a dynamic environment to learn the dependency between a vulnerability and certain program states. An implicitly defined neural network enables nearly infinite state transitions until convergence, which captures the structural information at a higher level. The experiments are conducted on two semi-synthetic and two real-world datasets. We show that DeepEXE is an accurate and efficient method and outperforms the state-of-the-art vulnerability detection methods.

A long-standing ambition for artificial intelligence (AI) in biomedicine is the development of AI systems that could eventually make major scientific discoveries, with the potential to be worthy of a Nobel Prize--fulfilling the Nobel Turing Challenge [1]. While the concept of an "AI scientist" is aspirational, advances in agent-based AI pave the way to the development of AI agents as conversable systems capable of skeptical learning and reasoning that coordinate large language models (LLMs), machine learning (ML) tools, experimental platforms, or even combinations of them [2-5] (Figure 1). The complexity of biological problems requires a multistage approach, where decomposing complex questions into simpler tasks is necessary. AI agents can break down a problem into manageable subtasks, which can then be addressed by agents with specialized functions for targeted problem-solving and integration of scientific knowledge, paving the way toward a future in which a major biomedical discovery is made solely by AI [2, 6].