Neo-fascism is a political and societal ideology that has been having remarkable growth in the last decade in the United States of America (USA), as well as in other Western societies. It poses a grave danger to democracy and the minorities it targets, and it requires active actions against it to avoid escalation. This work presents the first-of-its-kind neo-fascist coding scheme for digital discourse in the USA societal context, overseen by political science researchers. Our work bridges the gap between Natural Language Processing (NLP) and political science against this phenomena. Furthermore, to test the coding scheme, we collect a tremendous amount of activity on the internet from notable neo-fascist groups (the forums of Iron March and Stormfront.org), and the guidelines are applied to a subset of the collected posts. Through crowdsourcing, we annotate a total of a thousand posts that are labeled as neo-fascist or non-neo-fascist. With this labeled data set, we fine-tune and test both Small Language Models (SLMs) and Large Language Models (LLMs), obtaining the very first classification models for neo-fascist discourse. We find that the prevalence of neo-fascist rhetoric in this kind of forum is ever-present, making them a good target for future research. The societal context is a key consideration for neo-fascist speech when conducting NLP research. Finally, the work against this kind of political movement must be pressed upon and continued for the well-being of a democratic society. Disclaimer: This study focuses on detecting neo-fascist content in text, similar to other hate speech analyses, without labeling individuals or organizations.

Framing used by news media, especially in times of conflict, can have substantial impact on readers' opinion, potentially aggravating the conflict itself. Current studies on the topic of conflict framing have limited insights due to their qualitative nature or only look at surface level generic frames without going deeper. In this work, we identify indicators of war and peace journalism, as outlined by prior work in conflict studies, in a corpus of news articles reporting on the Israel-Palestine war. For our analysis, we use computational approaches, using a combination of frame semantics and large language models to identify both communicative framing and its connection to linguistic framing. Our analysis reveals a higher focus on war based reporting rather than peace based. We also show substantial differences in reporting across the US, UK, and Middle Eastern news outlets in framing who the assailant and victims of the conflict are, surfacing biases within the media.

Increasing wildfire occurrence has spurred growing interest in wildfire spread prediction. However, even the most complex wildfire models diverge from observed progression during multi-day simulations, motivating need for data assimilation. A useful approach to assimilating measurement data into complex coupled atmosphere-wildfire models is to estimate wildfire progression from measurements and use this progression to develop a matching atmospheric state. In this study, an approach is developed for estimating fire progression from VIIRS active fire measurements, GOES-derived ignition times, and terrain height data. A conditional Generative Adversarial Network is trained with simulations of historic wildfires from the atmosphere-wildfire model WRF-SFIRE, thus allowing incorporation of WRF-SFIRE physics into estimates. Fire progression is succinctly represented by fire arrival time, and measurements for training are obtained by applying an approximate observation operator to WRF-SFIRE solutions, eliminating need for satellite data during training. The model is trained on tuples of fire arrival times, measurements, and terrain, and once trained leverages measurements of real fires and corresponding terrain data to generate samples of fire arrival times. The approach is validated on five Pacific US wildfires, with results compared against high-resolution perimeters measured via aircraft, finding an average Sorensen-Dice coefficient of 0.81. The influence of terrain height on the arrival time inference is also evaluated and it is observed that terrain has minimal influence when the inference is conditioned on satellite measurements.

Large Language models (LLMs) are transforming digital usage, particularly in text generation, image creation, information retrieval and code development. ChatGPT, launched by OpenAI in November 2022, quickly became a reference, prompting the emergence of competitors such as Google's Gemini. However, these technological advances raise new cybersecurity challenges, including prompt injection attacks, the circumvention of regulatory measures ( jailbreaking), the spread of misinformation (hallucinations) and risks associated with deep fakes. This paper presents a comparative analysis of the security and alignment levels of ChatGPT and Gemini, as well as a taxonomy of jailbreak techniques associated with experiments.

Safely aligning large language models (LLMs) often demands extensive human-labeled preference data, a process that's both costly and time-consuming. While synthetic data offers a promising alternative, current methods frequently rely on complex iterative prompting or auxiliary models. To address this, we introduce Refusal-Aware Adaptive Injection (RAAI), a straightforward, training-free, and model-agnostic framework that repurposes LLM attack techniques. RAAI works by detecting internal refusal signals and adaptively injecting predefined phrases to elicit harmful, yet fluent, completions. Our experiments show RAAI effectively jailbreaks LLMs, increasing the harmful response rate from a baseline of 2.15% to up to 61.04% on average across four benchmarks. Crucially, fine-tuning LLMs with the synthetic data generated by RAAI improves model robustness against harmful prompts while preserving general capabilities on standard tasks like MMLU and ARC. This work highlights how LLM attack methodologies can be reframed as practical tools for scalable and controllable safety alignment.

AALpy is a well-established open-source automata learning library written in Python with a focus on active learning of systems with IO behavior. It provides a wide range of state-of-the-art algorithms for different automaton types ranging from fully deterministic to probabilistic automata. In this work, we present the recent addition of a generalized implementation of an important method from the domain of passive automata learning: state-merging in the red-blue framework. Using a common internal representation for different automaton types allows for a general and highly configurable implementation of the red-blue framework. We describe how to define and execute state-merging algorithms using AALpy, which reduces the implementation effort for state-merging algorithms mainly to the definition of compatibility criteria and scoring. This aids the implementation of both existing and novel algorithms. In particular, defining some existing state-merging algorithms from the literature with AALpy only takes a few lines of code.

Here's how AI has been used during the L.A. protests. Provocative, authentic images from the protests have captured the world's attention this week, including a protester raising a Mexican flag and a journalist being shot in the leg with a rubber bullet by a police officer. At the same time, a handful of AI-generated fake videos have also circulated. Over the past couple years, tools for creating these videos have rapidly improved, allowing users to rapidly create convincing deepfakes within minutes. Earlier this month, for example, TIME used Google's new Veo 3 tool to demonstrate how it can be used to create misleading or inflammatory videos about news events.

This is a discussion about artificial intelligence (AI), trust, power, and integrity. There are two kinds of trust--interpersonal and social--and we regularly confuse them. What matters here is social trust, which is about reliability and predictability in society. Our confusion will increase with AI, and the corporations controlling AI will use that confusion to take advantage of us. This is a security problem. This is a confidentiality problem. But it is much more an integrity problem. And that integrity is going to be the primary security challenge for AI systems of the future. It's also a regulatory problem, and it is government's role to enable social trust, which means incentivizing trustworthy AI. Okay, so let's break that down. Trust is a complicated concept, and the word is overloaded with many different meanings. When we say we trust a friend, it is less about their specific actions and more about them as a person.

State Armor founder and CEO Michael Lucci on CCP-linked researchers residing at American universities, national security threats from China and the need to block the subversion with legislation. FIRST ON FOX – Rep. Darin LaHood, R-Ill., is introducing a new bill Thursday imploring the National Security Administration (NSA) to develop an "AI security playbook" to stay ahead of threats from China and other foreign adversaries. The bill, dubbed the "Advanced AI Security Readiness Act," directs the NSA's Artificial Intelligence Security Center to develop an "AI Security Playbook to address vulnerabilities, threat detection, cyber and physical security strategies, and contingency plans for highly sensitive AI systems." It is co-sponsored by House Select Committee on China Chairman Rep. John Moolenaar, R-Mich., Ranking Member Rep. Raja Krishnamoorthi, D-Ill., and Rep. Josh Gottheimer, D-N.J. LaHood, who sits on the House Intelligence Committee and the House Select Committee on China, told Fox News Digital that the legislative proposal, if passed, would be the first time Congress codifies a "multi-prong approach to ensure that the U.S. remains ahead in the advanced technology race against the CCP." He said the bill will improve export control mechanisms – including for chips and high capacity chip manufacturing – protect covered AI technologies with a focus on cybersecurity, and limit outbound investment to firms directly tied to the Chinese Community Party or China's People's Liberation Army.

Israeli attacks have killed at least 42 people across Gaza since dawn, medical sources told Al Jazeera, as the United Nations General Assembly prepares for a vote urging an unconditional ceasefire in the besieged enclave. Sources told Al Jazeera that at least 26 of the people killed on Thursday died in Israeli drone attacks while waiting for food and basic supplies being distributed by the controversial United States and Israel-backed Gaza Humanitarian Foundation (GHF). Gaza civil defence official Mohammed el-Mougher told AFP news agency that al-Awda Hospital received at least 10 bodies and about 200 others who were wounded "after Israeli drones dropped multiple bombs on gatherings of civilians near an aid distribution point around the Netzarim checkpoint in central Gaza". El-Mougher said that Gaza City's al-Shifa Hospital also received six bodies after Israeli attacks on aid queues near Netzarim and in the as-Sudaniya area in northwestern Gaza. Since the GHF began its operation in Gaza in late May, dozens of Palestinians have been killed while trying to reach the aid distribution points, according to Gaza's civil defence agency.