The US military has launched a mission to transfer up to 7,000 Islamic State (IS) group fighters from prisons in north-eastern Syria to Iraq, as Syrian government forces take control of areas long controlled by Kurdish-led forces. US Central Command said it had already moved 150 IS fighters from Hassakeh province to a secure location in Iraq. The move aimed to prevent a breakout that would pose a direct threat to the United States and regional security, it added. On Tuesday night, Syria's government announced a new ceasefire with the Kurdish-led Syrian Democratic Forces (SDF), after the militia alliance withdrew from al-Hol camp, which holds thousands of relatives of IS fighters. Separately on Wednesday, Syria's defence ministry said seven soldiers were killed in a drone attack by Kurdish forces in the Kurdish-dominated province of Hasakah.

Open-source intelligence provides a stream of unstructured textual data that can inform assessments of territorial control. We present CONTACT, a framework for territorial control prediction using large language models (LLMs) and minimal supervision. We evaluate two approaches: SetFit, an embedding-based few-shot classifier, and a prompt tuning method applied to BLOOMZ-560m, a multilingual generative LLM. Our model is trained on a small hand-labeled dataset of news articles covering ISIS activity in Syria and Iraq, using prompt-conditioned extraction of control-relevant signals such as military operations, casualties, and location references. We show that the BLOOMZ-based model outperforms the SetFit baseline, and that prompt-based supervision improves generalization in low-resource settings. CONTACT demonstrates that LLMs fine-tuned using few-shot methods can reduce annotation burdens and support structured inference from open-ended OSINT streams. Our code is available at https://github.com/PaulKMandal/CONTACT/.

Despite the successes of language models, their evaluation remains a daunting challenge for new and existing tasks. We consider the task of text simplification, commonly used to improve information accessibility, where evaluation faces two major challenges. First, the data in existing benchmarks might not reflect the capabilities of current language models on the task, often containing disfluent, incoherent, or simplistic examples. Second, existing human ratings associated with the benchmarks often contain a high degree of disagreement, resulting in inconsistent ratings; nevertheless, existing metrics still have to show higher correlations with these imperfect ratings. As a result, evaluation for the task is not reliable and does not reflect expected trends (e.g., more powerful models being assigned higher scores). We address these challenges for the task of text simplification through three contributions. First, we introduce SynthSimpliEval, a synthetic benchmark for text simplification featuring simplified sentences generated by models of varying sizes. Through a pilot study, we show that human ratings on our benchmark exhibit high inter-annotator agreement and reflect the expected trend: larger models produce higher-quality simplifications. Second, we show that auto-evaluation with a panel of LLM judges (LLMs-as-a-jury) often suffices to obtain consistent ratings for the evaluation of text simplification. Third, we demonstrate that existing learnable metrics for text simplification benefit from training on our LLMs-as-a-jury-rated synthetic data, closing the gap with pure LLMs-as-a-jury for evaluation. Overall, through our case study on text simplification, we show that a reliable evaluation requires higher quality test data, which could be obtained through synthetic data and LLMs-as-a-jury ratings.

FOX News' Greg Palkot reports the latest from the Israel-Lebanon border. A drone attack on a U.S. base in Syria was thwarted on Wednesday, according to a report. Two drones targeting Syria's al-Tanf region were disabled or destroyed by the base defense system, an Iraqi government source told Reuters. The thwarted attack comes as U.S. and Coalition Forces at Combined Joint Task Force Operation Inherent Resolve (CJTF-OIR) installations in Iraq and Syria have been attacked at least 27 times between Oct. 17-31. Of these attacks, 16 happened in Iraq and 11 took place in Syria.

State-of-the-art object detection methods applied to satellite and drone imagery largely fail to identify small and dense objects. One reason is the high variability of content in the overhead imagery due to the terrestrial region captured and the high variability of acquisition conditions. Another reason is that the number and size of objects in aerial imagery are very different than in the consumer data. In this work, we propose a small object detection pipeline that improves the feature extraction process by spatial pyramid pooling, cross-stage partial networks, heatmap-based region proposal network, and object localization and identification through a novel image difficulty score that adapts the overall focal loss measure based on the image difficulty. Next, we propose novel contrastive learning with progressive domain adaptation to produce domain-invariant features across aerial datasets using local and global components. We show we can alleviate the degradation of object identification in previously unseen datasets. We create a first-ever domain adaptation benchmark using contrastive learning for the object detection task in highly imbalanced satellite datasets with significant domain gaps and dominant small objects. The proposed method results in a 7.4% increase in mAP performance measure over the best state-of-art.

Pro-Iranian forces in Syria have said they have a "long arm" to respond to further United States air strikes on their positions, after tit-for-tat missile and drone attacks in Syria over the last 24 hours. The online statement, released late on Friday and signed by the Iranian Advisory Committee in Syria, said US air strikes had left several of their fighters dead and wounded, without specifying the fighters' nationality. "We have the capability to respond if our centres and forces in Syria are targeted," the statement said. On Friday night, two Syrian opposition activist groups reported a new wave of US air attacks on eastern Syria, which hit positions of Iran-backed militias, after rockets were fired at bases in Syria housing US troops. Several US officials, however, denied that attacks were launched late on Friday.

AFPI Center for American Security's Fred Fleitz on the U.S. response to an Iranian drone attack that killed one American, TikTok CEO claims app is not influenced by China, and Antony Blinken says Taliban still detaining Americans in Afghanistan Iran proxy forces launched about seven rockets targeting a U.S. base in Northeast Syria today in retaliation to the U.S., a defense official confirms to Fox News. In first assessments, there are no U.S. casualties and no damage to the base near the Al-Omar oil field. The rocket attacks came after President Biden ordered a series of retaliatory strikes after a suspected Iranian-made drone killed a U.S. contractor and wounded six other Americans on Thursday. U.S. Defense Secretary Lloyd Austin said in a statement that the American intelligence community had determined the drone was of Iranian origin, but offered no other immediate evidence to support the claim. The drone hit a coalition base in the northeast Syrian city of Hasaka.

The United States military has said it carried out multiple air strikes in eastern Syria against Iran-aligned groups who it blamed for a deadly drone attack earlier that killed a contractor, injured another, and wounded five US troops, the Pentagon said. The US attacks late on Thursday night were in retaliation for an attack against a US-led coalition base near Hassakeh in northeast Syria at approximately 01:38pm (10:38 GMT) the same day, the Pentagon said in a statement. US intelligence has assessed that the drone was Iranian in origin and US Defence Secretary Lloyd Austin said the strikes targeted groups affiliated with Iran's Islamic Revolutionary Guards Corps in eastern Syria. "The airstrikes were conducted in response to today's attack as well as a series of recent attacks against Coalition forces in Syria by groups affiliated with the IRGC," Austin said in a statement. Austin said he authorised the retaliatory strikes at the direction of US President Joe Biden.

Building destruction during war is a specific form of violence which is particularly harmful to civilians, commonly used to displace populations, and therefore warrants special attention. Yet, data from war-ridden areas are typically scarce, often incomplete and highly contested, when available. The lack of such data from conflict zones severely limits media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, as well as the study of violent conflict in academic research. One approach has been to use remote sensing to identify destruction in satellite images[1]. This approach is gaining momentum as high-resolution imagery is becoming readily available and is updated ever quicker yielding weekly or even daily frequency. At the same time recent methodological advances related to deep learning have provided sophisticated tools to extract data from these images [2, 3, 4, 5].

Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. We model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our approach outperforms a competitive baseline by 10.2% in precision.