Why is Israel still in southern Lebanon? A war to shape Lebanon's future Two people have been killed in an Israeli drone strike on a minibus in eastern Lebanon as near-daily ceasefire violations continue, Lebanese state media reported. Lebanon's National News Agency (NNA) said on Thursday that the drone hit the vehicle on the Hosh al-Sayyed Ali road in the Hermel district. Israeli military spokesperson Avichay Adraee claimed on X that Thursday's strike targeted a "terrorist operative" in al-Nasiriyah in eastern Lebanon. The attack came hours after a passerby was injured in an Israeli drone strike targeting a car in the town of Jennata in the Tyre district of southern Lebanon late on Wednesday.

This is particularly true for language tasks that are culture-dependent.

Mixed modality search -- retrieving information across a heterogeneous corpus composed of images, texts, and multimodal documents -- is an important yet underexplored real-world application. In this work, we investigate how contrastive vision-language models, such as CLIP, perform on the mixed modality search task. Our analysis reveals a critical limitation: these models exhibit a pronounced modality gap in the embedding space, where image and text embeddings form distinct clusters, leading to intra-modal ranking bias and inter-modal fusion failure. To address this issue, we propose GR-CLIP, a lightweight post-hoc calibration method that removes the modality gap in CLIP's embedding space. Evaluated on MixBench -- the first benchmark specifically designed for mixed modality search -- GR-CLIP improves NDCG@10 by up to 26 percentage points over CLIP, surpasses recent vision-language generative embedding models by 4 percentage points, while using 75x less compute.

Lebanon's president says his country wants peace but not normalisation with Israel, as health authorities said an Israeli air strike killed one person in the south of the country. As well as causing one death on Friday, the drone attack on a car in Nabatieh district wounded five other people, according to Lebanon's Ministry of Health. It comes as Israel continues to launch regular strikes against sites in Lebanon, particularly in the south, despite a November 27 ceasefire agreement between it and the Lebanese armed group Hezbollah. Under the terms of the truce, Hezbollah had to retreat to the north of the Litani River, which is about 30km (20 miles) from the Israeli border, while Israel had to fully withdraw its troops, leaving only the Lebanese army and United Nations peacekeepers in the area. However, Israel still occupies five strategic locations in southern Lebanon.

An Israeli drone attack has killed at least one person and injured three near the Lebanese capital, Beirut, the Lebanese Ministry of Public Health says, the latest violation of the ceasefire between the two countries. The air raid on Thursday hit a vehicle on a busy motorway in the Khaldeh area, about 12km (8 miles) south of Beirut. The Israeli military said it targeted "military sites and weapons depots" in the area. Bombing an area near the Lebanese capital marks another escalation by Israel, which has been carrying out near-daily bombardment in Lebanon since it reached a truce with Hezbollah in November of last year. The identities of the victims of the attack have not been released.

We introduce Konooz, a novel multi-dimensional corpus covering 16 Arabic dialects across 10 domains, resulting in 160 distinct corpora. The corpus comprises about 777k tokens, carefully collected and manually annotated with 21 entity types using both nested and flat annotation schemes - using the Wojood guidelines. While Konooz is useful for various NLP tasks like domain adaptation and transfer learning, this paper primarily focuses on benchmarking existing Arabic Named Entity Recognition (NER) models, especially cross-domain and cross-dialect model performance. Our benchmarking of four Arabic NER models using Konooz reveals a significant drop in performance of up to 38% when compared to the in-distribution data. Furthermore, we present an in-depth analysis of domain and dialect divergence and the impact of resource scarcity. We also measured the overlap between domains and dialects using the Maximum Mean Discrepancy (MMD) metric, and illustrated why certain NER models perform better on specific dialects and domains. Konooz is open-source and publicly available at https://sina.birzeit.edu/wojood/#download

Overall the paper reads like a nice combination of existing tricks, and provides very convincing experimental results. Strengths of the paper are simplicity and a relatively straightforward idea, but not trivial to implement/test. The experimental section is therefore a strong part of this paper. Things to improve: handle better the interplay between regularized/not regularized formulations, be more rigorous with maths (computations/notations are a bit sloppy) and ideally provide an algorithmic box to see more clearly into what the authors propose. A few minor comments: - In Eq.1, the Euclidean distance between word embeddings is used as a cost, in Eq.6, for the purpose of Malahanobis metric learning, that cost becomes the squared euclidean metric (and thus what is usually referred to as 2-Wasserstein).

An Israeli drone strike that has killed one person in a south Lebanon village targeted a municipal worker operating a water well, not a Hezbollah member as the Israeli military had claimed, according to the Mayor of Nabatieh al-Fawqa Zein Ali Ghandour. Ghandour said on Thursday that the victim, Mahmoud Hasan Atwi, was "martyred" while on his official duty of trying to provide water for the people of the town. "We condemn in the strongest terms this blatant aggression against civilians and civilian infrastructure as well as the Lebanese state and its institutions," the mayor said in a statement. Ghandour called on the international community to press the issue and put an end to Israeli violations. The Israeli military had claimed that it fired at a "Hezbollah operative" who it said was "rehabilitating a site" used by the group.

Zero-Touch Networks (ZTNs) represent a state-of-the-art paradigm shift towards fully automated and intelligent network management, enabling the automation and intelligence required to manage the complexity, scale, and dynamic nature of next-generation (6G) networks. ZTNs leverage Artificial Intelligence (AI) and Machine Learning (ML) to enhance operational efficiency, support intelligent decision-making, and ensure effective resource allocation. However, the implementation of ZTNs is subject to security challenges that need to be resolved to achieve their full potential. In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of adversarial attacks targeting AI/ML models. In this survey paper, we provide a comprehensive review of current security issues in ZTNs, emphasizing the need for advanced AI/ML-based security mechanisms that require minimal human intervention and protect AI/ML models themselves. Furthermore, we explore the potential of Automated ML (AutoML) technologies in developing robust security solutions for ZTNs. Through case studies, we illustrate practical approaches to securing ZTNs against both conventional and AI/ML-specific threats, including the development of autonomous intrusion detection systems and strategies to combat Adversarial ML (AML) attacks. The paper concludes with a discussion of the future research directions for the development of ZTN security approaches.