How well do you know Iran? The United Arab Emirates has said its air defences intercepted ballistic and cruise missiles fired from Iran, while a fire was reported at an oil facility in Fujairah after a suspected drone attack. Tehran has not officially commented. Qatar, Jordan, Saudi Arabia and Kuwait, along with the Gulf Cooperation Council (GCC) and the European Union, have condemned the suspected Iranian strike on the UAE. The incident comes as tensions rise, with United States President Donald Trump warning Iran would be "blown off the face of the earth" if US Navy ships are targeted in the Strait of Hormuz.

The United Arab Emirates has said its air defences are engaging with missile attacks and incoming drones from Iran. The UAE Ministry of Defense said late on Monday afternoon that it was intercepting ballistic missiles, cruise missiles, and drones across the country. The emirate of Fujairah said that an Iranian drone sparked a fire at an oil facility. Civil defence teams were deployed immediately to contain the blaze, the Fujairah Media office said in a statement. There were no immediate reports of casualties.

Hundreds of passengers flying to Dubai spent 11 hours on a flight to nowhere after their plane was turned back over Egypt. The Emirates flight EK24 set off from Edinburgh at 21:26 on Sunday and was due to land in Dubai at 06:49 on Monday. However, as the plane flew over Egypt, flights at Dubai International Airport were suspended following a fire caused by an Iranian drone hitting a fuel tank. The plane was forced to return to Edinburgh. Travel journalist Simon Calder told the BBC's Radio Scotland Breakfast programme that although Dubai was on the UK Foreign Office's No go list, many people were still taking the risk of flying there. No injuries were reported following the drone strike but officials said they had taken all necessary measures to ensure public safety.

This study presents acoustic-based methods for the control of multiple autonomous underwater vehicles (AUV). This study proposes two different models for implementing boundary and path control on low-cost AUVs using acoustic communication and a single central acoustic beacon. Two methods are presented: the Range Variation-Based (RVB) model completely relies on range data obtained by acoustic modems, whereas the Heading Estimation-Based (HEB) model uses ranges and range rates to estimate the position of the central boundary beacon and perform assigned behaviors. The models are tested on two boundary control behaviors: Fencing and Milling. Fencing behavior ensures AUVs return within predefined boundaries, while Milling enables the AUVs to move cyclically on a predefined path around the beacon. Models are validated by successfully performing the boundary control behaviors in simulations, pool tests, including artificial underwater currents, and field tests conducted in the ocean. All tests were performed with fully autonomous platforms, and no external input or sensor was provided to the AUVs during validation. Quantitative and qualitative analyses are presented in the study, focusing on the effect and application of a multi-robot system.

Text summarization tasks commonly employ Pre-trained Language Models (PLMs) to fit diverse standard datasets. While these PLMs excel in automatic evaluations, they frequently underperform in human evaluations, indicating a deviation between their generated summaries and human summarization preferences. This discrepancy is likely due to the low quality of fine-tuning datasets and the limited availability of high-quality human-annotated data that reflect true human preference. To address this challenge, we introduce a novel human summarization preference alignment framework AlignSum. This framework consists of three parts: Firstly, we construct a Data Pymarid with extractive, abstractive, and human-annotated summary data. Secondly, we conduct the Gaussian Resampling to remove summaries with extreme lengths. Finally, we implement the two-stage hierarchical fine-tuning with Data Pymarid after Gaussian Resampling. We apply AlignSum to PLMs on the human-annotated CNN/DailyMail and BBC XSum datasets. Experiments show that with AlignSum, PLMs like BART-Large surpass 175B GPT-3 in both automatic and human evaluations. This demonstrates that AlignSum significantly enhances the alignment of language models with human summarization preferences.

Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning. In this paper, we present Program-Guided Fact-Checking (ProgramFC), a novel fact-checking model that decomposes complex claims into simpler sub-tasks that can be solved using a shared library of specialized functions. We first leverage the in-context learning ability of large language models to generate reasoning programs to guide the verification process. Afterward, we execute the program by delegating each sub-task to the corresponding sub-task handler. This process makes our model both explanatory and data-efficient, providing clear explanations of its reasoning process and requiring minimal training data. We evaluate ProgramFC on two challenging fact-checking datasets and show that it outperforms seven fact-checking baselines across different settings of evidence availability, with explicit output programs that benefit human debugging. Our codes and data are publicly available at https://github.com/mbzuai-nlp/ProgramFC.

The core expertise of InData Labs is in Artificial Intelligence and Data Science, and they are proficient in advanced analytics languages and tools such as Python, R, Tensorflow, Keras, Alteryx, and others. InData Labs offers AI consulting and development services, as well as AI-powered mobile app development, to help clients grow their businesses. Development of customized solutions based on artificial intelligence from scratch. Development of products based on artificial intelligence. Indium Softwares' Machine Learning (ML) service enables companies to gain a competitive edge with privileges such as customer lifetime value prediction, proactive maintenance, spam detection, and more. Indium's motto is "making technology work," and they provide best-in-class machine learning algorithms as well as machine learning consulting solutions.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. DUBAI, United Arab Emirates (AP) – The United States has joined the United Kingdom and Israel in accusing Iran of carrying out a deadly drone strike that killed two aboard a tanker off Oman. U.S. Secretary of State Antony Blinken made the announcement in a statement Sunday. Blinken said: "Upon review of the available information, we are confident that Iran conducted this attack, which killed two innocent people, using one-way explosive (drones), a lethal capability it is increasingly employing throughout the region." He added that there was "no justification for this attack, which follows a pattern of attacks and other belligerent behavior."

A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The fact that automatic summarization may produce plausible-sounding yet inaccurate summaries is a major concern that limits its wide application. In this paper we present an approach to address factual consistency in summarization. We first propose an efficient automatic evaluation metric to measure factual consistency; next, we propose a novel learning algorithm that maximizes the proposed metric during model training. Through extensive experiments, we confirm that our method is effective in improving factual consistency and even overall quality of the summaries, as judged by both automatic metrics and human evaluation.

A new artificial intelligence (AI)-powered platform is set to supplement school education with live and inclusive learning opportunities for students, not only in the UAE but even across the world. Launched by Sheikh Mohammed bin Hamad bin Mohammed Al Sharqi, Crown Prince of Fujairah, Online Learning World (OLW) comes after extensive market research in the country revealed that the percentage of students taking private classes rose through the grades – and in Grade 12, around 37 per cent of all Emirati and 35 per cent of all non-Emirati students sought online learning. OLW aims to break down complicated chapters into simple and interesting lesson plans which clarify and enhance concept-based learning as per a specific board's curriculum. Subjects like Maths, English, Science, Coding, Arabic, Hindi and French, will be taught in a fun and interactive way with masterclasses for well being, including fitness, culinary, life skills and other short certificate courses also being high on the agenda. "This is a phenomenal platform that empowers the students seeking support to learn complex subjects in a simple and efficient manner. This is also revolutionary because it is on-demand – thereby empowering students and parents to select specific and tailored tutoring and plan their learning days," said Amreesh Chandra, president of OLW.