arXiv ·
A novel agent-based framework called FIRE is introduced for fact-checking long-form text. FIRE iteratively integrates evidence retrieval and claim verification, deciding whether to provide a final answer or generate a subsequent search query. Experiments show FIRE achieves comparable performance to existing methods while reducing LLM costs by 7.6x and search costs by 16.5x.
arXiv ·
This paper presents the design and deployment of an autonomous unmanned ground vehicle (UGV) equipped with a robotic arm for urban firefighting. The UGV uses on-board sensors for navigation and a thermal camera for fire source identification, with a custom pump for fire suppression. The system was developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020, where it achieved the highest score among UGV solutions and contributed to winning first place. Why it matters: This demonstrates the potential of autonomous robotics in addressing complex and dangerous real-world challenges like urban firefighting in the GCC region and beyond.
arXiv ·
The UrduFake@FIRE2021 shared task focused on fake news detection in the Urdu language, framed as a binary classification problem. 34 teams registered, with 18 submitting results and 11 providing technical reports, showcasing diverse approaches. The top-performing system utilized the stochastic gradient descent (SGD) algorithm, achieving an F-score of 0.679.
arXiv ·
This paper presents a UAV-UGV team designed for autonomous firefighting, developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The system uses LiDAR for localization in GNSS-restricted environments and fuses LiDAR and thermal camera data to track fires. Relative navigation enables successful fire extinguishing. Why it matters: This research demonstrates the potential of robotic systems in autonomous firefighting, addressing challenges in dangerous and inaccessible environments, and advancing robotics research within the UAE.
arXiv ·
This paper provides an overview of the UrduFake@FIRE2021 shared task, which focused on fake news detection in the Urdu language. The task involved binary classification of news articles into real or fake categories using a dataset of 1300 training and 300 testing articles across five domains. 34 teams registered, with 18 submitting results and 11 providing technical reports detailing various approaches from BoW to Transformer models, with the best system achieving an F1-macro score of 0.679.
KAUST ·
KAUST's Winter Enrichment Program (WEP) 2021, themed "connectivity," will take place virtually from January 10-21 with over 60 speakers. The program will explore various facets of connectivity, from technological advancements to personal relationships, and address both its benefits and challenges, such as cybersecurity threats. The program was planned before the pandemic but its themes have only become more relevant. Why it matters: The WEP program provides a platform for discussing the evolving role of connectivity in a rapidly changing world, with a focus on technology and society.
arXiv ·
Team NimbRo presented four UAVs tailored for the MBZIRC 2020 challenges, including target chasing, wall building, and fire fighting. The UAVs utilized onboard object detection, aerial manipulation, LiDAR, and thermal cameras to perform their tasks autonomously. The team's software stack, which is mostly open-source, includes tools for system configuration, monitoring, and agile trajectory generation. Why it matters: The work demonstrates advanced robotics capabilities developed in the context of a major regional competition, advancing machine vision and trajectory generation, and showcasing potential applications in various sectors.
arXiv ·
This paper introduces two shared tasks for abusive and threatening language detection in Urdu, a low-resource language with over 170 million speakers. The tasks involve binary classification of Urdu tweets into Abusive/Non-Abusive and Threatening/Non-Threatening categories, respectively. Datasets of 2400/6000 training tweets and 1100/3950 testing tweets were created and manually annotated, along with logistic regression and BERT-based baselines. 21 teams participated and the best systems achieved F1-scores of 0.880 and 0.545 on the abusive and threatening language tasks, respectively, with m-BERT showing the best performance.