Naheed.pk, a Pakistani online retailer, has expanded its services to over 30 cities, offering same-day delivery in Karachi, Lahore, and Islamabad. The company has seen 100% year-on-year growth and aims to capture 5% of Pakistan's retail market share. Naheed.pk focuses on providing genuine products and a trustworthy shopping experience to compete with brick-and-mortar stores. Why it matters: The growth of e-commerce platforms like Naheed.pk signals a shift in consumer behavior in Pakistan and the increasing importance of online retail in the region's economy.
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.
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.
Qurrat-Ul-Ain Nadeem, a Ph.D. student in electrical engineering at KAUST, is researching MIMO technology for 5G communication systems as part of the Communication Theory Lab (CTL). She holds a Bachelor's degree from LUMS, Pakistan, and previously completed her master's at KAUST in 2015. Nadeem chose KAUST over fully funded Ph.D. scholarships from Cornell and Wisconsin-Madison due to its research opportunities and diverse environment. Why it matters: This highlights KAUST's ability to attract top talent and contribute to advancements in 5G technology, showcasing the university's role in fostering cutting-edge research within the region.
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.
The Saudi Data and Artificial Intelligence Authority (SDAIA) has launched the 'Applied AI Bootcamp' in Riyadh. The program aims to train participants on the latest AI technologies and their practical applications. It will cover topics such as machine learning, deep learning, and data analysis, with a focus on solving real-world problems. Why it matters: This initiative signals Saudi Arabia's continued investment in building local AI talent and driving the adoption of AI across various sectors.