Emirates News Agency (WAM) launched a new management system during the Global Media Congress. This system aims to enhance news operations, content creation, and distribution efficiency for the national news agency. It represents a significant step in modernizing WAM's internal technological infrastructure. Why it matters: This initiative underscores the UAE's commitment to digital transformation within its media sector, potentially leveraging advanced technologies to improve information dissemination across the region.
This paper presents six experiments evaluating personalization and user tracking in web search engine results. The experiments involve comparing search results based on VPN location (including UAE vs others), logged-in status, network type, search engine, browser, and trained Google accounts. The study measures total hits, first hit, and correlation between hits to identify patterns of personalization. Why it matters: The findings shed light on the extent of filter bubble effects and potential biases in search results for users in the UAE and globally.
KAUST researchers analyzed bacterial communities from Deception Island, Antarctica, finding heat-loving bacteria with potential for oil cleanup. Postdoctoral student Junia Schultz is now characterizing the microbiome of extreme terrestrial environments in Saudi Arabia, including volcanoes and deserts. These extremophiles secrete surfactants to break down oil and absorb it into their cells for degradation. Why it matters: This research could lead to efficient and safe methods for cleaning up oil contamination using extremophiles found in both Antarctica and Saudi Arabia.
This paper presents a benchmark study of contrastive learning (CL) methods applied to Arabic social meaning tasks like sentiment analysis and dialect identification. The study compares state-of-the-art supervised CL techniques against vanilla fine-tuning across a range of tasks. Results indicate that CL methods outperform vanilla fine-tuning in most cases and demonstrate data efficiency. Why it matters: This work highlights the potential of contrastive learning for improving performance in Arabic NLP, especially in low-resource scenarios.
The third Nuanced Arabic Dialect Identification Shared Task (NADI 2022) focused on advancing Arabic NLP through dialect identification and sentiment analysis at the country level. A total of 21 teams participated, with the winning team achieving 27.06 F1 score on dialect identification and 75.16 F1 score on sentiment analysis. The task highlights the challenges in Arabic dialect processing and motivates further research. Why it matters: Standardized evaluations like NADI are crucial for benchmarking progress and fostering innovation in Arabic NLP, especially for dialectal variations.
Researchers have developed Masader Plus, a web interface for browsing the Masader catalog of Arabic NLP datasets. The interface allows for data exploration, filtration, and API access to examine datasets. User interactions with the website are intended to provide a way to improve the dataset catalog itself. Why it matters: This interface lowers the barrier to entry for researchers seeking Arabic NLP datasets, facilitating more research in the field.
The Emirates News Agency (WAM) and Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) have signed a strategic partnership. The collaboration aims to advance knowledge and scientific research in AI, particularly in media. It will focus on developing innovative AI applications to enhance news content and delivery. Why it matters: This partnership signifies a growing trend of integrating AI into media in the UAE, potentially improving the quality and efficiency of news dissemination.
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.
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.
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.
Researchers developed COVIBOT, a smart chatbot to spread awareness and assist during the COVID-19 pandemic in Saudi Arabia. The chatbot uses Azure Cognitive Services and is available in both English and Arabic. COVIBOT's use cases were tested and validated using a scenario-based approach.
This paper presents an experience report on teaching an AI course to business executives in the UAE. The course focuses on enabling students to understand how to incorporate AI into existing business processes, rather than focusing only on theoretical and technical aspects. The paper discusses the course overview, curriculum, teaching methods, and reflections on teaching adult learners in the UAE.
KAUST's first Arabic MOOC, Entrepreneurship Adventures, on edX.org saw 17,000 learners attend the 10-week course, with 81% from Saudi Arabia. The free online course, developed by KAUST's Entrepreneurship Center, aims to provide fundamentals of entrepreneurship in Arabic. A survey showed that 74% of participants felt confident to join a startup and 60% would pursue entrepreneurship. Why it matters: This initiative helps break down barriers to entry for young Saudi entrepreneurs, fostering local talent and contributing to the Kingdom's economic diversification goals.
KAUST participated in SC21, the international supercomputing conference in St. Louis, Missouri, marking their 13th year of attendance. KAUST highlighted its commitment to being a supercomputing presence in the Arab world and its role in propelling research across scientific disciplines. KAUST graduates are contributing talent across the Middle East, and KAUST Computational Scientist Bilel Hadri will chair the reproducibility initiative at SC22. Why it matters: KAUST's involvement in SC21 underscores the growing importance of HPC in the Middle East and its contribution to building a knowledge-based economy.
MIT Technology Review reports on advancements in machine learning techniques that are significantly improving Arabic speech transcription capabilities. These developments aim to enhance the accuracy and robustness of Automatic Speech Recognition (ASR) systems for the complexities of the Arabic language, including its various dialects. The improvements are designed to overcome previous challenges in processing diverse phonetic patterns and linguistic nuances. Why it matters: This progress is vital for the development of more effective voice-enabled technologies, accessibility tools, and AI applications specifically tailored for Arabic-speaking populations in the Middle East and beyond.
The paper examines the performance of pre-trained Arabic language models on Arabic text intentionally stripped of diacritical dots to evade content classification. It proposes methods to support these "undotted" texts without retraining the models. The proposed methods achieve nearly perfect performance on one downstream task. Why it matters: The research highlights a vulnerability in Arabic NLP and offers solutions to maintain performance in the face of adversarial text manipulation.
This paper presents a comparative study of pre-trained transformer models for Arabic question answering (QA). The study evaluates the performance of AraBERTv2-base, AraBERTv0.2-large, and AraELECTRA models on four reading comprehension datasets: Arabic-SQuAD, ARCD, AQAD, and TyDiQA-GoldP. The researchers fine-tuned these models and analyzed the results to understand the performance disparities. Why it matters: This research contributes to the advancement of Arabic NLP by evaluating and comparing state-of-the-art models on important QA tasks, addressing the scarcity of resources in this domain.
Researchers created Masader, the largest public catalog for Arabic NLP datasets, containing 200 datasets annotated with 25 attributes. They developed a metadata annotation strategy applicable to other languages. The paper highlights issues within current Arabic NLP datasets and suggests recommendations. Why it matters: This curated dataset directory helps lower the barrier to entry for Arabic NLP research and development.
KAUST organized an Arabic Sentiment Analysis Challenge where participants developed ML models to classify tweets as positive, negative, or neutral. The competition used the ASAD dataset with 55K tweets for training, 20K for validation, and 20K for final evaluation. The full dataset of 100K labeled tweets has been released for public use.
KAUST has launched its first Massive Open Online Course (MOOC) titled "Entrepreneurship Adventures" on edX, starting July 4, 2021. The gamified masterclass is the first entrepreneurship course in Arabic on the edX platform. It features industry experts like Amin Al-Nasser (Saudi Aramco) and Lubna Olayan (Saudi British Bank). Why it matters: The course aligns with Saudi Vision 2030, addressing the increasing demand for entrepreneurship education and supporting the Kingdom's startup ecosystem.
This paper examines the relationship between COVID-19 spread and weather patterns across 89 cities in Saudi Arabia using machine learning. The study uses daily COVID-19 case reports from the Saudi Ministry of Health and historical weather data. The results indicate that temperature and wind speed have the strongest correlation with the spread of COVID-19, with a random forest model achieving the best performance.
This paper focuses on analyzing surveys of women entrepreneurs in the UAE using machine learning techniques. The goal is to extract relevant insights from the data to understand the current landscape and predict future trends. The study aims to support better business decisions related to women in entrepreneurship.
This paper presents team SPPU-AASM's hybrid model for Arabic sarcasm and sentiment detection in the WANLP ArSarcasm shared task 2021. The model combines sentence representations from AraBERT with static word vectors trained on Arabic social media corpora. Results show the system achieves an F1-sarcastic score of 0.62 and a F-PN score of 0.715, outperforming existing approaches. Why it matters: The research demonstrates that combining context-free and contextualized representations improves performance in nuanced Arabic NLP tasks like sarcasm and sentiment analysis.
Researchers studied user lifetime prediction in the location-based social network Jodel within Saudi Arabia, leveraging its disjoint communities. Machine learning models, particularly Random Forest, were trained to predict user lifetime as a regression and classification problem. A single countrywide model generalizes well and performs similarly to community-specific models.
Oman is actively pursuing a digital transformation agenda focused on integrating emerging technologies, particularly Artificial Intelligence, to drive its future economic growth. This strategic initiative aims to diversify the national economy, enhance public service delivery, and foster innovation across various sectors within the Sultanate. Efforts include developing robust digital infrastructure, attracting foreign and local investment, and upskilling the national workforce to meet future technological demands. Why it matters: This highlights Oman's national commitment to leveraging advanced technologies as a fundamental pillar of its long-term development strategy and to boost its global competitiveness.
KAUST and Lean Business Services (the digital arm of the Saudi health sector) have signed a Memorandum of Understanding to advance AI and data science for a "smart health" ecosystem in Saudi Arabia. During the COVID-19 pandemic, they collaborated to provide the Saudi Ministry of Health with AI-driven trends and analysis. KAUST's Shaheen II supercomputer was utilized to process healthcare data. Why it matters: This partnership aims to develop AI and advanced analytics solutions to enhance healthcare access, quality, and cost-efficiency in line with Saudi Vision 2030.
This study investigates the correlation between Google Trends data for COVID-19 symptoms and the actual number of COVID-19 cases in Saudi Arabia between March and October 2020. The researchers found that searches for "cough" and "sore throat" were most frequent, while "loss of smell", "loss of taste", and "diarrhea" showed the highest correlation with confirmed cases. The study concludes that Google searches can serve as a supplementary surveillance tool for monitoring the spread of COVID-19 in Saudi Arabia. Why it matters: The research demonstrates the potential of using readily available digital data to augment traditional surveillance methods for public health monitoring in the region.
This survey paper reviews the landscape of Natural Language Processing (NLP) research and applications in the Arab world. It discusses the unique challenges posed by the Arabic language, such as its morphological complexity and dialectal diversity. The paper also presents a historical overview of Arabic NLP and surveys various research areas, including machine translation, sentiment analysis, and speech recognition. Why it matters: The survey provides a comprehensive resource for researchers and practitioners interested in the current state and future directions of Arabic NLP, a field critical for enabling AI technologies to serve Arabic-speaking communities.
Researchers introduce ASAD, a new large-scale, high-quality Arabic Sentiment Analysis Dataset based on 95K tweets with positive, negative, and neutral labels. The dataset is launched with a competition sponsored by KAUST offering a total of 17000 USD in prizes. Baseline models are implemented and results reported to provide a reference for competition participants.
This paper proposes a smart dome system for mosques that uses machine learning to automatically control dome ventilation based on weather conditions and outside temperatures. The system was tested on the Prophet Mosque in Saudi Arabia using K-Nearest Neighbors and Decision Tree algorithms. The Decision Tree algorithm achieved a higher accuracy of 98% compared to 95% for the k-NN algorithm.
Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.
Scientists at the Qatar Computing Research Institute (QCRI) have extensively utilized big data and artificial intelligence technologies in the global fight against the COVID-19 pandemic. Their work involved developing tools and methodologies to track disease spread, predict outbreaks, and analyze public sentiment related to health measures. This research aimed to provide data-driven insights to inform public health strategies and decision-making processes. Why it matters: This highlights the significant role of Middle Eastern research institutions in leveraging advanced technologies to address global health crises and contribute to scientific solutions.
The Inception Team presented a system for Semantic Question Similarity in Arabic as part of the NSURL 2019 Task 8. The system explores different methods for determining question similarity in Arabic. Their best result was an ensemble model using a pre-trained multilingual BERT model, achieving a 95.924% F1-Score and ranking first among nine participating teams. Why it matters: This demonstrates strong performance on a key Arabic NLP task, advancing the state-of-the-art in semantic understanding for the language.
KAUST Associate Professor Xiangliang Zhang is using machine learning to analyze social media posts on Twitter related to COVID-19. Her team at KAUST's Computational Bioscience Research Center is analyzing sentiment in tweets using hashtags like #coronavirus and #covid19. Zhang aims to use this data to help predict localized outbreaks and provide an early warning system for governments and organizations. Why it matters: This research demonstrates the potential of AI-powered sentiment analysis to support public health efforts and inform decision-making during pandemics in the Middle East and globally.
Timothy Baldwin, president of Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), expressed optimism about the Middle East's potential to become a significant player in AI. He noted the region's strategic investments and focus on AI development. Baldwin acknowledged that while the Middle East currently lags behind other regions, it has the resources and vision to close the gap. Why it matters: The president's statement highlights the growing ambition and commitment within the Middle East to establish itself as a global AI hub.
KAUST researchers have discovered that combining ultraviolet sunlight with phages increases the susceptibility of antibiotic-resistant bacteria to sunlight disinfection. This breakthrough addresses the growing threat of antimicrobial resistance, as the rate of discovering new antibiotics has slowed. The team demonstrated this method's effectiveness against a pathogenic E. coli strain found in Saudi wastewater. Why it matters: This research offers a promising alternative to traditional antibiotics, particularly relevant in regions like Singapore and the GCC where treated wastewater is a crucial water supply source.
KAUST research scientist Dr. Maged Saad is working on unconventional methods for global food security within the Desert Agriculture Initiative. His research involves using selected strains of bacteria to increase salt tolerance and crop productivity in desert plants. Dr. Saad aims to convert this technology into a marketable product by securing intellectual property rights, testing prototypes with Saudi farmers, and establishing a startup. Why it matters: This research aligns with Saudi Arabia's Vision 2030 goals to enhance local agricultural production and promote sustainable solutions for food security in arid environments.
KAUST Library Director Dr. J. K. Vijayakumar has been elected to the board of directors of the International Association of University Libraries (IATUL). KAUST has been a member of IATUL since 2009, which provides a forum for library directors to discuss library service development. IATUL's president noted Vijayakumar was well-suited to foster IATUL's ambition to serve as a bridge between cultures. Why it matters: This appointment recognizes KAUST's growing role in international academic collaborations and knowledge sharing.
This paper explores language-independent alternatives to morphological segmentation for Arabic NLP using data-driven sub-word units, characters as a unit of learning, and word embeddings learned using a character CNN. The study evaluates these methods on machine translation and POS tagging tasks. Results show these methods achieve performance close to or surpassing state-of-the-art approaches. Why it matters: By offering simpler, more adaptable segmentation techniques, this research can help improve Arabic NLP applications across diverse domains and dialects.
This paper describes the MIT-QCRI team's Arabic Dialect Identification (ADI) system developed for the 2017 Multi-Genre Broadcast challenge (MGB-3). The system aims to distinguish between four major Arabic dialects and Modern Standard Arabic. The research explores Siamese neural network models and i-vector post-processing to handle dialect variability and domain mismatches, using both acoustic and linguistic features. Why it matters: The work contributes to the advancement of Arabic language processing, specifically in dialect identification, which is crucial for analyzing and understanding diverse Arabic speech content in media broadcasts.
The paper introduces Guided Deep List, a tool for automating the generation of epidemiological line lists from open source reports. The tool uses distributed vector representations and dependency parsing to extract tabular data on disease outbreaks. It was evaluated on MERS outbreak data in Saudi Arabia, demonstrating improved accuracy over baseline methods and enabling epidemiological inferences.
This paper describes QCRI's machine translation systems for the IWSLT 2016 evaluation campaign, focusing on Arabic-English and English-Arabic tracks. They built both Phrase-based and Neural machine translation models. A Neural MT system, trained by stacking data from different genres through fine-tuning, and applying ensemble over 8 models, outperformed a strong phrase-based system by 2 BLEU points in the Arabic->English direction. Why it matters: The research highlights the early promise of neural machine translation for Arabic language pairs, demonstrating its potential to surpass traditional methods.
This paper describes the QCRI-Columbia-NYUAD group's Egyptian Arabic-to-English statistical machine translation system submitted to the NIST OpenMT'2015 competition. The system used tools like 3arrib and MADAMIRA for processing and standardizing informal dialectal Arabic. The system was trained using phrase-based SMT with features such as operation sequence model, class-based language model and neural network joint model. Why it matters: The work demonstrates advances in machine translation for dialectal Arabic, a challenging but important area for regional communication and NLP research.
A study analyzes spam content on trending hashtags on Saudi Twitter, finding that approximately 75% of the total generated content is spam. The paper assesses the performance of previous spam detection systems on a newly gathered dataset and proposes an updated manual classification algorithm to improve accuracy. Adapted features are used to build a new data-driven detection system to respond to spammers' evolving techniques. Why it matters: The high prevalence of spam in Arabic content on Twitter necessitates the development of adaptive detection techniques to maintain the quality and trustworthiness of online information in the region.
KAUST Associate Professor Liming Xiong is researching how plants adapt to drought conditions, focusing on reducing water consumption, increasing water uptake, and surviving under stress. His "whole plant" approach aims to identify major genes controlling water uptake, water loss, and cellular detoxification. The research seeks to develop plants that use water more efficiently or can be irrigated with brackish water, important for agriculture in Saudi Arabia. Why it matters: Understanding the molecular mechanisms of plant drought tolerance can help in breeding stress-tolerant crops suitable for the arid conditions in the region.
Researchers from Alexandria University introduce AlexU-Word, a new dataset for offline Arabic handwriting recognition. The dataset contains 25,114 samples of 109 unique Arabic words, covering all letter shapes, collected from 907 writers. The dataset is designed for closed-vocabulary word recognition and to support segmented letter recognition-based systems. Why it matters: This dataset can help advance Arabic handwriting recognition systems, addressing a need for high-quality Arabic datasets in NLP research.
This paper introduces a novel dataset for Arabic handwritten isolated alphabet letters to serve as a benchmark for future research. The study presents a comparative evaluation of window-based descriptors for Arabic handwritten alphabet recognition, testing different descriptors with various classifiers. The experiments demonstrate that window-based descriptors perform well, especially when combined with a novel spatial pyramid partitioning scheme. Why it matters: The new dataset and analysis of descriptors will help advance Arabic OCR and handwritten text recognition systems.
KAUST's Salt Lab, led by Professor Mark Tester, is researching how salt-tolerant plants survive in harsh environments. The lab aims to improve plant yields in suboptimal conditions, focusing on naturally occurring variability in plants to enhance salinity tolerance. With 70% of global water used for agriculture and increasing water scarcity, the research seeks to unlock the potential of seawater for irrigation. Why it matters: Enhancing the salinity tolerance of crops is crucial for addressing food security challenges exacerbated by climate change and the growing global population, particularly in arid regions like the Middle East.
A proposed recognition system aims to identify missing persons, deceased individuals, and lost objects during the Hajj and Umrah pilgrimages in Saudi Arabia. The system intends to leverage facial recognition and object identification to manage the large crowds expected in the coming decade, estimated to reach 20 million pilgrims. It will be integrated into the CrowdSensing system for crowd estimation, management, and safety.
This paper introduces a new non-statistical Arabic lemmatizer algorithm designed for information retrieval systems. The lemmatizer leverages Arabic language knowledge resources to generate accurate lemma forms and relevant features. The algorithm achieves a maximum accuracy of 94.8% and 89.15% on first seen documents, outperforming the Stanford Arabic model's 76.7% on the same dataset. Why it matters: Accurate Arabic lemmatization is crucial for improving the performance of Arabic information retrieval systems, which can enhance access to Arabic language content.