With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that support multiple languages. One task of interest is claim veracity prediction, which can be addressed using stance detection with respect to relevant documents retrieved online. To this end, we present our new Arabic Stance Detection dataset (AraStance) of 4,063 claimarticle pairs from a diverse set of sources comprising three fact-checking websites and one news website. AraStance covers false and true claims from multiple domains (e.g., politics, sports, health) and several Arab countries, and it is well-balanced between related and unrelated documents with respect to the claims. We benchmark AraStance, along with two other stance detection datasets, using a number of BERT-based models. Our best model achieves an accuracy of 85% and a macro F1 score of 78%, which leaves room for improvement and reflects the challenging nature of AraStance and the task of stance detection in general.

Within the framework of the ongoing implementation of the 2030 Vision for Comprehensive Development of Higher Education in Saudi Arabia, the integration of artificial intelligence (AI) has emerged as a pivotal objective for the country’s numerous higher education institutions. This study aims to examine the opportunities and challenges that arise from the adoption of AI-based learning outcomes in Saudi Arabia’s higher education institutes. Moreover, the research also investigates the contribution of major higher education institutes in Saudi Arabia to the field of AI-based learning outcomes. To gather relevant literature, the Scopus and Web of Science databases were utilised, resulting in the selection of fifty-five studies for final analysis. The study employed the PRISMA statement 2020 for records filtration and utilised VOS viewer software to classify the literature on AI-based learning outcomes in Saudi Arabian universities. Through detailed analysis, three significant data streams were identified and examined. The findings indicate that AI is in a nascent stage within the realm of learning, and it has become an undeniable reality for higher education institutions. Embracing this transformative technology is crucial for meeting future learning challenges, and it is imperative that all students acquire the necessary technical skills to interact with and create artificial intelligence in the future. According to the findings, AI has the potential to address significant educational challenges, revolutionise teaching and learning methodologies, and accelerate progress toward the Saudi 2030 objectives. However, the study also highlights certain challenges associated with the implementation of AI-based learning in the higher education context of Saudi Arabia, emphasising the need for teachers to acquire new technological skills to effectively utilise AI pedagogically.

We propose a novel multitask learning method for diacritization which trains a model to both diacritize and translate. Our method addresses data sparsity by exploiting large, readily avail able bitext corpora. Furthermore, transla tion requires implicit linguistic and seman tic knowledge, which is helpful for resolving ambiguities in diacritization. We apply our method to the Penn Arabic Treebank and re port a new stateoftheart word error rate of 4.79%. We also conduct manual and automatic analysis to better understand our method and highlight some of the remaining challenges in diacritization. Our method has applications in texttospeech, speechtospeech translation, and other NLP tasks. * Work done while at Apple. 1 Notable exceptions include the Quran and many chil dren's books.

The proliferation of mobile and IoT devices, coupled with the advances in the wireless communication capabilities of these devices, have urged the need for novel communication paradigms for such heterogeneous hybrid networks. Researchers have proposed opportunistic routing as a means to leverage the potentials offered by such heterogeneous networks. While several proposals for multiple opportunistic routing protocols exist, only a few have explored fuzzy logic to evaluate wireless links status in the network to construct stable and faster paths towards the destinations. We propose FQ-AGO, a novel Fuzzy Logic Q-learning Based Asymmetric Link Aware and Geographic Opportunistic Routing scheme that leverages the presence of long-range transmission links to assign forwarding candidates towards a given destination. The proposed routing scheme utilizes fuzzy logic to evaluate whether a wireless link is useful or not by capturing multiple network metrics, the available bandwidth, link quality, node transmission power, and distance progress. Based on the fuzzy logic evaluation, the proposed routing scheme employs a Q-learning algorithm to select the best candidate set toward the destination. We implemented FQ-AGO on the ns-3 simulator and compared the performance of the proposed routing scheme with three other relevant protocols: AODV, DSDV, and GOR. For precise analysis, we considered various network metrics to compare the performance of the routing protocols. The simulation result validates our analysis and demonstrates remarkable performance improvements in terms of total network throughput, packet delivery ration, and end-to-end delay. FQ-AGO achieves up to 15%, 50%, and 45% higher throughput compared to DSDV, AODV, and GOR, respectively. Meanwhile, FQ-AGO reduces by 50% the end-to-end latency and the average number of hop-count.

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