AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results show that out-of-domain generalization remains a major bottleneck and highlight the effectiveness of recent Arabic-specialized speech models, multimodal dialect identification approaches, and ensemble methods.
This paper presents the Mawqif-XT, consisting of 996 manually annotated Arabic tweets collected from three public targets: Women Driving, E-Cars, and Trimester System, which provides a benchmark for evaluating cross-target generalization in Arabic stance detection.
BUL, a multi-dialect Arabic ASR dataset collected from 275 speakers in 11 Arab countries, includes structured dialect and sub-dialect coverage, as well as recordings of classical Arabic and modern standard Arabic spoken by participants in their native dialectal accents to support accent-aware modeling.
The findings suggest that the impact of GenAI use is present in various contexts, highlighting the need for instructional guidance on how students should use GenAI as a learning aid, and insights for other instructors that wish to integrate GenAI tools into computing curricula.
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