Spatial-Temporal Graph Representation Learning for Tactical Networks Future State Prediction
Resource allocation in tactical ad-hoc networks presents unique challenges due to their
dynamic and multi-hop nature. Accurate prediction of future network connectivity is essential …
dynamic and multi-hop nature. Accurate prediction of future network connectivity is essential …
Responsible Multilingual Large Language Models: A Survey of Development, Applications, and Societal Impact
Multilingual Large Language Models (MLLMs) represent a pivotal advancement in
democratizing artificial intelligence across linguistic boundaries. While theoretical …
democratizing artificial intelligence across linguistic boundaries. While theoretical …
BTREC: Bert-based trajectory recommendation for personalized tours
An essential task for tourists having a pleasant holiday is to have a well-planned itinerary
with relevant recommendations, especially when visiting unfamiliar cities. Many tour …
with relevant recommendations, especially when visiting unfamiliar cities. Many tour …
SkillRec: A data-driven approach to job skill recommendation for career insights
Understanding the skill sets and knowledge required for any career is of utmost importance,
but it is increasingly challenging in today's dynamic world with rapid changes in terms of the …
but it is increasingly challenging in today's dynamic world with rapid changes in terms of the …
[HTML][HTML] Fine-Grained Arabic Post (Tweet) Geolocation Prediction Using Deep Learning Techniques
Leveraging Twitter data for crisis management necessitates the accurate, fine-grained
geolocation of tweets, which unfortunately is often lacking, with only 1–3% of tweets being …
geolocation of tweets, which unfortunately is often lacking, with only 1–3% of tweets being …
Predicting the geolocation of tweets using transformer models on customized data
This research is aimed to solve the tweet/user geolocation prediction task and provide a
flexible methodology for the geotagging of textual big data. The suggested approach …
flexible methodology for the geotagging of textual big data. The suggested approach …
Leveraging Contrastive Learning for Few-shot Geolocation of Social Posts
Social geolocation is an important problem of predicting the originating locations of social
media posts. However, this task is challenging due to the need for a substantial volume of …
media posts. However, this task is challenging due to the need for a substantial volume of …
Sbtrec-a transformer framework for personalized tour recommendation problem with sentiment analysis
When traveling to an unfamiliar city for holidays, tourists often rely on guidebooks, travel
websites, or recommendation systems to plan their daily itineraries and explore popular …
websites, or recommendation systems to plan their daily itineraries and explore popular …
FewUser: Few-Shot Social User Geolocation via Contrastive Learning
To address the challenges of scarcity in geotagged data for social user geolocation, we
propose FewUser, a novel framework for Few-shot social User geolocation. We incorporate …
propose FewUser, a novel framework for Few-shot social User geolocation. We incorporate …
Similarity Guided Multimodal Fusion Transformer for Semantic Location Prediction in Social Media
Semantic location prediction aims to derive meaningful location insights from multimodal
social media posts, offering a more contextual understanding of daily activities than using …
social media posts, offering a more contextual understanding of daily activities than using …