Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …

This paper is dedicated to the trajectory tracking control of under-actuated unmanned
surface vessel (USV) with state and input quantization. In terms of kinematics, a distributed …

Drones are increasingly adopted for policing in many countries, as they can aid police
officers to detect hazards and respond to incidents with timely and low-cost services …

This study presents a case focused on sustainable farming practices, specifically the
cultivation of tilapia (Mozambican and aureus species) in ponds with geothermal water. This …

Traffic accidents pose a significant risk to human health and property safety. Therefore, to
prevent traffic accidents, predicting their risks has garnered growing interest. We argue that …

Crime forecasting is a critical component of urban analysis and essential for stabilizing
society today. Unlike other time series forecasting problems, crime incidents are sparse …

This study proposes a novel application-oriented variable speed limit (VSL) control strategy
based on a multi-objective deep reinforcement learning (MDRL) framework for freeway …

In the contemporary era of the knowledge economy, knowledge has assumed a paramount
role in production and daily life. Knowledge-sharing technologies rooted in deep learning …

Traffic Anomaly Prediction (TAP) has emerged as a critical concern in the field of Intelligent
Transportation Systems (ITS), attracting attention from numerous scholars. Accurately …

Large models have demonstrated significant effectiveness across various domains, yet they
are not inherently optimized for automated driving safety, necessitating the fine-tuning of …