Researchers have developed an innovative AI approach that embeds causal reasoning into offline reinforcement learning, enabling autonomous systems like driverless cars and medical support devices to make safer, more reliable decisions by accurately discerning true cause-and-effect relationships in historical data.

Researchers have developed a drone system that uses blockchain sharding to significantly enhance the speed, security, and real-time coordination of search and rescue missions.

Researchers have developed an innovative AI-driven system that automates and optimizes penetration testing, significantly reducing testing steps while expanding vulnerability assessments for large-scale networks.

Researchers at Shanghai Jiao Tong University have developed CHASER, a blockchain-based incentive system that transforms mobile crowdsensing by leveraging automated smart contracts and advanced encryption to deliver fair compensation, robust data security, and high efficiency, dramatically boosting user participation and reliability.

Researchers at Wuhan University have introduced Fair Adversarial Training (FairAT), a technique that enhances AI security and fairness by identifying and strengthening vulnerable data points, thereby reducing cyber-attack risks and mitigating ethical biases.

Researchers at Soochow University have unveiled DPEC, an advanced AI model that uses dual-view prompts and element correlation to significantly enhance spatial relation extraction from text, setting a new benchmark for technologies in autonomous driving, digital mapping, and smart personal assistants.