Data-driven AI systems increasingly influence our choices, raising concerns about autonomy, fairness, and accountability. Achieving algorithmic autonomy requires new infrastructures, motivation ...

Abstract: Federated Learning (FL) has emerged as a transformative approach for training machine learning models across decentralized data sources while preserving privacy. This study evaluates the ...

Abstract: This article concentrates on distributed optimization over networks with communication delays. Each subsystem in the network performs its local updates by using the information received from ...

Abstract: The need for data protection in national critical information infrastructure units has become more and more urgent with the deepening of digital transformation. At present, the rapid ...

Abstract: Fault diagnosis of railway assets has drawn the interest of both the scholarly and engineering communities. Federated learning (FL) enables training models across distributed assets to ...

Abstract: With the rapid development of the smart grid relying on communication and computer science technology, its vulnerabilities to intentional cyber-attacks are also exposed to cyber-attackers.

Existing algorithms can partially reconstruct the shape of a single tree from a clean point-cloud dataset acquired by laser-scanning technologies. Doing the same with forest data has proven far more ...

Abstract: 17-point algorithm is a popular method in relative pose estimation of multi-cameras. However, the role of overlap in 17-point algorithm remains unexplored. And the relaxed way in solving ...

Jan 10 (Reuters) - Elon Musk said on Saturday that social media platform X will open to the public its new algorithm, including all code for organic and advertising post recommendations, in seven days ...

Abstract: The demand for 3D scanning of workpiece geometries in automated assembly within workshops is increasingly critical, playing a vital role in the process. Point cloud registration, as an ...