Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Ma, Meiyi"'
Federated Learning (FL) offers a promising solution to the privacy concerns associated with centralized Machine Learning (ML) by enabling decentralized, collaborative learning. However, FL is vulnerable to various security threats, including poisonin
Externí odkaz:
http://arxiv.org/abs/2411.03231
Autor:
Cohn, Clayton, Davalos, Eduardo, Vatral, Caleb, Fonteles, Joyce Horn, Wang, Hanchen David, Ma, Meiyi, Biswas, Gautam
Recent technological advancements have enhanced our ability to collect and analyze rich multimodal data (e.g., speech, video, and eye gaze) to better inform learning and training experiences. While previous reviews have focused on parts of the multim
Externí odkaz:
http://arxiv.org/abs/2408.14491
Autor:
Wang, Hanchen David, Khan, Nibraas, Chen, Anna, Sarkar, Nilanjan, Wisniewski, Pamela, Ma, Meiyi
Recent global estimates suggest that as many as 2.41 billion individuals have health conditions that would benefit from rehabilitation services. Home-based Physical Therapy (PT) faces significant challenges in providing interactive feedback and meani
Externí odkaz:
http://arxiv.org/abs/2408.11837
Monte Carlo tree search (MCTS) is one of the most capable online search algorithms for sequential planning tasks, with significant applications in areas such as resource allocation and transit planning. Despite its strong performance in real-world de
Externí odkaz:
http://arxiv.org/abs/2407.10820
Recent advancements in federated learning (FL) have greatly facilitated the development of decentralized collaborative applications, particularly in the domain of Artificial Intelligence of Things (AIoT). However, a critical aspect missing from the c
Externí odkaz:
http://arxiv.org/abs/2401.07448
Emergency and non-emergency response systems are essential services provided by local governments and critical to protecting lives, the environment, and property. The effective handling of (non-)emergency calls is critical for public safety and well-
Externí odkaz:
http://arxiv.org/abs/2312.14185
Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) are widely used in automated verification, but there is a lack of interactive tools designed for educational purposes in this field. To address this gap, we present EduSAT, a pedag
Externí odkaz:
http://arxiv.org/abs/2308.07890
Autor:
Wang, Jiangwei, Yang, Shuo, An, Ziyan, Han, Songyang, Zhang, Zhili, Mangharam, Rahul, Ma, Meiyi, Miao, Fei
Reward design is a key component of deep reinforcement learning, yet some tasks and designer's objectives may be unnatural to define as a scalar cost function. Among the various techniques, formal methods integrated with DRL have garnered considerabl
Externí odkaz:
http://arxiv.org/abs/2306.06808
Smart cities operate on computational predictive frameworks that collect, aggregate, and utilize data from large-scale sensor networks. However, these frameworks are prone to multiple sources of data and algorithmic bias, which often lead to unfair p
Externí odkaz:
http://arxiv.org/abs/2302.11137
An increasing number of monitoring systems have been developed in smart cities to ensure that the real-time operations of a city satisfy safety and performance requirements. However, many existing city requirements are written in English with missing
Externí odkaz:
http://arxiv.org/abs/2302.09665