Zobrazeno 1 - 10
of 200
pro vyhledávání: '"NGAI, A. C."'
Speech enhancement is crucial in human-computer interaction, especially for ubiquitous devices. Ultrasound-based speech enhancement has emerged as an attractive choice because of its superior ubiquity and performance. However, inevitable interference
Externí odkaz:
http://arxiv.org/abs/2410.22076
The metaverse, emerging as a revolutionary platform for social and economic activities, provides various virtual services while posing security and privacy challenges. Wearable devices serve as bridges between the real world and the metaverse. To pro
Externí odkaz:
http://arxiv.org/abs/2410.21675
Autor:
Chen, Handi, Deng, Weipeng, Yang, Shuo, Xu, Jinfeng, Jiang, Zhihan, Ngai, Edith C. H., Liu, Jiangchuan, Liu, Xue
Edge Intelligence (EI) has been instrumental in delivering real-time, localized services by leveraging the computational capabilities of edge networks. The integration of Large Language Models (LLMs) empowers EI to evolve into the next stage: Edge Ge
Externí odkaz:
http://arxiv.org/abs/2410.18125
Group activities are important behaviors in human society, providing personalized recommendations for groups is referred to as the group recommendation task. Existing methods can usually be categorized into two strategies to infer group preferences:
Externí odkaz:
http://arxiv.org/abs/2409.02580
Autor:
Li, Shenghui, Ye, Fanghua, Fang, Meng, Zhao, Jiaxu, Chan, Yun-Hin, Ngai, Edith C. -H., Voigt, Thiemo
The recent development of Foundation Models (FMs), represented by large language models, vision transformers, and multimodal models, has been making a significant impact on both academia and industry. Compared with small-scale models, FMs have a much
Externí odkaz:
http://arxiv.org/abs/2406.12844
Graph Collaborative Filtering (GCF) has achieved state-of-the-art performance for recommendation tasks. However, most GCF structures simplify the feature transformation and nonlinear operation during message passing in the graph convolution network (
Externí odkaz:
http://arxiv.org/abs/2406.01034
With the increasing multimedia information, multimodal recommendation has received extensive attention. It utilizes multimodal information to alleviate the data sparsity problem in recommendation systems, thus improving recommendation accuracy. Howev
Externí odkaz:
http://arxiv.org/abs/2402.19407
Autor:
Zhang, Xinchen, Zhao, Running, Jiang, Zhihan, Sun, Zhicong, Ding, Yulong, Ngai, Edith C. H., Yang, Shuang-Hua
The rapid expansion of the Internet of Things (IoT) has raised increasing concern about targeted cyber attacks. Previous research primarily focused on static Intrusion Detection Systems (IDSs), which employ offline training to safeguard IoT systems.
Externí odkaz:
http://arxiv.org/abs/2402.01807
Federated learning (FL) inevitably confronts the challenge of system heterogeneity in practical scenarios. To enhance the capabilities of most model-homogeneous FL methods in handling system heterogeneity, we propose a training scheme that can extend
Externí odkaz:
http://arxiv.org/abs/2308.11464
Millimeter wave (mmWave) based speech recognition provides more possibility for audio-related applications, such as conference speech transcription and eavesdropping. However, considering the practicality in real scenarios, latency and recognizable v
Externí odkaz:
http://arxiv.org/abs/2308.08125