Zobrazeno 1 - 10
of 521
pro vyhledávání: '"Kang, Jiawen"'
Autor:
Liu, Jian, Xiao, Ming, Wen, Jinbo, Kang, Jiawen, Zhang, Ruichen, Zhang, Tao, Niyato, Dusit, Zhang, Weiting, Liu, Ying
Mobile Artificial Intelligence-Generated Content (AIGC) networks enable massive users to obtain customized content generation services. However, users still need to download a large number of AIGC outputs from mobile AIGC service providers, which str
Externí odkaz:
http://arxiv.org/abs/2409.17506
Autor:
He, Jiayi, Luo, Xiaofeng, Kang, Jiawen, Du, Hongyang, Xiong, Zehui, Chen, Ci, Niyato, Dusit, Shen, Xuemin
Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Deep Learning (DL)-based semantic codecs further enhance this efficiency. However, the vulnerability of DL models to s
Externí odkaz:
http://arxiv.org/abs/2409.15695
Autor:
Kang, Jiawen, Han, Dongrui, Meng, Lingwei, Zhou, Jingyan, Li, Jinchao, Wu, Xixin, Meng, Helen
Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models to distinguish between individuals with AD and those without. Unlike conventional classification tasks, we identify within
Externí odkaz:
http://arxiv.org/abs/2409.16322
Autor:
Kang, Jiawen, Meng, Lingwei, Cui, Mingyu, Wang, Yuejiao, Wu, Xixin, Liu, Xunying, Meng, Helen
Multi-talker speech recognition (MTASR) faces unique challenges in disentangling and transcribing overlapping speech. To address these challenges, this paper investigates the role of Connectionist Temporal Classification (CTC) in speaker disentanglem
Externí odkaz:
http://arxiv.org/abs/2409.12388
Autor:
Liu, Yinqiu, Du, Hongyang, Niyato, Dusit, Kang, Jiawen, Xiong, Zehui, Wen, Yonggang, Kim, Dong In
Generative AI (GenAI), exemplified by Large Language Models (LLMs) such as OpenAI's ChatGPT, is revolutionizing various fields. Central to this transformation is Data Center Networking (DCN), which not only provides the computational power necessary
Externí odkaz:
http://arxiv.org/abs/2409.09343
Autor:
Cui, Mingyu, Yang, Yifan, Deng, Jiajun, Kang, Jiawen, Hu, Shujie, Wang, Tianzi, Li, Zhaoqing, Zhang, Shiliang, Chen, Xie, Liu, Xunying
Self-supervised learning (SSL) based discrete speech representations are highly compact and domain adaptable. In this paper, SSL discrete speech features extracted from WavLM models are used as additional cross-utterance acoustic context features in
Externí odkaz:
http://arxiv.org/abs/2409.08797
Autor:
Meng, Lingwei, Hu, Shujie, Kang, Jiawen, Li, Zhaoqing, Wang, Yuejiao, Wu, Wenxuan, Wu, Xixin, Liu, Xunying, Meng, Helen
Recent advancements in large language models (LLMs) have revolutionized various domains, bringing significant progress and new opportunities. Despite progress in speech-related tasks, LLMs have not been sufficiently explored in multi-talker scenarios
Externí odkaz:
http://arxiv.org/abs/2409.08596
Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout the product life cycle, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to i
Externí odkaz:
http://arxiv.org/abs/2408.01173
Autor:
Meng, Lingwei, Kang, Jiawen, Wang, Yuejiao, Jin, Zengrui, Wu, Xixin, Liu, Xunying, Meng, Helen
Multi-talker speech recognition and target-talker speech recognition, both involve transcription in multi-talker contexts, remain significant challenges. However, existing methods rarely attempt to simultaneously address both tasks. In this study, we
Externí odkaz:
http://arxiv.org/abs/2407.09817
Despite progress in semantic communication (SemCom), research on SemCom security is still in its infancy. To bridge this gap, we propose a general covert SemCom framework for wireless networks, reducing eavesdropping risk. Our approach transmits sema
Externí odkaz:
http://arxiv.org/abs/2407.07475