Zobrazeno 1 - 10
of 128
pro vyhledávání: '"Xin, ChunSheng"'
Autor:
Fathalla, Efat Samir, Zargarzadeh, Sahar, Xin, Chunsheng, Wu, Hongyi, Jiang, Peng, Santos, Joao F., Kibilda, Jacek, da, Aloizio Pereira
This paper presents an experimental study on mmWave beam profiling on a mmWave testbed, and develops a machine learning model for beamforming based on the experiment data. The datasets we have obtained from the beam profiling and the machine learning
Externí odkaz:
http://arxiv.org/abs/2408.13403
The prevalent use of Transformer-like models, exemplified by ChatGPT in modern language processing applications, underscores the critical need for enabling private inference essential for many cloud-based services reliant on such models. However, cur
Externí odkaz:
http://arxiv.org/abs/2405.17485
While it is encouraging to witness the recent development in privacy-preserving Machine Learning as a Service (MLaaS), there still exists a significant performance gap for its deployment in real-world applications. We observe the state-of-the-art fra
Externí odkaz:
http://arxiv.org/abs/2209.01637
Publikováno v:
In Pervasive and Mobile Computing May 2024 100
As the number of IoT devices has increased rapidly, IoT botnets have exploited the vulnerabilities of IoT devices. However, it is still challenging to detect the initial intrusion on IoT devices prior to massive attacks. Recent studies have utilized
Externí odkaz:
http://arxiv.org/abs/2106.12753
Machine Learning as a Service (MLaaS) is enabling a wide range of smart applications on end devices. However, privacy-preserved computation is still expensive. Our investigation has found that the most time-consuming component of the HE-based linear
Externí odkaz:
http://arxiv.org/abs/2105.01827
Machine Learning as a Service (MLaaS) is enabling a wide range of smart applications on end devices. However, such convenience comes with a cost of privacy because users have to upload their private data to the cloud. This research aims to provide ef
Externí odkaz:
http://arxiv.org/abs/1911.05184
Publikováno v:
In Digital Communications and Networks October 2022 8(5):791-803
Publikováno v:
In High-Confidence Computing September 2022 2(3)