Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Phan, Khoa"'
Autor:
Zheng, Yu, Koh, Huan Yee, Jin, Ming, Chi, Lianhua, Wang, Haishuai, Phan, Khoa T., Chen, Yi-Ping Phoebe, Pan, Shirui, Xiang, Wei
The detection of anomalies in multivariate time series data is crucial for various practical applications, including smart power grids, traffic flow forecasting, and industrial process control. However, real-world time series data is usually not well
Externí odkaz:
http://arxiv.org/abs/2401.05800
Abuse in its various forms, including physical, psychological, verbal, sexual, financial, and cultural, has a negative impact on mental health. However, there are limited studies on applying natural language processing (NLP) in this field in Vietnam.
Externí odkaz:
http://arxiv.org/abs/2312.07831
Split learning (SL) aims to protect user data privacy by distributing deep models between client-server and keeping private data locally. Only processed or `smashed' data can be transmitted from the clients to the server during the SL process. Howeve
Externí odkaz:
http://arxiv.org/abs/2310.14434
Autor:
Chu, Nam H., Van Huynh, Nguyen, Nguyen, Diep N., Hoang, Dinh Thai, Gong, Shimin, Shu, Tao, Dutkiewicz, Eryk, Phan, Khoa T.
This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts: (i) the active-transmit message transmitted by the tr
Externí odkaz:
http://arxiv.org/abs/2308.02242
Autor:
Zheng, Yu, Koh, Huan Yee, Jin, Ming, Chi, Lianhua, Phan, Khoa T., Pan, Shirui, Chen, Yi-Ping Phoebe, Xiang, Wei
Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cann
Externí odkaz:
http://arxiv.org/abs/2307.08390
Autor:
Chu, Nam H., Nguyen, Diep N., Hoang, Dinh Thai, Phan, Khoa T., Dutkiewicz, Eryk, Niyato, Dusit, Shu, Tao
This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Speci
Externí odkaz:
http://arxiv.org/abs/2302.13445
Autor:
Chu, Nam H., Hoang, Dinh Thai, Nguyen, Diep N., Phan, Khoa T., Dutkiewicz, Eryk, Niyato, Dusit, Shu, Tao
Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking reso
Externí odkaz:
http://arxiv.org/abs/2205.11087
Autor:
Chu, Nam H., Nguyen, Diep N., Hoang, Dinh Thai, Pham, Quoc-Viet, Phan, Khoa T., Hwang, Won-Joo, Dutkiewicz, Eryk
Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (AVs), optimizing the waveform structure is one of the most challenging
Externí odkaz:
http://arxiv.org/abs/2202.11508
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detection typically only consider the information in a single scale (view), th
Externí odkaz:
http://arxiv.org/abs/2202.05525
By executing offloaded tasks from mobile users, edge computing augments mobile user equipments (UEs) with computing/communications resources from edge nodes (ENs), enabling new services (e.g., real-time gaming). However, despite being more resourcefu
Externí odkaz:
http://arxiv.org/abs/2201.00910