Zobrazeno 1 - 10
of 263
pro vyhledávání: '"Guo, Jianxiong"'
Meta computing is a new computing paradigm that aims to efficiently utilize all network computing resources to provide fault-tolerant, personalized services with strong security and privacy guarantees. It also seeks to virtualize the Internet as many
Externí odkaz:
http://arxiv.org/abs/2406.13404
Autor:
Guo, Jianxiong
In this survey, we offer an extensive overview of the Online Influence Maximization (IM) problem by covering both theoretical aspects and practical applications. For the integrity of the article and because the online algorithm takes an offline oracl
Externí odkaz:
http://arxiv.org/abs/2312.00099
Influence maximization aims to find a subset of seeds that maximize the influence spread under a given budget. In this paper, we mainly address the data-driven version of this problem, where the diffusion model is not given but needs to be inferred f
Externí odkaz:
http://arxiv.org/abs/2311.11080
Publikováno v:
IEEE Transactions on Mobile Computing, 2024
The multi-armed bandit (MAB) models have attracted significant research attention due to their applicability and effectiveness in various real-world scenarios such as resource allocation, online advertising, and dynamic pricing. As an important branc
Externí odkaz:
http://arxiv.org/abs/2310.11188
In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by exist
Externí odkaz:
http://arxiv.org/abs/2310.00560
Publikováno v:
IEEE Transactions on Mobile Computing, 2024
Terahertz communication networks and intelligent reflecting surfaces exhibit significant potential in advancing wireless networks, particularly within the domain of aerial-based multi-access edge computing systems. These technologies enable efficient
Externí odkaz:
http://arxiv.org/abs/2307.08474
Meta Computing is a new computing paradigm, which aims to solve the problem of computing islands in current edge computing paradigms and integrate all the resources on a network by incorporating cloud, edge, and particularly terminal-end devices. It
Externí odkaz:
http://arxiv.org/abs/2304.13463
Publikováno v:
IEEE Transactions on Mobile Computing, 2023
Mobile Crowdsourcing (MCS) is a novel distributed computing paradigm that recruits skilled workers to perform location-dependent tasks. A number of mature incentive mechanisms have been proposed to address the worker recruitment problem in MCS system
Externí odkaz:
http://arxiv.org/abs/2303.12460
Recently, machine learning methods have shown the prospects of stock trend forecasting. However, the volatile and dynamic nature of the stock market makes it difficult to directly apply machine learning techniques. Previous methods usually use the te
Externí odkaz:
http://arxiv.org/abs/2212.08656
Publikováno v:
IEEE Transactions on Artificial Intelligence, 2023
Federated Learning (FL) is a decentralized learning method used to train machine learning algorithms. In FL, a global model iteratively collects the parameters of local models without accessing their local data. However, a significant challenge in FL
Externí odkaz:
http://arxiv.org/abs/2211.07248