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Found 9 papers, 0 papers with code
Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology
The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments.
A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data.
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions.
A Survey on Explainable Artificial Intelligence for Cybersecurity
The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme
In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process.
Reward Shaping Using Convolutional Neural Network
Due to (1) the previous success of using message passing for reward shaping; and (2) the CNN planning behavior, we use these messages to train the CNN of VIN-RS.
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing
Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications.
Cloud computing as a platform for monetizing data services: A two-sided game business model
The players of the game are the big data service providers, cloud computing platform, and cloud users.
A two-level solution to fight against dishonest opinions in recommendation-based trust systems
In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels.
