Lately, there has been a surge in interest in utilizing Graph Convolutional Networks (GCNs) for the purpose of action recognition using skeletal data. In order to achieve optimal results, it is crucial to generate high-quality representations of the skeletal graph. Graph Convolutional Networks (GCNs) often employ the Message-Passing Mechanism (MPM) to acquire knowledge about various components of the skeleton by iteratively computing new features at each step. However, the interconnections between joints in the skeletal structure are intricate and extend beyond mere proximity. In order to address this issue, we propose the implementation of our Disassembled Hyper-Graph (DH-Graph), which draws inspiration from hyper-graph edges. The process of constructing the DH-network entails a few steps: partitioning the skeleton network into clusters of hyper-edges according to their semantic significance and relevance to action recognition, arranging these clusters in a hierarchical structure to enhance granularity, and establishing connections between joints within these clusters to discover hidden relationships. The DH-Graph employs a spatial domain GCN technique to construct the Pair-wise Hyper Hierarchical GCN (PH-GCN). In addition, we incorporate the HyperAttention module, which employs Multi-scale Representative Spatial Average Pooling and Edge Convolution techniques to emphasize significant sets of hyper-hierarchical information. Extensive experiments demonstrate that PH-GCN achieves remarkable performance on challenging NTU RGB+D and Northwestern UCLA datasets.

Traffic management in roads is one of the major challenges faced in developing efficient intelligent transportation systems. Recently, wireless networks have received significant attention for tackling this challenge. However, wireless technologies face the well-known spectrum scarcity problem due to the explosive demand for radio resources. To overcome this challenge, this study presents a novel intelligent traffic control system, utilizing the unused spectrum. Unlike existing works, the spectrum owners in this study hire free spectrum to drivers. The hired spectrum is deployed to build a short-range cost-effective wireless communication for monitoring traffic and enabling drivers to exchange warning messages, and thus enhancing road safety. Our objectives include minimizing crash probability, utilizing unused spectrum, and enabling spectrum owners to generate extra revenue. Numerical analysis demonstrates the capability of our approach to minimize the crash probability among vehicles under different operating road conditions.

In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP's profit under varying cloud environment conditions.

Wireless mesh networks (WMNs) have emerged recently to extend Internet access and other networking services. WMNs routers provide network access to the clients and other networking functions such as routing, and packet forwarding. Bandwidth scarcity is the main challenge that limits the performance of WMNs. Although considerable research has been conducted on spectrum allocation, spectrum management is still considered an important open problem. This problem can be solved using cognitive radio technology that allows radios to intelligently locate free frequencies and use them efficiently. In this work, we propose a new spectrum management scheme that supports local and global management for a wireless network. Our scheme is based on clusters where the coordinator for each cluster manages spectrum information by keeping the required information at cluster level and for the whole network. Our scheme provides robust operation against any cluster head failure, as well as clients mobility.