Le Emoji sono Armi! Utilizzate per diffondere malware e comunicare con i C2
https://poliverso.org/display/0477a01e-a868f315-2e80384af874cdbf
Le Emoji sono Armi! Utilizzate per diffondere malware e comunicare con i C2 I ricercatori di Volexity hanno …
Interesting piece, as always, from @… on future scenarios for the technical infrastructure of scholarly communication. I'm contemplating, as always, what parts of this visions comport with HSS publishing (quite a bit), and what parts don't (quite a bit).
#ScholarlyPublishing
HPE and Nvidia unveil private cloud offerings for generative AI workloads coming this fall, including HPE Private Cloud AI, a co-developed "turnkey" option (Larry Dignan/Constellation Research)
https://www.constellationr.com…
Reconfigurable Intelligent Surface Equipped UAV in Emergency Wireless Communications: A New Fading-Shadowing Model and Performance Analysis
Yinong Chen, Wenchi Cheng, Wei Zhang
https://arxiv.org/abs/2406.11241
This https://arxiv.org/abs/2311.13994 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…
Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning
Kangwei Qi, Qiong Wu, Pingyi Fan, Nan Cheng, Qiang Fan, Jiangzhou Wang
https://arxiv.org/abs/2406.11318 https://arxiv.org/pdf/2406.11318
arXiv:2406.11318v1 Announce Type: new
Abstract: Vehicular edge computing (VEC) is an emerging technology that enables vehicles to perform high-intensity tasks by executing tasks locally or offloading them to nearby edge devices. However, obstacles such as buildings may degrade the communications and incur communication interruptions, and thus the vehicle may not meet the requirement for task offloading. Reconfigurable intelligent surfaces (RIS) is introduced to support vehicle communication and provide an alternative communication path. The system performance can be improved by flexibly adjusting the phase-shift of the RIS. For RIS-assisted VEC system where tasks arrive randomly, we design a control scheme that considers offloading power, local power allocation and phase-shift optimization. To solve this non-convex problem, we propose a new deep reinforcement learning (DRL) framework that employs modified multi-agent deep deterministic policy gradient (MADDPG) approach to optimize the power allocation for vehicle users (VUs) and block coordinate descent (BCD) algorithm to optimize the phase-shift of the RIS. Simulation results show that our proposed scheme outperforms the centralized deep deterministic policy gradient (DDPG) scheme and random scheme.
Consistent Update Synthesis via Privatized Beliefs
Thomas Schl\"ogl, Roman Kuznets, Giorgio Cignarale
https://arxiv.org/abs/2406.10010 https://…
Fast Fractional Programming for Multi-Cell Integrated Sensing and Communications
Yannan Chen, Yi Feng, Xiaoyang Li, Licheng Zhao, Kaiming Shen
https://arxiv.org/abs/2406.10910
This https://arxiv.org/abs/2402.14297 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…
This https://arxiv.org/abs/2112.13787 has been replaced.
link: https://scholar.google.com/scholar?q=a