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SODA: Towards a Framework for Self Optimization via Demand Adaptation in Peer-to-Peer Networks.

, , and . Peer-to-Peer Computing, page 163-170. IEEE, (2009)

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SODA: Towards a Framework for Self Optimization via Demand Adaptation in Peer-to-Peer Networks., , and . Peer-to-Peer Computing, page 163-170. IEEE, (2009)A software defined approach to unified IPv6 transition, , , , , and . Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM, page 547--548. New York, NY, USA, ACM, (2013)EPI-CGAN: Robust EEG-based Person Identification Using Conditional Generative Adversarial Network., , , , and . IJCB, page 1-9. IEEE, (2022)A software defined approach to unified IPv6 transition., , , , , and . SIGCOMM, page 547-548. ACM, (2013)Deep Structure-Revealed Network for Texture Recognition., , , , and . CVPR, page 11007-11016. Computer Vision Foundation / IEEE, (2020)Mercury: Fast and Optimal Device Placement for Large Deep Learning Models., , , and . ICPP, page 412-422. ACM, (2023)MF-GAN: Multi-conditional Fusion Generative Adversarial Network for Text-to-Image Synthesis., , , , , , and . MMM (1), volume 13141 of Lecture Notes in Computer Science, page 41-53. Springer, (2022)Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning., , and . NeurIPS, page 6143-6154. (2019)Towards Secure Multi-Agent Deep Reinforcement Learning: Adversarial Attacks and Countermeasures., , , and . DSC, page 1-8. IEEE, (2022)Protecting access privacy of cached contents in information centric networks., , , , and . AsiaCCS, page 173-178. ACM, (2013)