Technology trends such as Software-Defined Networking (SDN) are transforming networking services in terms of flexibility and faster deployment times. SDN separates the control plane from the data plane with its centra...Technology trends such as Software-Defined Networking (SDN) are transforming networking services in terms of flexibility and faster deployment times. SDN separates the control plane from the data plane with its centralised architecture compared with the distributed approach used in other management systems. However, management systems are still required to adapt the new emerging SDN-like technologies to address various security and complex management issues. Simple Network Management Protocol (SNMP) is the most widespread management protocol implemented in a traditional Network Management System (NMS) but has some limitations with the development of SDN-like services. Hence, many studies have been undertaken to merge the SDN-like services with traditional network management systems. Results show that merging SDN with traditional NMS systems not only increases the average Management Information Base (MIB) polling time but also creates additional overheads on the network. Therefore, this paper proposes a dynamic scheme for MIB polling using an additional MIB controller agent within the SDN controller. Our results show that using the proposed scheme, the average polling time can be significantly reduced (i.e., faster polling of the MIB information) and also requires very low overhead because of the small sized OpenFlow messages used during polling.展开更多
It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke fashion.Despite advances in stroke-based image rendering and deep learning-based image rendering,existing painting methods h...It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke fashion.Despite advances in stroke-based image rendering and deep learning-based image rendering,existing painting methods have limitations:they(i)lack flexibility to choose different art-style strokes,(ii)lose content details of images,and(iii)generate few artistic styles for paintings.In this paper,we propose a stroke-style generative adversarial network,called Stroke-GAN,to solve the first two limitations.Stroke-GAN learns styles of strokes from different stroke-style datasets,so can produce diverse stroke styles.We design three players in Stroke-GAN to generate pure-color strokes close to human artists’strokes,thereby improving the quality of painted details.To overcome the third limitation,we have devised a neural network named Stroke-GAN Painter,based on Stroke-GAN;it can generate different artistic styles of paintings.Experiments demonstrate that our artful painter can generate various styles of paintings while well-preserving content details(such as details of human faces and building textures)and retaining high fidelity to the input images.展开更多
文摘Technology trends such as Software-Defined Networking (SDN) are transforming networking services in terms of flexibility and faster deployment times. SDN separates the control plane from the data plane with its centralised architecture compared with the distributed approach used in other management systems. However, management systems are still required to adapt the new emerging SDN-like technologies to address various security and complex management issues. Simple Network Management Protocol (SNMP) is the most widespread management protocol implemented in a traditional Network Management System (NMS) but has some limitations with the development of SDN-like services. Hence, many studies have been undertaken to merge the SDN-like services with traditional network management systems. Results show that merging SDN with traditional NMS systems not only increases the average Management Information Base (MIB) polling time but also creates additional overheads on the network. Therefore, this paper proposes a dynamic scheme for MIB polling using an additional MIB controller agent within the SDN controller. Our results show that using the proposed scheme, the average polling time can be significantly reduced (i.e., faster polling of the MIB information) and also requires very low overhead because of the small sized OpenFlow messages used during polling.
基金This work was supported in part by the Hong Kong Institute of Business Studies(HKIBS)Research Seed Fund under Grant HKIBS RSF-212-004in part by The Hong Kong Polytechnic University under Grant P0030419,Grant P0030929,and Grant P0035358.
文摘It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke fashion.Despite advances in stroke-based image rendering and deep learning-based image rendering,existing painting methods have limitations:they(i)lack flexibility to choose different art-style strokes,(ii)lose content details of images,and(iii)generate few artistic styles for paintings.In this paper,we propose a stroke-style generative adversarial network,called Stroke-GAN,to solve the first two limitations.Stroke-GAN learns styles of strokes from different stroke-style datasets,so can produce diverse stroke styles.We design three players in Stroke-GAN to generate pure-color strokes close to human artists’strokes,thereby improving the quality of painted details.To overcome the third limitation,we have devised a neural network named Stroke-GAN Painter,based on Stroke-GAN;it can generate different artistic styles of paintings.Experiments demonstrate that our artful painter can generate various styles of paintings while well-preserving content details(such as details of human faces and building textures)and retaining high fidelity to the input images.
