Huawei

About: Huawei is a company organization based out in Shenzhen, China. It is known for research contribution in the topics: Terminal (electronics) & Signal. The organization has 41417 authors who have published 44698 publications receiving 343496 citations. The organization is also known as: Huawei Technologies & Huawei Technologies Co., Ltd..

Systems and Methods for SDT to Interwork with NFV and SDN

Abstract: System and method embodiments are provided for enabling software defined topology (SDT) to interwork with network function virtualization (NFV) and software defined networking (SDN). In an embodiment, a method for software defined topology (SDT) management interworking with network function virtualization (NFV) and software defined networking (SDN) includes receiving, at an SDT Manager, from a service customer, a service request comprising a virtual service function forwarding graph (VSF FG); selecting a virtual network function (VNF) forwarding graph (FG) template in accordance with the received service request; generating a virtual function (VF) FG in accordance with the service request and the selected VNF FG template; selecting a point of presence (PoPs) for a VF in the VF FG; and transmitting, to an NFV Manager, instructions to instantiate the VF in accordance with at least one of the generated VF FG and the determined PoP.

AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

Abstract: Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manually designed or simply enumerated. However, enumerating all feature interactions brings large memory and computation cost. Even worse, useless interactions may introduce noise and complicate the training process. In this work, we propose a two-stage algorithm called Automatic Feature Interaction Selection (AutoFIS). AutoFIS can automatically identify important feature interactions for factorization models with computational cost just equivalent to training the target model to convergence. In the \emph{search stage}, instead of searching over a discrete set of candidate feature interactions, we relax the choices to be continuous by introducing the architecture parameters. By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model. In the \emph{re-train stage}, we keep the architecture parameters serving as an attention unit to further boost the performance. Offline experiments on three large-scale datasets (two public benchmarks, one private) demonstrate that AutoFIS can significantly improve various FM based models. AutoFIS has been deployed onto the training platform of Huawei App Store recommendation service, where a 10-day online A/B test demonstrated that AutoFIS improved the DeepFM model by 20.3\% and 20.1\% in terms of CTR and CVR respectively.

Charging Method, System and Server for PoC Service

Abstract: A charging method, a system and a server for Poc service are disclosed. The Poc server transmits updating CCR message to OCS when the number of the clients participated in conversation changes, the message carries the information of the number of the clients participated in the conversation. The present invention could provide the charging scheme based on the number of the clients participated in the conversation to the user, therefore the diversiform charging requirements of operators are fulfilled and the satisfaction degree of users is increased.

Cross-domain Detection via Graph-induced Prototype Alignment

Abstract: Applying the knowledge of an object detector trained on a specific domain directly onto a new domain is risky, as the gap between two domains can severely degrade model's performance. Furthermore, since different instances commonly embody distinct modal information in object detection scenario, the feature alignment of source and target domain is hard to be realized. To mitigate these problems, we propose a Graph-induced Prototype Alignment (GPA) framework to seek for category-level domain alignment via elaborate prototype representations. In the nutshell, more precise instance-level features are obtained through graph-based information propagation among region proposals, and, on such basis, the prototype representation of each class is derived for category-level domain alignment. In addition, in order to alleviate the negative effect of class-imbalance on domain adaptation, we design a Class-reweighted Contrastive Loss to harmonize the adaptation training process. Combining with Faster R-CNN, the proposed framework conducts feature alignment in a two-stage manner. Comprehensive results on various cross-domain detection tasks demonstrate that our approach outperforms existing methods with a remarkable margin. Our code is available at this https URL.

Content Based Traffic Engineering in Software Defined Information Centric Networks

Abstract: This paper describes a content centric network architecture which uses software defined networking principles to implement efficient metadata driven services by extracting content metadata at the network layer. The ability to access content metadata transparently enables a number of new services in the network. Specific examples discussed here include: a metadata driven traffic engineering scheme which uses prior knowledge of content length to optimize content delivery, a metadata driven content firewall which is more resilient than traditional firewalls and differentiated treatment of content based on the type of content being accessed. A detailed outline of an implementation of the proposed architecture is presented along with some basic evaluation.