Huawei

About: Huawei is a company organization based out in Shenzhen, China. It is known for research contribution in the topics: Terminal (electronics) & Node (networking). 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..

Batch Mode Active Sampling Based on Marginal Probability Distribution Matching

Abstract: Active Learning is a machine learning and data mining technique that selects the most informative samples for labeling and uses them as training data; it is especially useful when there are large amount of unlabeled data and labeling them is expensive. Recently, batch-mode active learning, where a set of samples are selected concurrently for labeling, based on their collective merit, has attracted a lot of attention. The objective of batch-mode active learning is to select a set of informative samples so that a classifier learned on these samples has good generalization performance on the unlabeled data. Most of the existing batch-mode active learning methodologies try to achieve this by selecting samples based on certain criteria. In this article we propose a novel criterion which achieves good generalization performance of a classifier by specifically selecting a set of query samples that minimize the difference in distribution between the labeled and the unlabeled data, after annotation. We explicitly measure this difference based on all candidate subsets of the unlabeled data and select the best subset. The proposed objective is an NP-hard integer programming optimization problem. We provide two optimization techniques to solve this problem. In the first one, the problem is transformed into a convex quadratic programming problem and in the second method the problem is transformed into a linear programming problem. Our empirical studies using publicly available UCI datasets and two biomedical image databases demonstrate the effectiveness of the proposed approach in comparison with the state-of-the-art batch-mode active learning methods. We also present two extensions of the proposed approach, which incorporate uncertainty of the predicted labels of the unlabeled data and transfer learning in the proposed formulation. In addition, we present a joint optimization framework for performing both transfer and active learning simultaneously unlike the existing approaches of learning in two separate stages, that is, typically, transfer learning followed by active learning. We specifically minimize a common objective of reducing distribution difference between the domain adapted source, the queried and labeled samples and the rest of the unlabeled target domain data. Our empirical studies on two biomedical image databases and on a publicly available 20 Newsgroups dataset show that incorporation of uncertainty information and transfer learning further improves the performance of the proposed active learning based classifier. Our empirical studies also show that the proposed transfer-active method based on the joint optimization framework performs significantly better than a framework which implements transfer and active learning in two separate stages.

Shortest path bridging: Efficient control of larger ethernet networks

Abstract: This article provides an overview of IEEE 802.1aq shortest path bridging and outlines some application scenarios that will benefit from the new capabilities SPB offers. SPB is built on the IEEE 802.1 standards, and inherits unaltered the existing OAM and data plane scalability enhancements, such as the MAC-in-MAC forwarding paradigm. SPB introduces link state control for bridge networks, thus improving control plane scalability, network bandwidth utilization, and control of the forwarding paths. Furthermore, SPB minimizes latency by forwarding frames on the shortest path. Network-wide load balancing is also supported by spreading the traffic on multiple equal cost paths in a user controllable manner. Thus, SPB provides enhanced control for Ethernet networks in metro, RAN backhaul, or data center environments.

Min Flow Rate Maximization for Software Defined Radio Access Networks

Abstract: We consider a cloud-based heterogeneous network of base stations (BSs) connected via a backhaul network of routers and wired/wireless links with limited capacity. The optimal provision of such networks requires proper resource allocation across the radio access links in conjunction with appropriate traffic engineering within the backhaul network. In this paper, we propose an efficient algorithm for joint resource allocation across the wireless links and flow control over the entire network. The proposed algorithm, which maximizes the min-rate among all the transmitted commodities, is based on a decomposition approach that leverages both the alternating direction method of multipliers (ADMM) and the weighted-MMSE (WMMSE) algorithm. We show that this algorithm is easily parallelizable and converges globally to a stationary solution of the joint optimization problem. The proposed algorithm can also be extended to networks with multi-antenna nodes and other utility functions.

Improving Ad Click Prediction by Considering Non-displayed Events

Abstract: Click-through rate (CTR) prediction is the core problem of building advertising systems. Most existing state-of-the-art approaches model CTR prediction as binary classification problems, where displayed events with and without click feedbacks are respectively considered as positive and negative instances for training and offline validation. However, due to the selection mechanism applied in most advertising systems, a selection bias exists between distributions of displayed and non-displayed events. Conventional CTR models ignoring the bias may have inaccurate predictions and cause a loss of the revenue. To alleviate the bias, we need to conduct counterfactual learning by considering not only displayed events but also non-displayed events. In this paper, through a review of existing approaches of counterfactual learning, we point out some difficulties for applying these approaches for CTR prediction in a real-world advertising system. To overcome these difficulties, we propose a novel framework for counterfactual CTR prediction. In experiments, we compare our proposed framework against state-of-the-art conventional CTR models and existing counterfactual learning approaches. Experimental results show significant improvements.

Method for updating voiceprint feature model and terminal

Abstract: A method for updating a voiceprint feature model and a terminal. The method comprises: obtaining an original audio stream comprising at least one speaker (S101); obtaining the respective audio stream of each speaker in the at least one speaker in the original audio stream according to a preset speaker segmentation and clustering algorithm (S102); matching the respective audio stream of each speaker in the at least one speaker with an original voiceprint feature model, so as to obtain the successfully-matched audio stream (S103); and using the successfully-matched audio stream as an additional audio stream training sample used for generating the original voiceprint feature model, and updating the original voiceprint feature model (S104). According to the present invention, the valid audio stream during a conversation process is adaptively extracted and used as the additional audio stream training sample, and the additional audio stream training sample is used for dynamically correcting the original voiceprint feature model, thus achieving a purpose of improving the precision of the voiceprint feature model and the accuracy of recognition under the premise of high practicability.