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..

Adversarial Training Towards Robust Multimedia Recommender System

Abstract: With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advances on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR .

Dual band adaptive concurrent processing method and apparatus

Abstract: Embodiments of the present invention provide a dual band adaptive concurrent processing method and apparatus. In the embodiments of the present invention, by using statistical information of a service at a first frequency band and a service at a service at a second frequency band in the Nth adjustment period, service performance of the services at the frequency bands in the adjustment period may be acquired; and based on the statistical information, and a QoS requirement that the services at the two frequency bands need to meet, a second coefficient of proportionality between timeslots occupied by the services at the two frequency bands in the (N+1)th adjustment period may be determined, so that switching control on a first channel and a second channel may be performed in the (N+1)th adjustment period according to the determined second coefficient of proportionality.

Presence service system and method for publishing presence information

Abstract: A method for publishing presence information enables the presence information to be preset and published at a preset time automatically. The method includes the steps of: setting the presence information to be published, and setting the publishing time corresponding to each piece of the presence information to be published; when determining the publishing time arrives, the presence information corresponding to the publishing time is published

Efficient Residual Dense Block Search for Image Super-Resolution

Abstract: Although remarkable progress has been made on single image super-resolution due to the revival of deep convolutional neural networks, deep learning methods are confronted with the challenges of computation and memory consumption in practice, especially for mobile devices. Focusing on this issue, we propose an efficient residual dense block search algorithm with multiple objectives to hunt for fast, lightweight and accurate networks for image super-resolution. Firstly, to accelerate super-resolution network, we exploit the variation of feature scale adequately with the proposed efficient residual dense blocks. In the proposed evolutionary algorithm, the locations of pooling and upsampling operator are searched automatically. Secondly, network architecture is evolved with the guidance of block credits to acquire accurate super-resolution network. The block credit reflects the effect of current block and is earned during model evaluation process. It guides the evolution by weighing the sampling probability of mutation to favor admirable blocks. Extensive experimental results demonstrate the effectiveness of the proposed searching method and the found efficient super-resolution models achieve better performance than the state-of-the-art methods with limited number of parameters and FLOPs.

On functionality separation for green mobile networks: concept study over LTE

Abstract: Traditional wireless networks are designed for ubiquitous network access provision with low-rate voice services, which thus preserve the homogeneous architecture and tight coupling for infrastructures such as base stations. With the traffic explosion and the paradigm shift from voice-oriented services to data-oriented services, traditional homogeneous architecture no longer maintains its optimality, and heterogeneous deployment with flexible network control capability becomes a promising evolution direction. To achieve this goal, in this article, we propose a two-layer network functionality separation scheme, targeting at low control signaling overhead and flexible network reconfiguration for future mobile networks. The proposed scheme is shown to support all kinds of user activities defined in current networks. Moreover, we give two examples to illustrate how the proposed scheme can be applied to multicarrier networks and suggest two important design principles for future green networks. Numerical results show that the proposed scheme achieves significant energy reduction over traditional LTE networks, and can be recommended as a candidate solution for future green mobile networks.