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..

Method and ethernet switching device for detecting loop position in ethernet

Abstract: A method and an Ethernet switching device for detecting loop position in an Ethernet, which relate to the communications field, are provided in the embodiments of the present invention, to solve the problem that the loop appearance position can not be located fast during the loop position detection. The method includes: the Ethernet switching device learns a Media Access Control (MAC) address; wherein, a first MAC address corresponds to a first port in a MAC table before learning the MAC address, and the first MAC address corresponds to a second port after learning the MAC address (S301); if the second port and the first port do not belong to the same sub-network, the difference between the time of learning on the second port and the time of latterly receiving or transmitting a message according to the first port is calculated (S302); if the difference is less than a preset judgment threshold of host migration, it is determined that the loop appears in the sub-network connected with the second port (S303). The embodiments of the present invention are applied to the loop position detection and location.

Universal Perturbation Attack Against Image Retrieval

Abstract: Universal adversarial perturbations (UAPs), a.k.a. input-agnostic perturbations, has been proved to exist and be able to fool cutting-edge deep learning models on most of the data samples. Existing UAP methods mainly focus on attacking image classification models. Nevertheless, little attention has been paid to attacking image retrieval systems. In this paper, we make the first attempt in attacking image retrieval systems. Concretely, image retrieval attack is to make the retrieval system return irrelevant images to the query at the top ranking list. It plays an important role to corrupt the neighbourhood relationships among features in image retrieval attack. To this end, we propose a novel method to generate retrieval-against UAP to break the neighbourhood relationships of image features via degrading the corresponding ranking metric. To expand the attack method to scenarios with varying input sizes or untouchable network parameters, a multi-scale random resizing scheme and a ranking distillation strategy are proposed. We evaluate the proposed method on four widely-used image retrieval datasets, and report a significant performance drop in terms of different metrics, such as mAP and mP@10. Finally, we test our attack methods on the real-world visual search engine, i.e., Google Images, which demonstrates the practical potentials of our methods.

Optimal Transport Theory for Cell Association in UAV-Enabled Cellular Networks

Abstract: In this paper, a novel framework for delay-optimal cell association in unmanned aerial vehicle (UAV)-enabled cellular networks is proposed. In particular, to minimize the average network delay under any arbitrary spatial distribution of the ground users, the optimal cell partitions of UAVs and terrestrial base stations (BSs) are determined. To this end, using the powerful mathematical tools of optimal transport theory, the existence of the solution to the optimal cell association problem is proved and the solution space is completely characterized. The analytical and simulation results show that the proposed approach yields substantial improvements of the average network delay.

Highly N,P-doped carbon dots: Rational design, photoluminescence and cellular imaging

Abstract: The authors describe the synthesis of carbon dots (CDs) that are highly doped with both nitrogen and phosphorus. Synthesis is accomplished via a hydrothermal reaction starting from diethylenetriaminepenta(methylenephosphonic acid) and m-phenylenediamine as the precursors. The high N,P-doping ratios renders the codoped CDs excellently water soluble, photostable over a wide range of pH values, and photostable in the presence of various metal ions. Ferric ions acts as a strong quencher of fluorescence. Their low cytotoxicity and strong green fluorescence (with excitation/emission peaks at 440/510 nm and a quantum yield of 0.32) make the CDs well suited for purposes of cell imaging, and this is demonstrated by fluorescent bioimaging of human lung carcinoma cells (type A549) and human breast cancer cells (type KB). Furthermore, the CDs were used as an effective probe for monitoring Fe(III) in both aqueous solution and living cells.

Differential privacy in telco big data platform

Abstract: Differential privacy (DP) has been widely explored in academia recently but less so in industry possibly due to its strong privacy guarantee. This paper makes the first attempt to implement three basic DP architectures in the deployed telecommunication (telco) big data platform for data mining applications. We find that all DP architectures have less than 5% loss of prediction accuracy when the weak privacy guarantee is adopted (e.g., privacy budget parameter e ≥ 3). However, when the strong privacy guarantee is assumed (e.g., privacy budget parameter e ≤ 0:1), all DP architectures lead to 15% ~ 30% accuracy loss, which implies that real-word industrial data mining systems cannot work well under such a strong privacy guarantee recommended by previous research works. Among the three basic DP architectures, the Hybridized DM (Data Mining) and DB (Database) architecture performs the best because of its complicated privacy protection design for the specific data mining algorithm. Through extensive experiments on big data, we also observe that the accuracy loss increases by increasing the variety of features, but decreases by increasing the volume of training data. Therefore, to make DP practically usable in large-scale industrial systems, our observations suggest that we may explore three possible research directions in future: (1) Relaxing the privacy guarantee (e.g., increasing privacy budget e) and studying its effectiveness on specific industrial applications; (2) Designing specific privacy scheme for specific data mining algorithms; and (3) Using large volume of data but with low variety for training the classification models.