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..

A Random Decision Tree Framework for Privacy-Preserving Data Mining

Abstract: Distributed data is ubiquitous in modern information driven applications. With multiple sources of data, the natural challenge is to determine how to collaborate effectively across proprietary organizational boundaries while maximizing the utility of collected information. Since using only local data gives suboptimal utility, techniques for privacy-preserving collaborative knowledge discovery must be developed. Existing cryptography-based work for privacy-preserving data mining is still too slow to be effective for large scale data sets to face today’s big data challenge. Previous work on random decision trees (RDT) shows that it is possible to generate equivalent and accurate models with much smaller cost. We exploit the fact that RDTs can naturally fit into a parallel and fully distributed architecture, and develop protocols to implement privacy-preserving RDTs that enable general and efficient distributed privacy-preserving knowledge discovery.

OAuth Web Authorization Protocol

Abstract: Allowing one Web service to act on our behalf with another has become increasingly important as social Internet services such as blogs, photo sharing, and social networks have become widely popular. OAuth, a new protocol for establishing identity management standards across services, provides an alternative to sharing our usernames and passwords, and exposing ourselves to attacks on our online data and identities.

OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction

Abstract: OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE). Specifically, by implementing typical RE methods, OpenNRE not only allows developers to train custom models to extract structured relational facts from the plain text but also supports quick model validation for researchers. Besides, OpenNRE provides various functional RE modules based on both TensorFlow and PyTorch to maintain sufficient modularity and extensibility, making it becomes easy to incorporate new models into the framework. Besides the toolkit, we also release an online system to meet real-time extraction without any training and deploying. Meanwhile, the online system can extract facts in various scenarios as well as aligning the extracted facts to Wikidata, which may benefit various downstream knowledge-driven applications (e.g., information retrieval and question answering). More details of the toolkit and online system can be obtained from http://github.com/thunlp/OpenNRE.

Multimode Decoupling Technique With Independent Tuning Characteristic for Mobile Terminals

Abstract: The isolation between antenna elements is a key metric in some promising fifth generation technologies such as beamforming and in-band full-duplex However, the multimode decoupling technology remains a great challenge, especially for mobile terminals One difficulty in achieving multidecoupling modes is that the operating modes of closely packed decoupling elements have very strong mutual effect, which makes the tuning complicated and even unfeasible Thus, in physical principle, a novel idea of achieving the stability of the boundary conditions of decoupling elements is proposed to solve the mutual effect problem; in physical structure, a metal boundary is adopted to realize the stability One distinguished feature of the proposed technique is that the independent tuning characteristic can be maintained even if the number of decoupling elements increases Therefore, wideband/multiband high isolation can be achieved by using multidecoupling elements To validate the concept, two case studies are given In a quad-mode decoupling design, the isolation is enhanced from 127 to >21 dB within 22% bandwidth by using a $0295\lambda _{0}\times 0059\lambda _{0}\times 0007\lambda _{0}$ decoupling structure The mechanism of the decoupling technique and the mutual effect between decoupling elements are investigated

HEVC Lossless Coding and Improvements

Abstract: The lossless coding mode of the High Efficiency Video Coding (HEVC) main profile that bypasses transform, quantization, and in-loop filters is described. Compared to the HEVC nonlossless coding mode with the smallest quantization parameter value (i.e., 0 for 8-b video and -12 for 10-b video), the HEVC lossless coding mode provides perfect fidelity and an average bit-rate reduction of 3.2%-13.2%. It also significantly outperforms the existing lossless compression solutions, such as JPEG2000 and JPEG-LS for images as well as 7-Zip and WinRAR for data archiving. To further improve the coding efficiency of the HEVC lossless mode, a sample-based angular intra prediction (SAP) method is presented. The SAP employs the same prediction mode signaling method and the sample interpolation method as the HEVC block-based angular prediction, but uses adjacent neighbors for better intra prediction accuracy and performs prediction sample by sample. The experimental results reveal that the SAP provides an additional bit-rate reduction of 1.8%-11.8% on top of the HEVC lossless coding mode.