Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

A simple participant attack on the brádler-dušek protocol

Abstract: The ring-arrangement quantum secret sharing protocol in the paper [K. Bradler and M. Dusek (2004) J. Opt. B: Quantum Semiclass. Opt. 6 63] is analyzed and it is shown that this protocol is secure for any eavesdropper except for a dishonest participant. For example, by a special strategy, Bob can steal Charlie's portion of information without being detected and then recover Alice's secret by himself. We give a description of this strategy and point out a possible way to improve the protocol to stand against this attack.

Stack decoding of polar codes

Abstract: A successive cancellation stack (SCS) decoding algorithm is proposed to improve the performance of polar codes. Unlike the conventional successive cancellation decoder which determines the bits successively with a local optimal strategy, the SCS algorithm stores a number of candidate partial paths in an ordered stack and tries to find the global optimal estimation by searching along the best path in the stack. Simulation results in the binary-input additive white Gaussian noise channel show that the SCS algorithm has the same performance as the successive cancellation list (SCL) algorithm and can approach that of the maximum likelihood algorithm. Moreover, the time complexity of the SCS decoder is much lower than that of the SCL and can be very close to that of the SC in the high SNR regime.

Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering

Abstract: State-of-the-art subspace clustering methods are based on expressing each data point as a linear combination of other data points while regularizing the matrix of coefficients with l1, l2 or nuclear norms. l1 regularization is guaranteed to give a subspace-preserving affinity (i.e., there are no connections between points from different subspaces) under broad theoretical conditions, but the clusters may not be connected. l2 and nuclear norm regularization often improve connectivity, but give a subspace-preserving affinity only for independent subspaces. Mixed l1, l2 and nuclear norm regularizations offer a balance between the subspacepreserving and connectedness properties, but this comes at the cost of increased computational complexity. This paper studies the geometry of the elastic net regularizer (a mixture of the l1 and l2 norms) and uses it to derive a provably correct and scalable active set method for finding the optimal coefficients. Our geometric analysis also provides a theoretical justification and a geometric interpretation for the balance between the connectedness (due to l2 regularization) and subspace-preserving (due to l1 regularization) properties for elastic net subspace clustering. Our experiments show that the proposed active set method not only achieves state-of-the-art clustering performance, but also efficiently handles large-scale datasets.

Fast, scalable detection of "Piggybacked" mobile applications

Abstract: Mobile applications (or apps) are rapidly growing in number and variety. These apps provide useful features, but also bring certain privacy and security risks. For example, malicious authors may attach destructive payloads to legitimate apps to create so-called "piggybacked" apps and advertise them in various app markets to infect unsuspecting users. To detect them, existing approaches typically employ pair-wise comparison, which unfortunately has limited scalability. In this paper, we present a fast and scalable approach to detect these apps in existing Android markets. Based on the fact that the attached payload is not an integral part of a given app's primary functionality, we propose a module decoupling technique to partition an app's code into primary and non-primary modules. Also, noticing that piggybacked apps share the same primary modules as the original apps, we develop a feature fingerprint technique to extract various semantic features (from primary modules) and convert them into feature vectors. We then construct a metric space and propose a linearithmic search algorithm (with O(n log n) time complexity) to efficiently and scalably detect piggybacked apps. We have implemented a prototype and used it to study 84,767 apps collected from various Android markets in 2011. Our results show that the processing of these apps takes less than nine hours on a single machine. In addition, among these markets, piggybacked apps range from 0.97% to 2.7% (the official Android Market has 1%). Further investigation shows that they are mainly used to steal ad revenue from the original developers and implant malicious payloads (e.g., for remote bot control). These results demonstrate the effectiveness and scalability of our approach.

A Vision of C-V2X: Technologies, Field Testing, and Challenges With Chinese Development

Abstract: Cellular vehicle-to-everything (C-V2X) is an important enabling technology for autonomous driving and intelligent transportation systems. It evolves from long-term evolution (LTE)-V2X to new radio (NR)-V2X, which will coexist and be complementary with each other to provide low-latency, high-reliability, and high-throughput communications for various C-V2X applications. In this article, a vision of C-V2X is presented. The requirements of the basic road safety and advanced applications, the architecture, the key technologies, and the standards of C-V2X are introduced, highlighting the technical evolution path from LTE-V2X to NR-V2X. Especially, based on the continual and active promotion of C-V2X research, field testing, and development in China, the related works and progresses are also presented. Finally, the trends of C-V2X applications with technical challenges are envisioned.