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.

One2Multi Graph Autoencoder for Multi-view Graph Clustering

Abstract: Multi-view graph clustering, which seeks a partition of the graph with multiple views that often provide more comprehensive yet complex information, has received considerable attention in recent years. Although some efforts have been made for multi-view graph clustering and achieve decent performances, most of them employ shallow model to deal with the complex relation within multi-view graph, which may seriously restrict the capacity for modeling multi-view graph information. In this paper, we make the first attempt to employ deep learning technique for attributed multi-view graph clustering, and propose a novel task-guided One2Multi graph autoencoder clustering framework. The One2Multi graph autoencoder is able to learn node embeddings by employing one informative graph view and content data to reconstruct multiple graph views. Hence, the shared feature representation of multiple graphs can be well captured. Furthermore, a self-training clustering objective is proposed to iteratively improve the clustering results. By integrating the self-training and autoencoder’s reconstruction into a unified framework, our model can jointly optimize the cluster label assignments and embeddings suitable for graph clustering. Experiments on real-world attributed multi-view graph datasets well validate the effectiveness of our model.

Coupled-Resonator-Induced Fano Resonances for Plasmonic Sensing with Ultra-High Figure of Merits

Abstract: Fano resonances are numerically predicted in an ultracompact plasmonic structure, comprising a metal-isolator-metal (MIM) waveguide side-coupled with two identical stub resonators. This phenomenon can be well explained by the analytic model and the relative phase analysis based on the scattering matrix theory. In sensing applications, the sensitivity of the proposed structure is about 1.1 × 103 nm/RIU and its figure of merit is as high as 2 × 105 at λ = 980 nm, which is due to the sharp asymmetric Fano line-shape with an ultra-low transmittance at this wavelength. This plasmonic structure with such high figure of merits and footprints of only about 0.2 μm2 may find important applications in the on-chip nano-sensors.

Federated Learning for Data Privacy Preservation in Vehicular Cyber-Physical Systems

Abstract: Recent developments in technologies such as MEC and AI contribute significantly in accelerating the deployment of VCPS. Techniques such as dynamic content caching, efficient resource allocation, and data sharing play a crucial role in enhancing the service quality and user driving experience. Meanwhile, data leakage in VCPS can lead to physical consequences such as endangering passenger safety and privacy, and causing severe property loss for data providers. The increasing volume of data, the dynamic network topology, and the availability of limited resources make data leakage in VCPS an even more challenging problem, especially when it involves multiple users and multiple transmission channels. In this article, we first propose a secure and intelligent architecture for enhancing data privacy. Then we present our new privacy-preserving federated learning mechanism and design a two-phase mitigating scheme consisting of intelligent data transformation and collaborative data leakage detection. Numerical results based on a real-world dataset demonstrate the effectiveness of our proposed scheme and show that our scheme achieves good accuracy, efficiency, and high security.

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

Abstract: Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble -nearest neighbor graphs- (-NNG-) based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.

cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks

Abstract: Swarm intelligence algorithms have been widely adopted in solving many highly nonlinear, multimodal problems and have achieved tremendous successes. However, their application on deep neural networks is largely unexplored. On the other hand, deep neural networks, especially convolutional neural network (CNN), have recently achieved breakthroughs in tackling many intractable problems; nevertheless their performance depends heavily on the chosen values of their hyper-parameters, whose fine-tuning is both labor-intensive and time-consuming. In this paper, we propose a novel particle swarm optimization (PSO) variant cPSO-CNN for optimizing the hyper-parameter configuration of architecture-determined CNNs. cPSO-CNN utilizes a confidence function defined by a compound normal distribution to model experts' knowledge on CNN hyper-parameter fine-tunings so as to enhance the canonical PSO's exploration capability. cPSO-CNN also redefines the scalar acceleration coefficients of PSO as vectors to better adapt for the variant ranges of CNN hyper-parameters. Besides, a linear prediction model is adopted for fast ranking the PSO particles to reduce the cost of fitness function evaluation. The experimental results demonstrate that cPSO-CNN performs competitively when compared with several reported algorithms in terms of both CNN hyper-parameter superiority and overall computation cost.