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A Fast Method for Shortest-Path Cover Identification in Large Complex Networks

Computers, Materials & Continua 2020年第5期63卷 705-724页

作者： Qiang Wei Guangmin Hu Chao Shen Yunfei Yin School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041China MOE Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong UniversityXi’an710049China The Electronic Engineering Department Universidad de SevillaSeville41004Spain School of Astronautics Harbin Institute of TechnologyHarbin150001China

Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and ***,the existing methods are time-consuming for even moderate-scale *** this paper,we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance *** effectiveness of the proposed method is validated through synthetic and real-world *** experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.

关键词： Network discovery shortest-path cover shortest-path distance query large complex networks

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Analysis of the jamming method for high frequency M-dimensional orthogonal modulated signals 19

Analysis of the jamming method for high frequency M-dimensio...

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2019 International Conference on Artificial Intelligence, Information processing and Cloud Computing, AIIPCC 2019

作者： Zhu, Shaozheng Xin, Jirong Luo, Wuzhong Tang, Wenqi National Key Lab of Science and Technology on Blind Signal Processing Chengdu Sichuan China

ISBN: (纸本)9781450376334

In order to disturb the M-dimensional orthogonal modulated signal effectively, the link layer coding in M-dimensional orthogonal modulated signal is studied. A Walsh-coding jamming signal is proposed to jam M-dimensional orthogonal modulated signals with Walsh modulation. The simulation results show that the interference signal with the same parameters of the target M-dimensional modulation signal performs best. © 2019 Association for Computing Machinery.

关键词： Jamming

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A Modified Multi-Objective Whale Optimization Algorithm with Dynamic Leader Selection Mechanism

A Modified Multi-Objective Whale Optimization Algorithm with...

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International Conference on Measuring technology and Mechatronics Automation, ICMTMA

作者： Wentao Feng Bing Deng National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan China

ISBN: (数字)9781728170817

ISBN: (纸本)9781728170824

The whale optimization algorithm (WOA) has less control parameters and it is easier to implement. The multi-objective whale optimization algorithm (MOWOA) also shows good exploration and exploitation capability. A modified multi-objective whale optimization algorithm with dynamic leader selection mechanism (MMOWOA-DLS) is proposed. First, the opposition-based learning (OBL) is employed to accelerate the convergence speed. Second, a dynamic leader selection mechanism is utilized to improve the solution accuracy. Third, a modified archive grid controller is proposed to delete redundant solutions in external archive. The simulation results show that the performance of MMOWOA-DLS outperforms other algorithms.

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Continuous-time dynamic network node classification algorithm based on autoencoder 9

Continuous-time dynamic network node classification algorith...

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9th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2020

作者： Huang, Xin Li, Yun Xiong, Jinyu Liang, Jie Wu, Jie Jing, Jie PLA Strategic Support Force Information Engineering University College of Information System Engineering Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China Officers College of PAP Chengdu China

Node classification is an important issue in network researches. Existing methods lay more emphasis on the classification of static network nodes, while there are insufficient researches on network dynamic characteristics. At the same time, the existing adjacency matrix representation cannot reflect the potential relationship of indirectly connected nodes. To better describe the network structure and utilize network time information, the author proposes a new Node Classification Algorithm based on Autoencoder, namely NCAA. It adopts the random walk in limited spatio-temporal and the matrix representation of node relationship. The low-dimensional representation vector of the node is generated by the autoencoder model, and the LogicRegression classifier is used to classify the representation vectors, thereby achieving node classification. Experiments show that, compared to some classic algorithms, NCAA can better represent the network, and its two-dimensional visualization of node vectors has a superior effect. Under the same conditions, the F1 value of the classification result is significantly higher than that of baseline algorithms. © 2020 IEEE.

关键词： Classification (of information)

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Tag Pollution Detection in Web Videos via Cross-Modal Relevance Estimation

Tag Pollution Detection in Web Videos via Cross-Modal Releva...

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International Workshop on Quality of Service

作者： Yixin Chen Xinye Lin Keshi Ge Wenbo He Dongsheng Li College of Computer National University of Defense Technology National Key Laboratory of Science and Technology on Blind Signal Processing McMaster University

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

In the era of big data, web videos are known for their astronomical volume and great difficulties to be understood by computers. Therefore it is challenging to detect and curb tag pollution on social networking platforms. Intuitively, the pollution in the tags of a video can be identified by exploiting the tags of visually similar videos. From this intuition, we develop a semi-supervised approach to estimate the relevance between Internet videos and user-provided labels accurately and detect polluted labels accordingly. To further enhance the accuracy of relevance estimation and pollution detection, we introduce three multi-view multi-label models, which employ the coherence and differences between various similarity relations of videos. Compared with two quintessential multi-view fusion models, the proposed models consistently outperform or achieve comparable performance.

关键词： Videos Visualization Pollution Neural networks Social network services Correlation Unsolicited electronic mail

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Research on Automatic Decoding of Morse Code Based on Deep Learning

Research on Automatic Decoding of Morse Code Based on Deep L...

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2019 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2019

作者： Li, Weihao Wang, Keren Information Engineering University Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781728161068

The current automatic decoding method of the Morse telegram has limited accuracy, and can't adapt to signal distortion and code length deviation of the manual telegram. This paper introduces the deep learning method and constructs an automatic decoding model, which integrates feature extraction, sequence modeling and transcription into an end-to-end training neural network. The time-frequency diagrams of signals are used for training and testing. Experimental results show that the decoding system has strong adaptability to manual deviation and frequency drift, and is robust in a noisy environment. © 2019 IEEE.

关键词： Decoding

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Performance Analysis of OSM-OFDM Systems with Carrier Frequency Offset

Performance Analysis of OSM-OFDM Systems with Carrier Freque...

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International Conference on Communication technology (ICCT)

作者： Tingmin Jiang Yue Xiao Lilin Dan Xinrong Ji Wenqi Tang Yan Zhao National Key Laboratory of Science and Technology on Communications UESTC Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (数字)9781728181417

ISBN: (纸本)9781728181424

Offset spatial modulation (OSM) is a novel modulation scheme with simplified radio-frequency-switching structure. In this paper, the concept of OSM is combined with orthogonal frequency division multiplexing (OFDM) transmission to take advantage of the benefits of these two techniques. We shed light on the effects of carrier frequency offset (CFO) on the OSM-OFDM systems over multipath Rayleigh fading channel, where the inter-carrier interference (ICI) induced by CFO degrades the system performance. We further derive the theoretical bit-error rate (BER) upper bound of OSM-OFDM impaired by CFO. Simulation results show that the theoretical BER is in good agreement with its simulated counterpart, which demonstrates the accuracy of the theoretical analysis.

关键词： OFDM Transmitting antennas Receiving antennas Precoding Radio frequency Frequency division multiplexing Upper bound

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Sparse Bayesian Learning for Distributed Passive Radar Imaging via Covariance Sparse Representation 6

Sparse Bayesian Learning for Distributed Passive Radar Imagi...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Gong, Fukang Zhang, Shunsheng He, Qing Research Institute of Electronic Science and Technology University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781728129129

Due to low imaging cost and robustness, the distributed passive radars using multiple transmitters and multiple receivers to observe targets have become a hot research. In the case of low SNR, the imaging accuracy of the distributed passive radar imaging model via Orthogonal Matching Pursuit (OMP) sparse reconstruction is low. For this problem, a framework consisting of sparse representation of the received multi-snapshot radar signal covariance matrix, Sparse Bayesian Learning (SBL) based reconstruction algorithm has been built. At the end of paper, through the simulation experiment, the imaging results of the original data and covariance data at low SNR are compared, and the reconstruction errors under different SNR are used to verify the effectiveness of the proposed algorithm. © 2019 IEEE.

关键词： Covariance matrix

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Deep learning detection of anomalous patterns from bus trajectories for traffic insight analysis

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Knowledge-Based Systems 2021年 217卷 106833-106833页

作者： Zhang, Xiaocai Zheng, Yi Zhao, Zhixun Liu, Yuansheng Blumenstein, Michael Li, Jinyan Advanced Analytics Institute Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia National Key Laboratory of Science and Technology on Blind Signal Processing ChengduSichuan610041 China College of Information Science and Engineering Hunan University ChangshaHunan410082 China Australian Artificial Intelligence Institute & Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia

Existing data-driven methods for traffic anomaly detection are modeled on taxi trajectory datasets. The concern is that the data may contain much inaccuracy about the actual traffic situations, because taxi drivers often choose optimal routes to evade from the congestions caused by traffic anomalies. We use bus trajectory data in this work. Bus trajectories can capture real traffic conditions in the road networks without drivers’ preference, which are more objective and appropriate for accurately detecting anomalous patterns for a broad range of insight analyses on traffics. We proposed a deep learning-based feature visualization method to map 3-dimensional features into a red–green–blue (RGB) color space. A color trajectory (CT) is then derived by encoding a trajectory with the RGB colors. With the spatial and temporal properties extracted from the CT, spatio-temporal outliers are detected by a novel offline detection method. We then conduct GIS map fusion to obtain insights for better understanding the traffic anomaly locations, and more importantly the influences on the road affected by the corresponding anomalies. Extended from the offline detection, an online detection method is developed for real-time detection of anomalous patterns. Our proposed methods were tested on 3 real-world bus trajectory datasets to demonstrate the performance of high accuracies, high detection rates and relatively low false alarm rates. © 2021

关键词： Trajectories

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Active Learning for Spam Email Classification 19

Active Learning for Spam Email Classification

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Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence

作者： Zheng Chen Ruiwen Tao Xiaoyang Wu Zhimin Wei Xiao Luo School of Information & Software Engineering University of Electronic Science & Technology of China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan China

ISBN: (纸本)9781450372619

Deep learning has yielded state-of-the-art performance on text classification tasks. In this paper, a new neural network based on Long-Short-Term-Memory model is applied to classify spam emails. Using deep learning method to classify spam emails requires large amounts of labeled data. To solve this problem, active learning method is used to reduce labeling cost and increase model adaptability. In this paper, it is found that the new model performs better than standard CNNs and RNNs on email classification task, and active learning methods can match state-of-the-art performance with just 10% of the labeled data.

关键词： LSTM

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