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A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With Applications in Tomographic SAR Imaging

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for...

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作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Jiangsu215123 China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method. Conventional single-snapshot (a.k.a. single-measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the range and/or azimuth resolution or adopt the sparsity constraint and utilize the compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus, it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least-squares (LSs) estimation problem and solves the corresponding objective function via a gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, and performance analysis for evenly distributed antenna array cases are discussed in this article. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods, for example, CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experimental data. Results show the great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. © 1980-2012 IEEE.

关键词： Spectrum analysis

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GNEA: A Novel GCN-based Network Embedding Algorithm for Semantic Social Network

GNEA: A Novel GCN-based Network Embedding Algorithm for Sema...

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International Symposium on System and Software Reliability (ISSSR)

作者： Jinbo Zhao Jinling He Yuanhan Du Ming Tang Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350362930

ISBN: (纸本)9798350362947

With the development of semantic social networks, social networks become more complex and their size expands rapidly, which brings significant challenges to social network analysis. Network Embedding can transform the network from a high-dimensional adjacency matrix to a low-dimensional one, which has excellent foreground in link prediction, vertex classification, and graph visualization. Nowadays, most algorithms are proposed for static networks with little consideration of the dynamic changes in the network. However, most real-world networks change over time. Directly applying static network embedding algorithms to dynamic networks will result in poor stability, flexibility, and efficiency. Because of these shortcomings, we propose a graph convolution-based dynamic network representation learning algorithm (GNEA), which combines Graph Convolutional Network (GCN) with Long Short-term Memory (LSTM) to train the time series networks. GCN is applied to extract features in the network, and LSTM is utilized to capture temporal information. GNEA can not only perform characterization learning on the network but also obtain more abundant information during the dynamic evolution of the network. GNEA is compared with several typical algorithms on the datasets Stochastic Block Model (SBM), Autonomous Systems (AS), and Digital Bibliography and Library Project (DBLP). The results prove that GNEA has good performance in MAP and precision@k.

关键词： Representation learning Accuracy Social networking (online) Heuristic algorithms Semantics Prediction algorithms Feature extraction

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Variable projection algorithms with sparse constraint for separable nonlinear models

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Control Theory and Technology 2024年第1期22卷 135-146页

作者： Hui-Lang Xu Guang-Yong Chen Si-Qing Cheng Min Gan Jing Chen College of Computer and Data Science Fuzhou UniversityFuzhou350116FujianChina Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350116FujianChina Key Laboratory of Intelligent Metro of Universities in Fujian Fuzhou UniversityFuzhou350116FujianChina Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou UniversityFuzhou350116FujianChina School of Science Jiangnan UniversityWuxi214122JiangsuChina

Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on \(L_2\) penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradient-based and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.

关键词： Variable projection(VP) Non-smooth constraint Separable nonlinear models

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TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud 27

TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Jiang, Yu Xu, Xiaolong Lin, Kunda Duan, Weihua Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665408783

In order to select a composition scheme that meets user's needs and high performance from large-scale web services in the edge cloud, this paper proposes a trusted service composition optimization scheme called TSC-ECFA for edge cloud, which applies the predation strategy to the firefly algorithm (FA) and divides the services with the same input and output into one category, reducing the number of combinations to search for feasible solutions. The cotangent chaos theory is used to generate the initial firefly population and disturb the location of the individual to improve the overall search efficiency of FA. This paper also improves the step factor and attractiveness formula of FA to give full play to the detection ability of the step factor in the early stage of the algorithm, and balance the local search and the global search. Considering that the QoS attribute values of the service are vulnerable to be tampered with, the blockchain is used to store the QoS attribute values to ensure the tamper-proof and reliability of the QoS attribute value. Finally, simulation experiments compare the number of iterations and optimization of the three algorithms. The experimental results show that the comprehensive performance of TSC-ECFA is better than other algorithms. © 2021 IEEE.

关键词： Quality of service

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Model-based Offline Policy Optimization with Adversarial Network

arXiv

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arXiv 2023年

作者： Yang, Junming Chen, Xingguo Wang, Shengyuan Zhang, Bolei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China School of Modern Posts Nanjing University of Posts and Telecommunications China

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model’s uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Multi-task regression learning for survival analysis via prior information guided transductive matrix completion

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Frontiers of Computer Science 2020年第5期14卷 99-112页

作者： Lei Chen Kai Shao Xianzhong Long Lingsheng Wang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing210023China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and AstronauticsNanjing211106China

Survival analysis aims to predict the occurrence time of a particular event of interest,which is crucial for the prognosis analysis of ***,due to the limited study period and potential losing tracks,the observed data inevitably involve some censored instances,and thus brings a unique challenge that distinguishes from the general regression *** addition,survival analysis also suffers from other inherent challenges such as the high-dimension and small-sample-size *** address these challenges,we propose a novel multi-task regression learning model,i.e.,prior information guided transductive matrix completion(PigTMC)model,to predict the survival status of the new ***,we use the multi-label transductive matrix completion framework to leverage the censored instances together with the uncensored instances as the training samples,and simultaneously employ the multi-task transductive feature selection scheme to alleviate the overfitting issue caused by high-dimension and small-sample-size *** addition,we employ the prior temporal stability of the survival statuses at adjacent time intervals to guide survival ***,we design an optimization algorithm with guaranteed convergence to solve the proposed PigTMC ***,the extensive experiments performed on the real microarray gene expression datasets demonstrate that our proposed model outperforms the previously widely used competing methods.

关键词： survival analysis matrix completion multi-task regression transductive learning multi-task feature selection

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A Virtual Network Mapping Method Based on Compound Particle Swarm Optimization 7

A Virtual Network Mapping Method Based on Compound Particle ...

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7th International Conference on Cyber security and Information Engineering, ICCSIE 2022

作者： Huang, Jianhua Tang, Yunlong Wei, Yongjun Wang, Huan Zhang, Haifeng Zhang, Bing School of Computer Science and Technology School of Software Guangxi University of Science and Technology Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Guangxi Education System Network Security Monitoring Center Liuzhou China Liuzhou Railway Vocational Technical College Liuzhou China

ISBN: (纸本)9781665455749

Aiming at problems of low credibility of results and low accuracy of the scheme in the mapping process of virtual networks. It is suggested to use a composite particle swarm optimization approach for virtual network mapping. Firstly, to derive the maximum number of virtual cyberspaces, we construct the virtual network model and the physical network model;secondly, resource limitations are used to build the node mapping and link mapping models, and once more, the mapping scheme of the virtual network on the physical network is solved using the composite particle swarm algorithm to improve the acceptance rate of virtual network requests. In simulation experiments, we compare our established algorithm with two algorithms, D-ViNE-SP and VNE-R-PSO, in two dimensions, and the results show that our proposed VNE-MM-PSO mapping method improves the acceptance rate of network requests by 8% and also improves on the benefit-cost ratio. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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Dlr: Adversarial Examples Detection and Label Recovery for Deep Neural Networks

SSRN

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SSRN 2024年

作者： Han, Keji Li, Yun Ge, Yao Wang, Ruchuan Nanjing University of Posts and Telecommunications Wenyuan Road 9 Nanjing210046 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Wenyuan Road 9 Nanjing210046 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Wenyuan Road 9 Nanjing210046 China

Deep neural networks (DNNs) are demonstrated to be vulnerable to the adversarial example crafted by the adversary to fool the target model. Adversarial training and adversarial example detection are two popular methods to mitigate the adversarial issue. The adversarial training makes the target model recognize the adversarial example correctly for the image classification task, while it does not generalize well. The adversarial detection generalizes well but cannot help the target model recognize the adversarial example. We first define the label recovery task to address the adversarial issue for DNNs. Then we propose a new generative classifier and apply it to the adversarial example label recovery task. The proposed method is named as Detection and Label Recovery (DLR) defense method, which consists of Detector and Recover. Detector feeds the legitimate and adversarial examples to the target model and Recover, respectively. The Recover explores the ground-truth label of the detected adversarial example. DLR combines the merits of adversarial training and adversarial example detection. Experimental results demonstrate that our method outperforms some state-of-the-art adversarial training methods. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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A Fraud Detection Approach Based on Combined Feature Weighting 10th

A Fraud Detection Approach Based on Combined Feature Weighti...

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10th EAI International Conference on Cloud Computing, CloudComp 2020

作者： Liu, Xiaoqian Yu, Chenfei Xia, Bin Gu, Haiyan Wang, Zhenli Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Nanjing China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Nanjing China

ISBN: (纸本)9783030699918

data mining technology has yielded fruitful results in the area of crime discovery and intelligent decision making. Credit card is one of the most popular payment methods, providing great convenience and efficiency. However, due to the vulnerabilities of credit card transactions, criminals are able to commit fraud to infringe on the interests of the state and citizens. How to discover potential fraudsters while guaranteeing high efficiency becomes an extremely valuable problem to solve. In this work, we talk about the advantages and disadvantages of different models to detect credit card fraud. We first introduce the data preprocessing measures for handling imbalanced fraud detection dataset. Then we compare related models to implement fraudster recognition. We also propose a feature selection approach based on combined feature weights. Some future research interests are also envisioned. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Crime

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