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19th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Zuo Wenbo Liu Zhen Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781665493895

Graph structure is a widely existing data structure in real life. It has good modeling ability. Social networks, physical particles, texts, and so on, all can be represented as graph structure. Link prediction is a classical task in graph mining. This task uses the information of the observed network to predict missing links or possible new links between node pairs. Most of the existing studies are based on unweighted graphs. But in fact, lots of scenarios should be abstracted as weighted graphs, and the weight of links can reflect the strength of the ties between nodes. In this paper, we propose a deep weighted graph embedding method based on autoencoder and contrastive learning for link weight prediction on weighted graphs. Experiments on five real-world graph datasets demonstrate the effectiveness of our method.

关键词： Graph embedding Link weight prediction Contrastive learning auto-encoder

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Deep Reinforcement Learning For Secure Communication 96

Deep Reinforcement Learning For Secure Communication

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IEEE 96th Vehicular Technology Conference (VTC-Fall)

作者： Yang, Yinchao Shikh-Bahaei, Mohammad Kings Coll London Dept Engn London England

ISBN: (纸本)9781665454681

In physical layer security, one interest of the community is the development of practical approaches to achieve reliable and secure communication, such as model-free approaches, in which the gradient of the channel model is required. This paper proposes a new model-free approach based on information-theoretic metrics. We train the encoder with deep reinforcement learning that uses a policy-based gradient descent algorithm whose loss function contains a feed-forward neural network. Simulation results show that our model is capable of retaining the eavesdropper's BLER at a high level whilst ensuring the legitimate receiver's BLER reduces to nearly zero.

关键词： Wiretap channel Machine learning auto-encoder Model-free Physical Layer Security

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Enabling Heterogeneous Domain Adaptation in Multi-inhabitants Smart Home Activity Learning 18

Enabling Heterogeneous Domain Adaptation in Multi-inhabitant...

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18th IEEE International Conference on Mobility, Sensing and Networking (MSN)

作者： Rahman, Md Mahmudur Mousavi, Mahta Tarr, Peri Alam, Mohammad Arif U. I. Univ Massachusetts Lowell Lowell MA 01854 USA Univ Calif San Diego San Diego CA USA IBMS Thomas J Watson Res Ctr Yorktown Hts NY USA Univ Massachusetts Chan Med Sch Worcester MA USA

ISBN: (纸本)9781665464574

Domain adaptation for sensor-based activity learning is of utmost importance in remote health monitoring research. However, many domain adaptation algorithms suffer with failure to operate adaptation in presence of target domain heterogeneity (which is always present in reality) and presence of multiple inhabitants dramatically hinders their generalizability producing unsatisfactory results for semi-supervised and unseen activity learning tasks. We propose AEDA, a novel deep auto-encoderbased model to enable semi-supervised domain adaptation in the existence of target domain heterogeneity and how to incorporate it to empower heterogeneity to any homogeneous deep domain adaptation architecture for cross-domain activity learning. Experimental evaluation on 18 different heterogeneous and multiinhabitants use-cases of 8 different domains created from 2 publicly available human activity datasets (wearable and ambient smart homes) shows that AEDA outperforms (max. 12.8% and 8.9% improvements for ambient smart home and wearables) over existing domain adaptation techniques for both seen and unseen activity learning in a heterogeneous setting.

关键词： Deep Learning Activity Recognition Domain Adaptation Semi-supervised Learning auto-encoder

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Quantitative estimation of side-channel leaks with neural networks

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INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER 2021年第4期23卷 641-654页

作者： Tizpaz-Niari, Saeid Cerny, Pavol Sankaranarayanan, Sriram Trivedi, Ashutosh Univ Texas El Paso El Paso TX 79968 USA TU Wien Vienna Austria Univ Colorado Boulder CO 80309 USA

Information leaks via side channels remain a challenging problem to guarantee confidentiality. Static analysis is a prevalent approach for detecting side channels. However, the side-channel analysis poses challenges to the static techniques since they arise from non-functional aspects of systems and require an analysis of multiple traces. In addition, the outcome of static analysis is usually restricted to binary answers. In practice, real-world applications may need to disclose some aspects of the confidential information to ensure desired functionality. Therefore, quantification techniques are necessary to evaluate the resulting threats. In this paper, we propose a dynamic analysis technique to detect and quantify side channels. Our novel approach is to split the problem into two tasks. First, we learn a timing model of the program as a neural network. While the program implements the functionality, the neural network models the non-functional property that does not exist in the syntax or semantics of programs. Second, we analyze the neural network to quantify information leaks. As demonstrated in our experiments, both of these tasks are feasible in practice-making the approach a significant improvement over state-of-the-art side channel detectors and quantifiers. Thus, our key technical contributions are (a) a binarized neural network architecture that enables side-channel discovery and (b) a novel MILP-based counting algorithm to estimate the side-channel strength. On a set of benchmarks, we show that neural network models the timing of programs with thousands of methods precisely. We also show that neural networks with thousands of neurons can be efficiently analyzed to quantify information leaks via timing side channels.

关键词： Security Deep learning Side channels Binarized neural network auto-encoder Quantitative information flow

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End-to-End Optimization of Coherent Optical Communications Over the Split-Step Fourier Method Guided by the Nonlinear Fourier Transform Theory

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2021年第2期39卷 418-428页

作者： Gaiarin, Simone Da Ros, Francesco Jones, Rasmus T. Zibar, Darko Tech Univ Denmark Dept Photon Engn DK-2800 Lyngby Denmark Oticon DK-2765 Smorum Denmark

Optimizing modulation and detection strategies for a given channel is critical to maximizing the throughput of a communication system. Such an optimization can be easily carried out analytically for channels that admit closed-form analytical models. However, this task becomes extremely challenging for nonlinear dispersive channels such as the optical fiber. End-to-end optimization through autoencoders (AEs) can be applied to define symbol-to-waveform (modulation) and waveform-to-symbol (detection) mappings, but so far it has been mainly shown for systems relying on approximate channel models. Here, for the first time, we propose an AE scheme applied to the full optical channel described by the nonlinear Schrodinger equation (NLSE). Transmitter and receiver are jointly optimized through the split-step Fourier method (SSFM) which accurately models an optical fiber. In this first numerical analysis, the detection is performed by a neural network (NN), whereas the symbol-to-waveform mapping is aided by the nonlinear Fourier transform (NFT) theory in order to simplify and guide the optimization on the modulation side. This proof-of-concept AE scheme is thus benchmarked against a manually-optimized NFT-based system and a three-fold increase in achievable distance (from 2000 to 6640 km) is demonstrated.

关键词： Optical transmitters Optimization Communication systems Channel models Nonlinear optics Optical receivers auto-encoder modulation detection nonlinear frequency division multiplexing nonlinear Fourier transform

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Vehicle Trajectory Reconstruction on Urban Traffic Network Using automatic License Plate Recognition Data

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IEEE ACCESS 2021年 9卷 49110-49120页

作者： Qi, Xinyi Ji, Yanjie Li, Wenhao Zhang, Shuichao Southeast Univ Sch Transportat Nanjing 211189 Peoples R China No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA Ningbo Univ Technol Sch Civil & Transportat Engn Ningbo 315211 Peoples R China

Vehicle trajectory data are critical to urban active traffic management and simulation applications. automatic license plate recognition (ALPR) data can provide partial vehicle trajectory information by matching the detected vehicle license plates through time series. However, the trajectory extracted from ALPR data tend to be sparse and incomplete due to technical and financial constraints. This paper deals with the problem of sparse trajectory reconstruction based on ALPR data. Firstly, the multiple travel activities of the vehicle are divided based on the reasonable travel time threshold, and the incomplete vehicle trajectory is identified. Then, candidate trajectories are generated by an improved K-shortest-path (KSP) algorithm based on space-time prism theory. Finally, the auto-encoder model is utilized to select the candidate trajectory with optimal decision indicators, which realizes the vehicle trajectory reconstruction. The proposed method was implemented on a realistic urban traffic network in Ningbo, China. The verification results show that the proposed method has a comprehensive accuracy of 85% and good robustness. From the comparison with the baseline algorithm, it can be seen that the proposed method still has high accuracy in low ALPR coverage rate, and there exists a minimum required ALPR coverage rate (50% in the test network) for reconstructing trajectories accurately.

关键词： Trajectory Data mining Roads Particle filters Licenses Detectors Data models automatic license plate recognition space-time prism auto-encoder trajectory reconstruction

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Multi-Layer Extreme Learning Machine-Based Keystroke Dynamics Identification for Intelligent Keyboard

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IEEE SENSORS JOURNAL 2021年第2期21卷 2324-2333页

作者： Zhao, Guangquan Wu, Zhiyi Gao, Yongcheng Niu, Guangxing Wang, Zhong Lin Zhang, Bin Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Georgia Inst Technol Sch Mat Sci & Engn Atlanta GA 30332 USA Univ South Carolina Dept Elect Engn Columbia SC 29208 USA

Information security plays critical roles in modern society. However, traditional security measures like passwords, tokens and personal ID numbers only provide limited protection. Inspired by the fast training speed of extreme learning machine (ELM) and the promising feature extraction capability of extreme learning machine auto-encoder (ELM-AE), this paper proposes a keystroke dynamics identification method for intelligent keyboard (IKB) based on multi-layer extreme learning machine (ML-ELM). The IKB, as first demonstrated by Wang's group, is a self-powered, non-mechanical perforated keyboard that converts mechanical stimuli applied to the keyboard into electrical signals without the needing an external power source. ML-ELM is a multilayer neural network stacking on top of ELM-AE. Our major contribution is to develop an accurate and efficient keystroke dynamics identification method based on ML-ELM. One significant advantage of the proposed method is that it does not rely on manual feature extraction and selection. In other words, the raw current signals obtained by IKB are directly the input to the network. The network structure is determined by grid search, which minimizes the human involvement. The other one is that the whole training process of the model does not require fine tuning, which makes the training process significantly faster than that of existing deep networks. Experimental results demonstrate that the proposed method has excellent timeliness whereas obtaining high identification accuracy. The proposed method has great potential in applications to computer or network access control, online payment, and cyber security.

关键词： Feature extraction Keyboards Training Electrodes Fingers Sensors Password Multi-layer extreme learning machine classification auto-encoder intelligent keyboard keystroke dynamics

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MSCS: Multi-stage feature learning with channel-spatial attention mechanism for infrared and visible image fusion

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INFRARED PHYSICS & TECHNOLOGY 2024年 142卷

作者： Huang, Zhenghua Xu, Biyun Xia, Menghan Li, Qian Zou, Lianying Li, Shaoyi Li, Xi Wuhan Donghu Univ Sch Elect & Informat Engn Wuhan 430212 Peoples R China Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Northwestern Polytech Univ Xian 710072 Peoples R China Nanchang Inst Sci & Technol Sch Informat & Artificial Intelligence Nanchang 330108 Peoples R China

The intention of infrared and visible image fusion is to combine the images captured by different modal sensors in the same scene to enhance its understanding. Deep learning has been proven its powerful application in image fusion due to its fine generalization, robustness, and representability of deep features. However, the performance of these deep learning-based methods heavily depends on the illumination condition. Especially in dark or exposed scenes, the fused results are over-smoothness and low-contrast, resulting in inaccuracy of object detection. To address these issues, this paper develops a multi-stage feature learning approach with channel- spatial attention mechanism, namely MSCS, for infrared and visible image fusion. The MSCS is composed of the following four key procedures: Firstly, the infrared and visible images are decomposed into illumination and reflectance components by a proposed network called as Retinex_Net. Then, the components are transported to an encoder for features coding. Next, we propose an adaptive fusion module with attention mechanisms to fuse the features. Finally, the fused image is generated by the decoder for decoding the fused features. Meanwhile, a novel fusion loss function and a multi-stage training strategy are proposed to train the above modules. The subjective and objective results of experiments on TNO, , LLVIP and MSRS datasets illustrate that the proposed method is effective and performs better than the state-of-the-art fusion methods on achieving enjoyable results in dark or over-exposure scenes. And the results of further experiments on the fused images for object detection demonstrate that the fusion outputs produced by our MSCS are more beneficial for detection tasks.

关键词： Image fusion Multi-stage features learning Attention mechanism auto-encoder

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Gas-insulated switch-gear mechanical fault detection based on acoustic using feature fused neural network

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 230卷

作者： Zhang, Zipeng Liu, Houguang Yuan, Guogang Yang, Jianhua Liu, Songyong Shao, Yuying Zhang, Yang China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Peoples R China Shanghai Rhythm Elect Technol Co Ltd Shanghai 201108 Peoples R China State Grid Shanghai Municipal Elect Power Co Shanghai 200122 Peoples R China

The acoustic -based method is a prevalent way for non -contact fault diagnosis on gas -insulated switchgear. Gas -insulated switchgear always work under different voltages causing great diversity in acoustic frequency, which challenges robust fault detection. This paper proposed a novel feature -fused method to improve the robustness of fault detection on gas -insulated switchgear. The proposed method consists of four components: wave reduction module, spectrogram reduction module, fusion module and classifier. Wave reduction module extracts operating voltage information from acoustic emissions in the gas -insulated switchgear;spectrogram reduction module uses auto -encoder training schedule for feature extraction on spectrogram;fusion module fuses extracted features;classifier makes final classification. Also, we proposed an objective function for thoroughly utilizing spectrogram information. The efficacy of the proposed method was validated using experimental data from a real gas -insulated switchgear, and it shows competitive performance in fault detection compared to existing methods.

关键词： Gas-insulated switchgear Fault diagnosis auto-encoder Feature fusion

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Robust autoencoder feature selector for unsupervised feature selection

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INFORMATION SCIENCES 2024年 660卷

作者： Ling, Yunzhi Nie, Feiping Yu, Weizhong Ling, Yunhao Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Xian 710072 Shaanxi Peoples R China Minist Ind & Informat Technol Xian 710072 Shaanxi Peoples R China East China Normal Univ Software Engn Inst Shanghai 200062 Peoples R China

Unsupervised Feature Selection (UFS) methods are known to produce models with excellent ability to select high -quality features. This advantage, however, is challenged when analyzing noisy real -world data, where anomalies are prevalent. For example, there may be a feature (anomalous feature) that is corrupted across many samples or a sample (anomalous sample) that has more corruptions than its peers. Previous literature focused on addressing anomalous samples, and methods that are robust to both types of anomalies have been under -explored. This paper proposes a novel general framework for reconstruction -based UFS methods, which can be embedded into the feature learning process to simultaneously remove anomalous samples and features. Specifically, the framework learns double binary weight vectors to assign 0 weights to samples or features with the highest reconstruction errors and 1 weights to the others when computing reconstruction errors. By discarding the 0 -weighted samples and features when updating the model parameters, the anomalies in the data are excluded. This allows the model to focus more on learning from the clean part of the noisy data. Our proposed framework is then integrated with autoencoder Feature Selector (AEFS [10]) to develop a new method, which jointly performs anomaly removal and feature selection. The experimental results demonstrate the effectiveness of the proposed framework. Particularly, processing both types of anomalies provides better robustness than processing only one type. Moreover, our proposed method outperforms several state-of-the-art methods on various real -world datasets.

关键词： Unsupervised feature selection auto-encoder Anomaly detection Robustness Unsupervised learning

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