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218 results on '"Cao, Weipeng"'

1. Knowledge Distillation with Classmate

Author

Su, Hongwei, Liu, Han, Cao, Weipeng, Ming, Zhong, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Zhang, Xiankun, editor, and Pan, Yijie, editor

Published
2024
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2. WGGAL: A Practical Time Series Forecasting Framework for Dynamic Cloud Environments

Author

Qiu, Yunyun, Cao, Weipeng, Xiao, Zhijiao, Ming, Zhong, Ji, Changping, Gu, Jiongjiong, Cao, Chuqing, Zheng, Liang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Cungeng, editor, Chen, Huajun, editor, Zhao, Liang, editor, Arshad, Junaid, editor, Asyhari, Taufiq, editor, and Wang, Yonghao, editor

Published
2024
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3. Cop: Continuously Pairing of Heterogeneous Wearable Devices Based on Heartbeat

Author

He, Wenfeng, Lin, Jiawei, Zou, Yongpan, Cao, Weipeng, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Cungeng, editor, Chen, Huajun, editor, Zhao, Liang, editor, Arshad, Junaid, editor, Asyhari, Taufiq, editor, and Wang, Yonghao, editor

Published
2024
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4. DSCVSR: A Lightweight Video Super-Resolution for Arbitrary Magnification

Author

Hong, Zixuan, Cao, Weipeng, Xu, Zhiwu, Ming, Zhong, Cao, Chuqing, Zheng, Liang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Cungeng, editor, Chen, Huajun, editor, Zhao, Liang, editor, Arshad, Junaid, editor, Asyhari, Taufiq, editor, and Wang, Yonghao, editor

Published
2024
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5. MetaVSR: A Novel Approach to Video Super-Resolution for Arbitrary Magnification

Author

Hong, Zixuan, Cao, Weipeng, Xu, Zhiwu, Chen, Zhenru, Tao, Xi, Ming, Zhong, Cao, Chuqing, Zheng, Liang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rudinac, Stevan, editor, Hanjalic, Alan, editor, Liem, Cynthia, editor, Worring, Marcel, editor, Jónsson, Björn Þór, editor, Liu, Bei, editor, and Yamakata, Yoko, editor

Published
2024
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7. Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles

Author

Ding, Liuhui, Li, Dachuan, Liu, Bowen, Lan, Wenxing, Bai, Bing, Hao, Qi, Cao, Weipeng, and Pei, Ke

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Systems and Control, 68T40, I.2.9
Abstract

Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles. In this paper, we propose a safe motion planning framework featuring the quantification and propagation of DNN-based perception uncertainties and motion uncertainties. Contributions of this work are twofold: (1) A Bayesian Deep Neural network model which detects 3D objects and quantitatively captures the associated aleatoric and epistemic uncertainties of DNNs; (2) An uncertainty-aware motion planning algorithm (PU-RRT) that accounts for uncertainties in object detection and ego-vehicle's motion. The proposed approaches are validated via simulated complex scenarios built in CARLA. Experimental results show that the proposed motion planning scheme can cope with uncertainties of DNN-based perception and vehicle motion, and improve the operational safety of autonomous vehicles while still achieving desirable efficiency., Comment: To appear in the 19th IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA 2021)

Published
2021

10. AdaL: Adaptive Gradient Transformation Contributes to Convergences and Generalizations

Author

Zhang, Hongwei, Zou, Weidong, Zhao, Hongbo, Ming, Qi, Yan, Tijin, Xia, Yuanqing, and Cao, Weipeng

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Adaptive optimization methods have been widely used in deep learning. They scale the learning rates adaptively according to the past gradient, which has been shown to be effective to accelerate the convergence. However, they suffer from poor generalization performance compared with SGD. Recent studies point that smoothing exponential gradient noise leads to generalization degeneration phenomenon. Inspired by this, we propose AdaL, with a transformation on the original gradient. AdaL accelerates the convergence by amplifying the gradient in the early stage, as well as dampens the oscillation and stabilizes the optimization by shrinking the gradient later. Such modification alleviates the smoothness of gradient noise, which produces better generalization performance. We have theoretically proved the convergence of AdaL and demonstrated its effectiveness on several benchmarks.

Published
2021

11. A comparative study of neural network techniques for automatic software vulnerability detection

Author

Tang, Gaigai, Meng, Lianxiao, Ren, Shuangyin, Cao, Weipeng, Wang, Qiang, and Yang, Lin

Subjects
Computer Science - Software Engineering, Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Software vulnerabilities are usually caused by design flaws or implementation errors, which could be exploited to cause damage to the security of the system. At present, the most commonly used method for detecting software vulnerabilities is static analysis. Most of the related technologies work based on rules or code similarity (source code level) and rely on manually defined vulnerability features. However, these rules and vulnerability features are difficult to be defined and designed accurately, which makes static analysis face many challenges in practical applications. To alleviate this problem, some researchers have proposed to use neural networks that have the ability of automatic feature extraction to improve the intelligence of detection. However, there are many types of neural networks, and different data preprocessing methods will have a significant impact on model performance. It is a great challenge for engineers and researchers to choose a proper neural network and data preprocessing method for a given problem. To solve this problem, we have conducted extensive experiments to test the performance of the two most typical neural networks (i.e., Bi-LSTM and RVFL) with the two most classical data preprocessing methods (i.e., the vector representation and the program symbolization methods) on software vulnerability detection problems and obtained a series of interesting research conclusions, which can provide valuable guidelines for researchers and engineers. Specifically, we found that 1) the training speed of RVFL is always faster than BiLSTM, but the prediction accuracy of Bi-LSTM model is higher than RVFL; 2) using doc2vec for vector representation can make the model have faster training speed and generalization ability than using word2vec; and 3) multi-level symbolization is helpful to improve the precision of neural network models., Comment: This paper has been published at April 28,2021. However, there are some experimental data issues in the published manuscript, which are caused by the calculation error of indicators. This paper is a revised version

Published
2021
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12. A Biologically Inspired Feature Enhancement Framework for Zero-Shot Learning

Author

Xie, Zhongwu, Cao, Weipeng, Wang, Xizhao, Ming, Zhong, Zhang, Jingjing, and Zhang, Jiyong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Most of the Zero-Shot Learning (ZSL) algorithms currently use pre-trained models as their feature extractors, which are usually trained on the ImageNet data set by using deep neural networks. The richness of the feature information embedded in the pre-trained models can help the ZSL model extract more useful features from its limited training samples. However, sometimes the difference between the training data set of the current ZSL task and the ImageNet data set is too large, which may lead to the use of pre-trained models has no obvious help or even negative impact on the performance of the ZSL model. To solve this problem, this paper proposes a biologically inspired feature enhancement framework for ZSL. Specifically, we design a dual-channel learning framework that uses auxiliary data sets to enhance the feature extractor of the ZSL model and propose a novel method to guide the selection of the auxiliary data sets based on the knowledge of biological taxonomy. Extensive experimental results show that our proposed method can effectively improve the generalization ability of the ZSL model and achieve state-of-the-art results on three benchmark ZSL tasks. We also explained the experimental phenomena through the way of feature visualization.

Published
2020

17. W-Hash: A Novel Word Hash Clustering Algorithm for Large-Scale Chinese Short Text Analysis

Author

Chen, Yaofeng, Zhang, Chunyang, Ye, Long, Peng, Xiaogang, Qiu, Meikang, Cao, Weipeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Memmi, Gerard, editor, Yang, Baijian, editor, Kong, Linghe, editor, Zhang, Tianwei, editor, and Qiu, Meikang, editor

Published
2022
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18. A Novel RVFL-Based Algorithm Selection Approach for Software Model Checking

Author

Cao, Weipeng, Wu, Yuhao, Wang, Qiang, Zhang, Jiyong, Zhang, Xingjian, Qiu, Meikang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Memmi, Gerard, editor, Yang, Baijian, editor, Kong, Linghe, editor, Zhang, Tianwei, editor, and Qiu, Meikang, editor

Published
2022
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19. Automated Reliability Analysis of Redundancy Architectures Using Statistical Model Checking

Author

He, Hongbin, Kuang, Hongyu, Yang, Lin, Yang, Feng, Wang, Qiang, Cao, Weipeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Memmi, Gerard, editor, Yang, Baijian, editor, Kong, Linghe, editor, Zhang, Tianwei, editor, and Qiu, Meikang, editor

Published
2022
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20. BLSHF: Broad Learning System with Hybrid Features

Author

Cao, Weipeng, Li, Dachuan, Zhang, Xingjian, Qiu, Meikang, Liu, Ye, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Memmi, Gerard, editor, Yang, Baijian, editor, Kong, Linghe, editor, Zhang, Tianwei, editor, and Qiu, Meikang, editor

Published
2022
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21. Charge Prediction for Criminal Law with Semantic Attributes

Author

Zhou, Cong, Cao, Weipeng, Xu, Zhiwu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Qiu, Meikang, editor, Gai, Keke, editor, and Qiu, Han, editor

Published
2022
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26. Interpretation of Learning-Based Automatic Source Code Vulnerability Detection Model Using LIME

Author

Tang, Gaigai, Zhang, Long, Yang, Feng, Meng, Lianxiao, Cao, Weipeng, Qiu, Meikang, Ren, Shuangyin, Yang, Lin, Wang, Huiqiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Qiu, Han, editor, Zhang, Cheng, editor, Fei, Zongming, editor, Qiu, Meikang, editor, and Kung, Sun-Yuan, editor

Published
2021
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27. Dense Incremental Extreme Learning Machine with Accelerating Amount and Proportional Integral Differential

Author

Zou, Weidong, Xia, Yuanqing, Qiu, Meikang, Cao, Weipeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Qiu, Han, editor, Zhang, Cheng, editor, Fei, Zongming, editor, Qiu, Meikang, editor, and Kung, Sun-Yuan, editor

Published
2021
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29. Distributed and Parallel Ensemble Classification for Big Data Based on Kullback-Leibler Random Sample Partition

Author

Wei, Chenghao, Zhang, Jiyong, Valiullin, Timur, Cao, Weipeng, Wang, Qiang, Long, Hao, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Qiu, Meikang, editor

Published
2020
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30. Broad Learning System with Proportional-Integral-Differential Gradient Descent

Author

Zou, Weidong, Xia, Yuanqing, Cao, Weipeng, Ming, Zhong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Qiu, Meikang, editor

Published
2020
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31. Research Progress of Zero-Shot Learning Beyond Computer Vision

Author

Cao, Weipeng, Zhou, Cong, Wu, Yuhao, Ming, Zhong, Xu, Zhiwu, Zhang, Jiyong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Qiu, Meikang, editor

Published
2020
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32. A Hierarchical-Tree-Based Method for Generative Zero-Shot Learning

Author

Wang, Xizhao, Xie, Zhongwu, Cao, Weipeng, Ming, Zhong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Qiu, Meikang, editor

Published
2020
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38. Runtime safety assurance methods for cyber physical systems based on simplex architecture.

Author

WANG Qiang, CHEN Qiang, CAO Weipeng, and HE Wenfeng

Abstract

Runtime safety assurance is a method that can ensure security attributes being satisfied during system operation. The simplex architecture-based runtime safety assurance methods are able to provide the safety guarantees even in the presence of unverified complex controllers. The basic idea is to combine the complex controller with a rigorously formalized safe controller, where the former operates in the absence of safety risk. Whenever a potential safety risk is detected, a decision module then completes the switching between the complex controller and the safe controller in order to guarantee the safety. This paper investigates the runtime safety assurance methods for cyber physical systems and summarizes the most relevant work from three aspects: the design of simplex decision-making module, the extension and improvement of simplex architecture, and the application of simplex architecture. This paper also provides an analysis of technical challenges and future directions of simplex architecture-based runtime safety assurance methods, indicating their potential and effectiveness in solving the runtime safety assurance issues in intelligent cyber-physical systems. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Impact of Probability Distribution Selection on RVFL Performance

Author

Cao, Weipeng, Gao, Jinzhu, Ming, Zhong, Cai, Shubin, Zheng, Hua, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Qiu, Meikang, editor

Published
2018
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