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144 results on '"Qu, Guangzhi"'

1. Edged Weisfeiler-Lehman Algorithm

Author

Yue, Xiao, Liu, Bo, Zhang, Feng, Qu, Guangzhi, 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, Wand, Michael, editor, Malinovská, Kristína, editor, Schmidhuber, Jürgen, editor, and Tetko, Igor V., editor

Published
2024
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4. Boundary U-Net: A Segmentation Method to Improve Salt Bodies Identification Accuracy

Author

Zhao, Yelong, Liu, Bo, Li, Jianqiang, Li, Yong, Chen, Hongli, Qu, Guangzhi, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Chang, Jia-Wei, editor, Yen, Neil, editor, and Hung, Jason C., editor

Published
2021
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5. Granger-Causality Mining in Atmospheric Visibility Based on Deep Learning

Author

Liu, Bo, He, Xi, Li, Jianqiang, Qu, Guangzhi, Lang, Jianlei, Gu, Rentao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Hung, Jason C., editor, Yen, Neil Y., editor, and Chang, Jia-Wei, editor

Published
2020
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6. Semi-automated Construction of Air Pollution Domain Ontology

Author

Liu, Bo, Zhang, Jiahui, Li, Jianqiang, Qu, Guangzhi, Li, Yong, Lang, Jianlei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Hung, Jason C., editor, Yen, Neil Y., editor, and Chang, Jia-Wei, editor

Published
2020
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11. North Atlantic Right Whale Contact Call Detection

Author

Abousleiman, Rami, Qu, Guangzhi, and Rawashdeh, Osamah

Subjects
Computer Science - Learning, Computer Science - Sound
Abstract

The North Atlantic right whale (Eubalaena glacialis) is an endangered species. These whales continuously suffer from deadly vessel impacts alongside the eastern coast of North America. There have been countless efforts to save the remaining 350 - 400 of them. One of the most prominent works is done by Marinexplore and Cornell University. A system of hydrophones linked to satellite connected-buoys has been deployed in the whales habitat. These hydrophones record and transmit live sounds to a base station. These recording might contain the right whale contact call as well as many other noises. The noise rate increases rapidly in vessel-busy areas such as by the Boston harbor. This paper presents and studies the problem of detecting the North Atlantic right whale contact call with the presence of noise and other marine life sounds. A novel algorithm was developed to preprocess the sound waves before a tree based hierarchical classifier is used to classify the data and provide a score. The developed model was trained with 30,000 data points made available through the Cornell University Whale Detection Challenge program. Results showed that the developed algorithm had close to 85% success rate in detecting the presence of the North Atlantic right whale., Comment: 6 pages, ICML 2013 Workshop on Machine Learning for Bioacoustics

Published
2013

12. H-Verifier: Verifying Confidential System State with Delegated Sandboxes

Author

Liu, Anyi, Qu, Guangzhi, 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, Liu, Feng, editor, Xu, Shouhuai, editor, and Yung, Moti, editor

Published
2018
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13. An XGBoost-Based Method for Improved Orbit Prediction With an Orbit-Separate Modeling Strategy

Author

Huang, Wenbin, Tang, Rui, Qu, Guangzhi, and Zhang, Feng

Abstract

This article introduces a novel approach to improving the accuracy of satellite orbit prediction (OP) by combining XGBoost with the extended Kalman filter (EKF). While EKF is a well-established method in orbit determination, it falls short when dealing with OP due to the absence of observation data during the prediction period. To the best of the authors' knowledge, this article is the first attempt to apply XGBoost, a leading ensemble learning method, to address this challenge. Furthermore, we adopt the orbit-separate strategy rather than the orbit-mixed strategy for prediction. This choice is based on the belief that the former outperforms the latter, given that it treats each orbit uniquely. This belief aligns with the understanding that each orbit follows its own physical laws, influenced by distinct perturbations, such as gravity, atmospheric drag, radiation pressure, and the gravitational pull of the Sun and Moon. The experimental results, conducted on five Starlink satellites, highlight the effectiveness of the XGBoost-based ensemble learning method when modeled with the orbit-separate strategy. It successfully captures the differences between predicted values from the physical model and true values simulated by the EKF. Notably, when compared with the state-of-the-art model employing an orbit-mixed strategy, the XGBoost model with an orbit-separate strategy demonstrates an overall improvement in prediction accuracy.

Published
2024
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16. Simple is Beautiful: An Online Collaborative Filtering Recommendation Solution with Higher Accuracy

Author

Zhang, Feng, Gong, Ti, Lee, Victor E., Zhao, Gansen, Qu, Guangzhi, 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, Cheng, Reynold, editor, Cui, Bin, editor, Zhang, Zhenjie, editor, Cai, Ruichu, editor, and Xu, Jia, editor

Published
2015
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20. Earth Observation Data Provenance: A Blockchain-Based Solution

Author

Zhang, Feng, Wang, Zihao, Guo, Ruixin, and Qu, Guangzhi

Abstract

Earth observation (EO) data provenance is vital for facilitating data sharing and cooperative processing. However, existing techniques for managing EO data provenance still have various weaknesses, including decentralization, traceability, transparency, tamper-proofing, and security protection. Despite being a transformative solution in various domains, the potential of blockchain technology in EO data provenance remains largely unexplored. This article introduces a blockchain-based solution for EO data provenance, aiming to facilitate data sharing and traceability. We have implemented a prototype based on the blockchain technology and conducted a performance evaluation. To the best of authors' knowledge, this is the first paper to explore the application of blockchain in the management of EO data provenance.

Published
2024
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24. Empirical Analysis of Local Round Trip Time for Wireless Traffic Differentiation

Author

Qu, Guangzhi, Nefcy, Michael M., Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Coulson, Geoffrey, Series editor, Zhang, Xi, editor, and Qiao, Daji, editor

Published
2012
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25. Security Analysis of Opportunistic Networks Using Complex Network Properties

Author

Mohan, Srikar, Qu, Guangzhi, Mili, Fatma, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Xinbing, editor, Zheng, Rong, editor, Jing, Tao, editor, and Xing, Kai, editor

Published
2012
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26. Modelling the Behaviour of Crowds in Panicked Conditions

Author

Wendt, Jake, Qu, Guangzhi, Niu, Jianwei, 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Zhong, Ning, editor, Callaghan, Vic, editor, Ghorbani, Ali A., editor, and Hu, Bin, editor

Published
2011
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27. Self-adjust Local Connectivity Analysis for Spectral Clustering

Author

Wu, Hui, Qu, Guangzhi, Zhu, Xingquan, 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Huang, Joshua Zhexue, editor, Cao, Longbing, editor, and Srivastava, Jaideep, editor

Published
2011
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33. Retyping of triple‐negative breast cancer based on clustering method.

Author

Liu, Bo, Li, Xingrui, Wang, Huina, Zhao, Shuangtao, Li, Jianqiang, Qu, Guangzhi, and Wang, Fei

Subjects
TRIPLE-negative breast cancer, BREAST cancer prognosis, BREAST cancer research, BREAST cancer
Abstract

Triple‐negative breast cancer is the worst prognosis in breast cancer, accounting for 10.0–20.8% of all breast cancers. Considering that triple‐negative breast cancer has great heterogeneity and very poor prognosis, clinical medication guidance is in urgent need of a more detailed classification of breast cancer itself. Although many researchers have been dedicated to the clustering of triple‐negative breast cancer and have found possible targets based on typing, their results are not closely related to the prognosis. This paper utilizes three clustering methods to retype the patient data with triple‐negative breast cancer, and the results show that the triple‐negative breast cancer data could be classified into two categories. Eight important genes and three important clinical factors related to the prognosis of two types of triple‐negative breast cancer have been obtained. These genes have the following three characteristics: co‐expression, differential expression and interaction. In terms of breast cancer control, the prognosis can be controlled as much as possible by regulating gene levels, which provides new directions and ideas for related research on breast cancer prognosis. [ABSTRACT FROM AUTHOR]

Published
2023
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40. A new dependency and correlation analysis for features

Author

Qu, Guangzhi, Hariri, Salim, and Yousif, Mazin

Subjects
Algorithms, Algorithm, Business, Computers, Electronics, Electronics and electrical industries
Abstract

The quality of the data being analyzed is a critical factor that affects the accuracy of data mining algorithms. There are two important aspects of the data quality, one is relevance and the other is data redundancy. The inclusion of irrelevant and redundant features in the data mining model results in poor predictions and high computational overhead. This paper presents an efficient method concerning both the relevance of the features and the pairwise features correlation in order to improve the prediction and accuracy of our data mining algorithm. We introduce a new feature correlation metric [Q.sub.Y]([X.sub.i], [X.sub.j]) and feature subset merit measure e(S) to quantify the relevance and the correlation among features with respect to a desired data mining task (e.g., detection of an abnormal behavior in a network service due to network attacks). Our approach takes into consideration not only the dependency among the features, but also their dependency with respect to a given data mining task. Our analysis shows that the correlation relationship among features depends on the decision task and, thus, they display different behaviors as we change the decision task. We applied our data mining approach to network security and validated it using the DARPA KDD99 benchmark data set. Our results show that, using the new decision dependent correlation metric, we can efficiently detect rare network attacks such as User to Root (U2R) and Remote to Local (R2L) attacks. The best reported detection rates for U2R and R2L on the KDD99 data sets were 13.2 percent and 8.4 percent with 0.5 percent false alarm, respectively. For U2R attacks, our approach can achieve a 92.5 percent detection rate with a false alarm of 0.7587 percent. For R2L attacks, our approach can achieve a 92.47 percent detection rate with a false alarm of 8.35 percent. Index Terms--Feature extraction, correlation measure.

Published
2005

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