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269 results on '"Tu, Shanshan"'

3. Facial Expression Recognition from Occluded Images Using Deep Convolution Neural Network with Vision Transformer

Author

Li, Mingxiu, Tu, Shanshan, Rehman, Sadaqat ur, 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, Lu, Huchuan, editor, Ouyang, Wanli, editor, Huang, Hui, editor, Lu, Jiwen, editor, Liu, Risheng, editor, Dong, Jing, editor, and Xu, Min, editor

Published
2023
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4. Deep Learning Techniques for Future Intelligent Cross-Media Retrieval

Author

Rehman, Sadaqat ur, Waqas, Muhammad, Tu, Shanshan, Koubaa, Anis, Rehman, Obaid ur, Ahmad, Jawad, Hanif, Muhammad, and Han, Zhu

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

With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention. It plays a significant role in big data applications and consists in searching and finding data from different types of media. In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation. These challenges are evaluated on deep learning (DL) based methods, which are categorized into four main groups: 1) unsupervised methods, 2) supervised methods, 3) pairwise based methods, and 4) rank based methods. Then, we present some well-known cross-media datasets used for retrieval, considering the importance of these datasets in the context in of deep learning based cross-media retrieval approaches. Moreover, we also present an extensive review of the state-of-the-art problems and its corresponding solutions for encouraging deep learning in cross-media retrieval. The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems and the potential solutions of deep learning assisted cross-media retrieval. To the best of our knowledge, this is the first comprehensive survey to address cross-media retrieval under deep learning methods., Comment: arXiv admin note: text overlap with arXiv:1804.09539 by other authors

Published
2020

8. Water Preservation in Soan River Basin using Deep Learning Techniques

Author

Rehman, Sadaqat ur, Yang, Zhongliang, Shahid, Muhammad, Wei, Nan, Huang, Yongfeng, Waqas, Muhammad, Tu, Shanshan, and Rehman, Obaid ur

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Water supplies are crucial for the development of living beings. However, change in the hydrological process i.e. climate and land usage are the key issues. Sustaining water level and accurate estimating for dynamic conditions is a critical job for hydrologists, but predicting hydrological extremes is an open issue. In this paper, we proposed two deep learning techniques and three machine learning algorithms to predict stream flow, given the present climate conditions. The results showed that the Recurrent Neural Network (RNN) or Long Short-term Memory (LSTM), an artificial neural network based method, outperform other conventional and machine-learning algorithms for predicting stream flow. Furthermore, we analyzed that stream flow is directly affected by precipitation, land usage, and temperature. These indexes are critical, which can be used by hydrologists to identify the potential for stream flow. We make the dataset publicly available (https://github.com/sadaqat007/Dataset) so that others should be able to replicate and build upon the results published., Comment: 14 pages

Published
2019

9. Cross Validation for Penalized Quantile Regression with a Case-Weight Adjusted Solution Path

Author

Tu, Shanshan, Zhu, Yunzhang, and Lee, Yoonkyung

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

Cross validation is widely used for selecting tuning parameters in regularization methods, but it is computationally intensive in general. To lessen its computational burden, approximation schemes such as generalized approximate cross validation (GACV) are often employed. However, such approximations may not work well when non-smooth loss functions are involved. As a case in point, approximate cross validation schemes for penalized quantile regression do not work well for extreme quantiles. In this paper, we propose a new algorithm to compute the leave-one-out cross validation scores exactly for quantile regression with ridge penalty through a case-weight adjusted solution path. Resorting to the homotopy technique in optimization, we introduce a case weight for each individual data point as a continuous embedding parameter and decrease the weight gradually from one to zero to link the estimators based on the full data and those with a case deleted. This allows us to design a solution path algorithm to compute all leave-one-out estimators very efficiently from the full-data solution. We show that the case-weight adjusted solution path is piecewise linear in the weight parameter, and using the solution path, we examine case influences comprehensively and observe that different modes of case influences emerge, depending on the specified quantiles, data dimensions and penalty parameter., Comment: 54 pages, 10 figures

Published
2019

10. A Spatial-Temporal Integration Analysis to Classify Dynamic Functional Connectivity for Brain Disease Diagnosis

Author

Xu, Gaoxu, Liang, Yin, Tu, Shanshan, ur Rehman, Sadaqat, 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, Sun, Xingming, editor, Zhang, Xiaorui, editor, and Xia, Zhihua, editor

Published
2022
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11. Blockchain Enable IoT Using Deep Reinforcement Learning: A Novel Architecture to Ensure Security of Data Sharing and Storage

Author

Bai, Xuetao, Tu, Shanshan, Waqas, Muhammad, Wu, Aiming, Zhang, Yihe, Yang, Yongjie, 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, Sun, Xingming, editor, Zhang, Xiaorui, editor, and Xia, Zhihua, editor

Published
2022
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18. Recent Development, Trends and Challenges in IoT Security

Author

Talezari, Morteza, Tu, Shanshan, Rehman, Sadaqat ur, Chuangbai, Xiao, Ahmad, Basharat, Waqas, Muhammad, Rehman, Obaid ur, 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, Sun, Xingming, editor, Zhang, Xiaorui, editor, and Xia, Zhihua, editor

Published
2021
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19. Deep Learning Models for Intelligent Healthcare: Implementation and Challenges

Author

Rehman, Sadaqat ur, Tu, Shanshan, Shah, Zubair, Ahmad, Jawad, Waqas, Muhammad, Rehman, Obaid ur, Kouba, Anis, Abbasi, Qammer H., 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, Sun, Xingming, editor, Zhang, Xiaorui, editor, and Xia, Zhihua, editor

Published
2021
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27. Intelligent Contract for Power Market Transaction Based on the BlockChain

Author

Zhang, Yutong, Meng, Yuan, Tu, Shanshan, Waqas, Muhammad, Rehman, Sadaqat Ur, Zhao, Wei, Abbas, Ghulam, Abbas, Ziaul Haq, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Xingming, editor, Wang, Jinwei, editor, and Bertino, Elisa, editor

Published
2020
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28. An Efficient Approach for Selecting a Secure Relay Node in Fog Computing Social Ties Network

Author

Yin, Mingxi, Yu, Jinliang, Tu, Shanshan, Waqas, Muhammad, Rehman, Sadaqat Ur, Abbas, Ghulam, Abbas, Ziaul Haq, 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, Sun, Xingming, editor, and Wang, Jinwei, editor

Published
2020
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31. USAF-IoD: Ultralightweight and Secure Authenticated Key Agreement Framework for Internet of Drones Environment

Author

Badshah, Akhtar, Abbas, Ghulam, Waqas, Muhammad, Tu, Shanshan, Abbas, Ziaul Haq, Muhammad, Fazal, and Chen, Sheng

Abstract

The use of Internet of Drones (IoD) technology has surged across various domains such as logistics, surveying, industrial inspections, emergency response, security, infrastructure monitoring, crop management, and more. However, real-time communication with drones or Unmanned Aerial Vehicles (UAVs) in the IoD environment occurs over an insecure open channel, making it susceptible to various security and privacy vulnerabilities, including unauthorized access, data interception, denial of service attacks, and privacy concerns. Due to their unique characteristics, including long transmission distances, unstable communication environments, resource limitations, and the highly dynamic nature of UAVs, ensuring the security and privacy of IoD systems is of paramount importance for the success of IoD-based applications. Furthermore, drones are resource-constrained devices, and employing expensive security solutions is impractical, as it would significantly reduce the operational capacity of drones. In this paper, we present the design of an ultralightweight, secure, and robust user-authenticated key agreement framework for the IoD environment, named USAF-IoD. The proposed USAF-IoD is developed by incorporating authenticated encryption (ASCON), cryptographic hashing, XOR operations, and the use of physical unclonable functions (PUFs). PUFs are employed to enhance resistance against physical tampering attacks. The security analysis reveals that the proposed USAF-IoD meets the essential security requirements of the IoD environment. The comparative analysis further highlights the effectiveness of the proposed USAF-IoD, notably excelling in terms of security and functionality characteristics when compared to existing benchmark schemes, and showcasing competitive performance in computation, communication, and energy overheads.

Published
2024
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33. Intelligent model for the detection and classification of encrypted network traffic in cloud infrastructure.

Author

Dawood, Muhammad, Xiao, Chunagbai, Tu, Shanshan, Alotaibi, Faiz Abdullah, Alnfiai, Mrim M., and Farhan, Muhammad

Subjects
COMPUTER network traffic, COMMUNICATION infrastructure, INTERNET domain naming system, SECURE Sockets Layer (Computer network protocol), RADIAL basis functions, INTELLIGENT transportation systems, COMPUTER networks
Abstract

This article explores detecting and categorizing network traffic data using machine-learning (ML) methods, specifically focusing on the Domain Name Server (DNS) protocol. DNS has long been susceptible to various security flaws, frequently exploited over time, making DNS abuse a major concern in cybersecurity. Despite advanced attack, tactics employed by attackers to steal data in real-time, ensuring security and privacy for DNS queries and answers remains challenging. The evolving landscape of internet services has allowed attackers to launch cyber-attacks on computer networks. However, implementing Secure Socket Layer (SSL)-encrypted Hyper Text Transfer Protocol (HTTP) transmission, known as HTTPS, has significantly reduced DNS-based assaults. To further enhance security and mitigate threats like man-in-the-middle attacks, the security community has developed the concept of DNS over HTTPS (DoH). DoH aims to combat the eavesdropping and tampering of DNS data during communication. This study employs a ML-based classification approach on a dataset for traffic analysis. The AdaBoost model effectively classified Malicious and Non-DoH traffic, with accuracies of 75% and 73% for DoH traffic. The support vector classification model with a Radial Basis Function (SVC-RBF) achieved a 76% accuracy in classifying between malicious and non-DoH traffic. The quadratic discriminant analysis (QDA) model achieved 99% accuracy in classifying malicious traffic and 98% in classifying non-DoH traffic. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Learning a Semantic Space for Modeling Images, Tags and Feelings in Cross-Media Search

Author

ur Rehman, Sadaqat, Huang, Yongfeng, Tu, Shanshan, Ahmad, Basharat, 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, U., Leong Hou, editor, and Lauw, Hady W., editor

Published
2019
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44. An Abnormal Behavior Clustering Algorithm Based on K-means

Author

Zhang, Jianbiao, Yang, Fan, Tu, Shanshan, Zhang, Ai, 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, Ren, Jinchang, editor, Hussain, Amir, editor, Zheng, Jiangbin, editor, Liu, Cheng-Lin, editor, Luo, Bin, editor, Zhao, Huimin, editor, and Zhao, Xinbo, editor

Published
2018
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45. Facebook5k: A Novel Evaluation Resource Dataset for Cross-Media Search

Author

ur Rehman, Sadaqat, Huang, Yongfeng, Tu, Shanshan, ur Rehman, Obaid, 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, Sun, Xingming, editor, Pan, Zhaoqing, editor, and Bertino, Elisa, editor

Published
2018
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50. A Many-Objective Ensemble Optimization Algorithm for the Edge Cloud Resource Scheduling Problem

Author

Zhang, Jiangjiang, Ning, Zhenhu, Ali, Raja Hashim, Waqas, Muhammad, Tu, Shanshan, and Ahmad, Iftekhar

Abstract

An edge cloud architecture plays a key role in improving the user task computing service system by combining the powerful data processing capability of cloud centres with the low latency of edge computing. Existing methods for maximizing the efficiency of an edge cloud architecture take into account time and task parameters but ignore other factors such as load balancing, cost, and user satisfaction when scheduling resources. In this work, we propose a many-objective resource scheduling model for optimizing the performance of an edge cloud architecture, which takes into account the time spent on task, cost, load balance, user satisfaction, and trust measurement. The resource scheduling model converges to the optimal solution using a novel many-objective ensemble optimization algorithm based on a dynamic selection mechanism. The study also explores the support set convergence of eight evolutionary operators using the ensemble algorithm. The model solutions are dynamically updated with the help of the dynamic integration probability, and then a selection criteria is used to pick the best solutions from the pool of generated solutions. Two simulations on a benchmark dataset are used to verify the usefulness and performance of the designed algorithm. Our approach was able to locate more than half of the best solutions on the benchmark functions, and it also showed to be a better model solution than the some of the popular many-objective algorithms for dealing with the edge cloud resource scheduling problem, according to the results obtained from the simulations.

Published
2024
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