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20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019

作者： Liao, Hao Liu, Qixin Vidmer, Alexandre Zhou, Mingyang Mao, Rui National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126166

In the past two decades, analyzing the information network has been intensively studied from various disciplines. Small world property and scale-free property prevail in network science research. The comparison and classification of different kinds of graphs are extremely important. However, how to design a robust and accurate classification with deep learning techniques for network property still lack enough attention, which is a vital task in various application scenarios. In this paper, we proposed the reliable network property classifier based on graph embedding(RNC) to classify the network property (scale free or small world property). In order to process non-euclidean data, we embedded each network into an image and use dimensional reduction, rasterization, and convolutional neural networks to complete the classification problem. The method can effectively accomplish classification tasks in not only artificial networks but also real networks. Besides, RNC wins greatly in terms of robustness on real networks, showing the robustness of RNC against the incomplete structure of the network. © 2019 IEEE.

关键词： Deep learning

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A lightweight scalable hybrid authentication framework for Internet of Medical Things (IoMT) using blockchain hyperledger consortium network with edge computing

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Scientific reports 2025年第1期15卷 19856页

作者： Abdullah Ayub Khan Asif Ali Laghari Roobaea Alroobaea Abdullah M Baqasah Majed Alsafyani Hamed Alsufyani Sajid Ullah Department of Computer Science Bahria University Karachi Campus Karachi 75260 Pakistan. abdullah.khan00763@***. Software Collage Shenyang Normal University Shenyang China. Department of Computer Science College of Computers and Information Technology Taif University P. O. Box 11099 21944 Taif Saudi Arabia. Department of Information Technology College of Computers and Information Technology Taif University 21974 Taif Saudi Arabia. Department of Computer Science College of Computing and Informatics Saudi Electronic University 11673 Riyadh Saudi Arabia. Department of Water Resources and Environmental Engineering Nangarhar University Jalalabad 2600 Nangarhar Afghanistan. sajidjalwan@***.

The Internet of Things (IoMT) has revolutionized the global landscape by enabling the hierarchy of interconnectivity between medical devices, sensors, and healthcare applications. Significant limitations in terms of scalability, privacy, and security are associated with this connection. This study presents a scalable, lightweight hybrid authentication system that integrates blockchain and edge computing within a Hyperledger Consortium network to address such real-time problems, particularly the use of Hyperledger Indy. For secure authentication, Hyperledger ensures a permissioned, decentralized, and impenetrable environment, while edge computing lowers latency by processing data closer to IoMT devices. The proposed framework balances security and computing performance by utilizing a hybrid cryptographic technique, like NuCypher Threshold Proxy Re-Encryption. Applicational activities are now appropriate for IoMT devices with limited resources thanks to this integration. By facilitating cooperation between numerous stakeholders with restricted access, the consortium network improves scalability and data governance. Comparing the proposed framework to the state-of-the-art techniques, experimental evaluation shows that it reduces latency by 2.93% and increases authentication efficiency by 98.33%. Therefore, in contrast to current solutions, guarantee data integrity and transparency between patients, consultants, and hospitals. The development of dependable, scalable, and secure IoMT applications is facilitated by this work, enabling next-generation medical applications.

关键词： Blockchain Cloud computing Edge computing Hyperledger technology Lightweight consensus Scalable hybrid authentication

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The Library for Hadoop Deflate Compression Based on FPGA Accelerator with Load Balance 20

The Library for Hadoop Deflate Compression Based on FPGA Acc...

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20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019

作者： Du, Haixin Zhang, Jiankui Sha, Shihao Ye, Cai Luo, Qiuming College of Computer Science and Software Engineering Shenzhen University NHPCC/Guangdong Key Laboratory of Popular HPC Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen China

ISBN: (纸本)9781728126166

Hadoop application will produce lots of intermediate results in the map/reduce process that requires disk I/O and network transmission. By compressing the large-scale data of intermediate result, it will greatly improve disk access efficiently and reduce program run time. Hardware-accelerated solutions have become more desirable. This paper design a multi-FPGA compression accelerator on the Hadoop platform, and the system performance analysis compared with a software-only solution that mainly uses CPU to processing. The testing programs are zpipe, TestDFSIO and Terasort. In contrast with the software-only solution. The max speedup of zpipe is 6.55X (single FPGA) and 10.24X (dual FPGA), the max speedup of TestDFSIO is 6.28X (single FPGA) and 6.28X (dual FPGA), and the max speedup of Terasort application is up to 3.25X(single FPGA) and 3.35X(dual FPGA). © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A two-stage algorithm for engagement detection in online learning

A two-stage algorithm for engagement detection in online lea...

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2019 International Conference on Sustainable Technologies for Industry 4.0, STI 2019

作者： Dash, Saswat Akber Dewan, M. Ali Murshed, Mahbub Lin, Fuhua Abdullah-Al-Wadud, M. Das, Animesh National Institute of Technology Department of Computer Science and Engineering Rourkela India School of Computing and Information Systems Athabasca University Edmonton Canada Software Development Bow Valley College Calgary Canada King Saud University Department of Software Engineering Riyadh Saudi Arabia

ISBN: (纸本)9781728160979

Online learning plays a key role in current education system. Engagement detection in online learning is crucial as the student's success in online courses heavily depends on his/her state of mind. In our previous work, we used facial expressions labeled as engaged and not-engaged for student's engagement detection. In this paper, we use student's behavioral (on-task and off-task) and emotional (satisfied, bored, and confused) information for engagement detection. Five different convolutional neural network models have been tested for the behavioral and the emotional dimensions detection to detect student's engagement in online learning. The models are All Convolutional Network, Network in Network, Very Deep Convolutional Network, Conv-Pool Convolutional Network, and a proposed model combing some special features from the above models. We used the dataset Dataset for the Affective States in E-Environments - for the performance evaluation. Experimental results show that the behavioral and emotional dimensions based engagement detection provides a high accuracy. © 2019 IEEE.

关键词： Neural network models

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Stable Gaussian process tracking control of antagonistic variable stiffness actuators

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Scientific reports 2025年第1期15卷 19762页

作者： Zhigang Zou Wei Li Chenguang Yang Yongping Pan College of Mechatronics Engineering Guangdong Polytechnic Normal University Guangzhou 510665 China. School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 China. School of Software Engineering Sun Yat-sen University Guangzhou 510006 China. Department of Computer Science University of Liverpool L69 3BX Liverpool UK. Peng Cheng Laboratory Shenzhen 518057 China. panyp@***. School of Mathematical Sciences Center for Applied Mathematics of Guangxi Guangxi Minzu University Nanning 530006 China. panyp@***.

The control of variable stiffness actuators (VSAs) is challenging because they have highly nonlinear characteristics and are difficult to model accurately. Classical control approaches using high control gains can make VSAs stiff, which alters the inherent compliance of VSAs. Iterative learning control can achieve high tracking accuracy for VSAs but generally lacks sufficient generalization. This study applies Gaussian process (GP) regression to design a stable tracking controller combining feedforward and low-gain feedback control actions for agonistic-antagonistic (AA) VSAs subjected to unknown dynamics. The GP model learns the inverse dynamics of AA-VSAs and provides model fidelity by the predicted variance. The stability analysis of the closed-loop system demonstrates that the tracking error is uniformly ultimately bounded and exponentially converges to a small ball under a given probability. Experiments on an AA-VSA named qbmove Advanced have validated the superiority of the proposed method with respect to tracking accuracy and generalization.

关键词： Compliant actuation Gaussian process Machine learning for control Motion control Variable stiffness actuator

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Correction: Highly Accurate Gear Fault Diagnosis Based on Support Vector Machine

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Journal of Vibration engineering & Technologies 2022年第3期11卷 1389-1389页

作者： Abdul, Zrar Kh. Al-Talabani, Abdulbasit K. Department of Information System Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Applied Computer Science College of Medicals and Applied Sciences Charmo University Chamchamal Sulaymaniyah Iraq Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region Iraq

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Predictive Analytics Model Based on Multiclass Classification for Asthma Severity by Using Random Forest Algorithm

Predictive Analytics Model Based on Multiclass Classificatio...

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International Conference on Electrical, Communication, and computer engineering (ICECCE)

作者： Wasif Akbar Wei-Ping Wu Muhammad Faheem Sehrish Saleem Arslan Javed Muhammad Asim Saleem School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China National College of Business Administration & Economics Lahore Pakistan MNS University of Engineering and Technology Multan Multan Pakistan Faculty of Electronic Information and Electrical Engineering School of Computer Science Dalian University of Technology Liaoning China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728171166

ISBN: (纸本)9781728171173

In modern life, health status prediction has become very crucial. Big data analysis plays a vital role to predict this perfectly. Asthma is a severe chronic disease with severe symptoms. Asthma disease is a chronic disease that leads to death. Researchers have focused on this for better decision making to predict the disease timely use of predictive analysis. This study proposes a Predictive Analytic Model for Asthma prediction using Random Forest (PAM-RF). Data of patients suffering from Asthma has been trained by a random forest approach which predicts to classify the data. Experiments are performed on Hadoop-spark which predicts the future health state of patients. The proposed approach has attained an accuracy of 98.80 percent to predict the asthma disease.

关键词： Big Data Diseases Forestry Predictive models Respiratory system

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High-Resolution Natural Image Matting by Refining Low-resolution Alpha Mattes

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Xianmin Ye Yihui Liang Mian Tan Fujian Feng Lin Wang Han Huang College of Data Science and Information Engineering Guizhou Minzu University Guiyang China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Key Laboratory of Pattern Recognition and Intelligent System and the College of Data Science and Information Engineering Guizhou Minzu University Guiyang China School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot (SCUT) MOE of China Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology Guangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

High-resolution natural image matting plays an important role in image editing, film-making and remote sensing due to its ability of accurately extract the foreground from a natural background. However, due to the complexity brought about by the proliferation of resolution, the existing image matting methods cannot obtain high-quality alpha mattes on high-resolution images in reasonable time. To overcome this challenge, we introduce a high-resolution image matting framework based on alpha matte refinement from low-resolution to high-resolution (HRIMF-AMR). The proposed framework transforms the complex high-resolution image matting problem into low-resolution image matting problem and high-resolution alpha matte refinement problem. While the first problem is solved by adopting an existing image matting method, the latter is addressed by applying the Detail Difference Feature Extractor (DDFE) designed as a part of our work. The DDFE extracts detail difference features from high-resolution images by measuring the image feature difference between high-resolution images and low-resolution images. The low-resolution alpha matte is refined according to the extracted detail difference feature, providing the high-resolution alpha matte. In addition, the Matte Detail Resolution Difference (MDRD) loss is introduced to train the DDFE, which imposes an additional constraint on the extraction of detail difference features with mattes. Experimental results show that integrating HRIMF-AMR significantly enhances the performance of existing matting methods on high-resolution images of Transparent-460 and Alphamatting. Project page: https://***/yexianmin/HRAMR-Matting.

关键词： Feature extraction Image resolution Image color analysis Optimization Remote sensing Clustering algorithms Accuracy Visualization Transforms Training

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XLCS: A new bit-parallel longest common subsequence algorithm on xeon phi clusters 21

XLCS: A new bit-parallel longest common subsequence algorith...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Yin, Zekun Zhang, Hao Xu, Kai Chan, Yuandong Peng, Shaoliang Liu, Weiguo Schmidt, Bertil School of Software Shenzhen Research Institute Shandong University Jinan China School of Software Shandong University Jinan China National Supercomputing Center Wuxi China College of Computer Science and Electronic Engineering National Supercomputing Centre in Changsha Hunan University School of Computer Science National University of Defense Technology Changsha China Institute for Computer Science Johannes Gutenberg University Mainz Germany

ISBN: (纸本)9781728120584

Finding the longest common subsequence (LCS) of two strings is a classical problem in bioinformatics. A basic approach to solve this problem is based on dynamic programming. As the biological sequence databases are growing continuously, bit-parallel sequence comparison algorithms are becoming increasingly important. In this paper, we present XLCS, a new parallel implementation to accelerate the LCS algorithm on Xeon Phi clusters by performing bit-wise operations. We have designed an asynchronous IO framework to improve the data transfer efficiency. To make full use of the computing resources of Xeon Phi clusters, we use three levels of parallelism: node-level, thread-level and vector-level. We also propose a segmentation-method to decrease cache misses. Our performance evaluation shows that XLCS achieves a peak performance of 3.61 TCUPS on a single 31S1P card and 8.20 TCUPS on a single 7210 card. Compared to the well-parallelized LCS algorithm OCS implemented on three M2090 cards, XLCS achieves a speedup of 3.6 on a KNC and 8.1 on a KNL, respectively. XLCS further achieves 93.84 TCUPS running 16 compute nodes of the Tianhe-2 supercomputer and 312.30 TCUPS with 32 compute nodes of a KNL cluster. To our knowledge, this is the first reported implementation of the bit-parallel LCS algorithm on Xeon Phi clusters. XLCS is available at https://***/wxiaoning/Longest-Common-Subsequence. © 2019 IEEE.

关键词： Data transfer

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Cache location selected algorithm for information-centric networking

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Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology 2019年第1期41卷 152-160页

作者： Wang, Xingwei Wang, Zijian Li, Fuliang Huang, Min College of Software Northeastern University Shenyang110004 China School of Computer Science and Engineering Northeastern University Shenyang110004 China College of Information Science and Engineering Northeastern University Shenyang110004 China

The cache ubiquity feature in ICN (information-centric networking) causes many problems such as higher data duplication rate, underutilization of cache space, etc. To solve such problems, the benefits of multiple network roles were considered. From the views of user-service provider and network operator, two single-objective optimization models were established respectively, aiming at minimizing the network traffic and the network expense, which were merged into the Pareto model. The proposed cache location selected algorithm was described based on the mathematical programming method of Pareto. Simulation results show that the proposed algorithm outperforms the default cache mechanism of ICN in terms of traffic cost-effective. In terms of network expense, the proposed algorithm is more applicable to ICN when there are a few popular contents. However, when all contents are popular, the default mechanism of "cache everywhere" in ICN is more applicable. © 2019, NUDT Press. All right reserved.

关键词： Mathematical programming

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