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150 results on '"Zhao, Yinliang"'

3. Knowledge Graph Completion with Information Adaptation and Refinement

Author

Liu, Yifan, Shang, Bin, Wang, Chenxin, Zhao, Yinliang, 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, Yang, Xiaochun, editor, Suhartanto, Heru, editor, Wang, Guoren, editor, Wang, Bin, editor, Jiang, Jing, editor, Li, Bing, editor, Zhu, Huaijie, editor, and Cui, Ningning, editor

Published
2023
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6. Joint Activity Recognition and Indoor Localization with WiFi Fingerprints

Author

Wang, Fei, Feng, Jianwei, Zhao, Yinliang, Zhang, Xiaobin, Zhang, Shiyuan, and Han, Jinsong

Subjects
Computer Science - Human-Computer Interaction
Abstract

Recent years have witnessed the rapid development in the research topic of WiFi sensing that automatically senses human with commercial WiFi devices. This work falls into two major categories, i.e., the activity recognition and the indoor localization. The former work utilizes WiFi devices to recognize human daily activities such as smoking, walking, and dancing. The latter one, indoor localization, can be used for indoor navigation, location-based services, and through-wall surveillance. The key rationale behind this type of work is that people behaviors can influence the WiFi signal propagation and introduce specific patterns into WiFi signals, called WiFi fingerprints, which can be further explored to identify human activities and locations. In this paper, we propose a novel deep learning framework for joint activity recognition and indoor localization task using WiFi Channel State Information~(CSI) fingerprints. More precisely, we develop a system running standard IEEE 802.11n WiFi protocol, and collect more than 1400 CSI fingerprints on 6 activities at 16 indoor locations. Then we propose a dual-task convolutional neural network with 1-dimensional convolutional layers for the joint task of activity recognition and indoor localization. Experimental results and ablation study show that our approach achieves good performances in this joint WiFi sensing task. Data and code have been made publicly available at https://github.com/geekfeiw/apl., Comment: accepted by IEEE Access; 10 pages, 15 figures, 4 tables

Published
2019
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15. Similar Samples Cleaning in Speculative Multithreading

Author

Li, Yuxiang, Zhao, Yinliang, Liu, Bin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, 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, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sun, Xian-he, editor, Qu, Wenyu, editor, Stojmenovic, Ivan, editor, Zhou, Wanlei, editor, Li, Zhiyang, editor, Guo, Hua, editor, Min, Geyong, editor, Yang, Tingting, editor, Wu, Yulei, editor, and Liu, Lei, editor

Published
2014
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16. Dynamically Spawning Speculative Threads to Improve Speculative Path Execution

Author

Li, Meirong, Zhao, Yinliang, Tao, You, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, 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, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sun, Xian-he, editor, Qu, Wenyu, editor, Stojmenovic, Ivan, editor, Zhou, Wanlei, editor, Li, Zhiyang, editor, Guo, Hua, editor, Min, Geyong, editor, Yang, Tingting, editor, Wu, Yulei, editor, and Liu, Lei, editor

Published
2014
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20. Least Squares Support Vector Machine on Gaussian Wavelet Kernel Function Set

Author

Wu, Fangfang, Zhao, Yinliang, 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, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Jun, editor, Yi, Zhang, editor, Zurada, Jacek M., editor, Lu, Bao-Liang, editor, and Yin, Hujun, editor

Published
2006
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21. The Minimization of QoS Deviation in Grid Environment

Author

Zhang, YongZhong, Zhao, Yinliang, Wu, FangFang, Li, ZengZhi, 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, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zhuge, Hai, editor, and Fox, Geoffrey C., editor

Published
2005
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22. A Novel Parallel Reduced Support Vector Machine

Author

Wu, Fangfang, Zhao, Yinliang, Jiang, Zefei, 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, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Lipo, editor, Chen, Ke, editor, and Ong, Yew Soon, editor

Published
2005
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23. Least Squares Littlewood-Paley Wavelet Support Vector Machine

Author

Wu, Fangfang, Zhao, Yinliang, 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, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gelbukh, Alexander, editor, de Albornoz, Álvaro, editor, and Terashima-Marín, Hugo, editor

Published
2005
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31. Performance prediction for distributed graph computing.

Author

Ji, Shuo, Zhao, Yinliang, and Li, Yuxiang

Subjects
DISTRIBUTED computing, GRAPH algorithms, ARTIFICIAL neural networks, COMPLETE graphs, DISTRIBUTED algorithms, PREDICTION models, FORECASTING
Abstract

Summary: Performance prediction for executing graph applications on distributed systems is a prerequisite to improve system performance. Especially for distributed systems optimized by sacrificing the accuracy of results to improve runtime performance, performance prediction can be used to determine accuracy‐related system parameters to achieve a tradeoff between the runtime and inaccuracy of results. This article presents an approach that predicts the runtime and inaccuracy of executing graph algorithms on bulk synchronous parallel (BSP)‐based systems to optimize system parameters by using an artificial neural network (ANN). The proposed approach samples different scales of subgraphs from the input graph by maintaining that the features of subgraphs are similar to the features of the input graph. Then it executes graph algorithm on each subgraph and extracts their runtime features. An ANN‐based performance prediction model is trained off‐line based on the extracted features and is used to predict the performance of executing graph algorithm on the complete input graph. We have validated the proposed approach by conducting single‐source shortest path, connected component, and PageRank on the BSP‐based distributed systems. The experimental results demonstrate that the prediction method can effectively predict the runtime and the inaccuracy with a relative error rate under 14% and under 25%, respectively, compared with the actual performance results. [ABSTRACT FROM AUTHOR]

Published
2022
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38. An Object Proxy-Based Dynamic Layer Replacement to Protect IoMT Applications

Author

Zhu Chang-Peng, Bo Han, and Zhao Yinliang

Subjects
Java, Article Subject, Computer Networks and Communications, Computer science, business.industry, Distributed computing, 020206 networking & telecommunications, 020207 software engineering, 02 engineering and technology, computer.software_genre, Virtual machine, lcsh:Technology (General), 0202 electrical engineering, electronic engineering, information engineering, Business logic, lcsh:T1-995, The Internet, business, lcsh:Science (General), computer, Information Systems, computer.programming_language, Heap (data structure), lcsh:Q1-390
Abstract

The Internet of medical things (IoMT) has become a promising paradigm, where the invaluable additional data can be collected by the ordinary medical devices when connecting to the Internet. The deep understanding of symptoms and trends can be provided to patients to manage their lives and treatments. However, due to the diversity of medical devices in IoMT, the codes of healthcare applications may be manipulated and tangled by malicious devices. In addition, the linguistic structures for layer activation in languages cause controls of layer activation to be part of program’s business logic, which hinders the dynamic replacement of layers. Therefore, to solve the above critical problems in IoMT, in this paper, a new approach is firstly proposed to support the dynamic replacement of layer in IoMT applications by incorporating object proxy into virtual machine (VM). Secondly, the heap and address are used to model the object and object evolution to guarantee the feasibility of the approach. After that, we analyze the influences of field access and method invocation and evaluate the risk and safety of the application when these constraints are satisfied. Finally, we conduct the evaluations by extending Java VM to validate the effectiveness of the proposal.

Published
2019
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48. User identification based on multiple attribute decision making in social networks

Author

Enjie Liu, Dong Lili, Zhao Yinliang, Ye Na, Gordon Clapworthy, and Bian Genqing

Subjects
User information, User profile, Information retrieval, Computer Networks and Communications, Computer science, business.industry, User modeling, Computer user satisfaction, computer.software_genre, Personalization, Weighting, Web application, Data mining, Electrical and Electronic Engineering, business, computer, Personally identifiable information
Abstract

Social networks are becoming increasingly popular and influential, and users are frequently registered on multiple networks simultaneously, in many cases leaving large quantities of personal information on each network. There is also a trend towards the personalization of web applications; to do this, the applications need to acquire information about the particular user. To maximise the use of the various sets of user information distributed on the web, this paper proposes a method to support the reuse and sharing of user profiles by different applications, and is based on user profile integration. To realize this goal, the initial task is user identification, and this forms the focus of the current paper. A new user identification method based on Multiple Attribute Decision Making (MADM) is described in which a subjective weight-directed objective weighting, which is obtained from the Similarity Weight method, is proposed to determine the relative weights of the common properties. Attribute Synthetic Evaluation is used to determine the equivalence of users. Experimental results show that the method is both feasible and effective despite the incompleteness of the candidate user dataset.

Published
2013

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