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Your search keyword '"Xiong, Neal"' showing total 721 results
Start Over Author "Xiong, Neal" Publication Year Range Last 10 years Publication Type Periodicals
721 results on '"Xiong, Neal"'

151. Research on Multifeature-Based Superposter Identification in Online Learning Forums

Author

Luo, Changri, Zhang, Xinhua, He, Tingting, Zhang, Yong, Xiong, Neal, and Lu, Zizhou

Subjects
Social networks -- Analysis, Online education -- Analysis, Transportation industry
Abstract

With the development of online learning and distance education, online learners' discussions in forums become increasingly effective to facilitate learning. Superposters, who play a more and more important role in forums, have attracted researchers' close attention. The key to the research is how to identify superposters among a large number of participants. Some studies focus on the network interaction of superposters and some content-related features but neglect the basic quality like language expression that a superposter should possess and the learning-related features like learning collaboration. Based on the analysis of online learning corpus, through network interaction and combination of the different features of N-gram, the paper proposed the superposter identification method based on the three primary features including language expression (L), content quality (C), and social network interaction (S) and the eight secondary features including learning collaboration. The paper applied the method in the real online learning forum corpus for identifying 28 preset superposters, achieving the results of P@15=1.0, Avg.P@15=1.0, P@28=0.86, and Avg.P@28=0.95. Experiments showed that this was an effective superposter identification method in online learning forums., Author(s): Changri Luo [1]; Xinhua Zhang [2]; Tingting He [3]; Yong Zhang (corresponding author) [3]; Neal Xiong [4]; Zizhou Lu [1] 1. Introduction With the improvement in online learning and [...]

Published
2021
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167. Analysis of Weight-Directed Functional Brain Networks in the Deception State Based on EEG Signal

Author

Wei, Sihong, Gao, Junfeng, Yang, Yong, Xiong, Neal, Zhang, Jiaqi, Song, Jian, Kang, Qianruo, Li, Yaqian, and Lv, Haoan

Abstract

Although analyzing the brain's functional and structural network has revealed that numerous brain networks are necessary to collaborate during deception, the directionality of these functional networks is still unknown. This study investigated the effective connectivity of the brain networks during deception and uncovers the information-interaction patterns of lying neural oscillations. The electroencephalography (EEG) data of 40 lying persons and 40 honest persons were used to create the weight- directed functional brain networks (WDFBN). Specifically, the connecting edge weight was defined based on the normalized phase transfer entropy (dPTE) between each electrode pair, where the network nodes involved 30 electrode channels. Additionally, the signal connectivity matrices were constructed in four frequency bands: delta, theta, alpha, and beta and were subjected to a difference analysis of entropy values between the groups. Statistical analysis of the classification results revealed that all frequency bands correctly detect deception and innocence with an accuracy of 92.83%, 94.17%, 85.93%, and 92.25%, respectively. Therefore, dPTE can be considered a valuable feature for identifying lying. According to WDFBN analysis, deception has stronger information flow in the frontoparietal, frontotemporal and temporoparietal networks compare to honest people. Furthermore, the prefrontal cortex was also found to be activated in all frequency ranges. This study examined the critical pathways of brain information interaction during deception, providing new insights into the underlying neural mechanisms. Our analysis offers significant evidence for the development of brain networks that could potentially be used for lie detection.

Published
2023
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168. An Intelligent Semi-Honest System for Secret Matching against Malicious Adversaries.

Author

Liu, Xin, Kong, Jianwei, Luo, Dan, Xiong, Neal, Xu, Gang, and Chen, Xiubo

Subjects
PATTERN matching, NATURAL language processing, QUESTION answering systems, DATABASES, INFORMATION retrieval, DEEP learning
Abstract

With natural language processing as an important research direction in deep learning, the problems of text similarity calculation, natural language inference, question and answer systems, and information retrieval can be regarded as text matching applications for different data and scenarios. Secure matching computation of text string patterns can solve the privacy protection problem in the fields of biological sequence analysis, keyword search, and database query. In this paper, we propose an Intelligent Semi-Honest System (ISHS) for secret matching against malicious adversaries. Firstly, a secure computation protocol based on the semi-honest model is designed for the secret matching of text strings, which adopts a new digital encoding method and an ECC encryption algorithm and can provide a solution for honest participants. The text string matching protocol under the malicious model which uses the cut-and-choose method and zero-knowledge proof is designed for resisting malicious behaviors that may be committed by malicious participants in the semi-honest protocol. The correctness and security of the protocol are analyzed, which is more efficient and has practical value compared with the existing algorithms. The secure text matching has important engineering applications. [ABSTRACT FROM AUTHOR]

Published
2023
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169. IRGA: An Intelligent Implicit Real-time Gait Authentication System in Heterogeneous Complex Scenarios.

Author

LI YANG, XI LI, ZHUORU MA, LU LI, XIONG, NEAL, and JIANFENG MA

Subjects
CONVOLUTIONAL neural networks, ARTIFICIAL neural networks
Abstract

Gait authentication as a technique that can continuously provide identity recognition on mobile devices for security has been investigated by academics in the community for decades. However, most of the existing work achieves insufficient generalization to complex real-world environments due to the complexity of the noisy real-world gait data. To address this limitation, we propose an intelligent Implicit Real-time Gait Authentication (IRGA) system based on Deep Neural Networks (DNNs) for enhancing the adaptability of gait authentication in practice. In the proposed system, the gait data (whether with complex interference signals) will first be processed sequentially by the imperceptible collection module and data preprocessing module for improving data quality. In order to illustrate and verify the suitability of our proposal, we provide analysis of the impact of individual gait changes on data feature distribution. Finally, a fusion neural network composed of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is designed to perform feature extraction and user authentication. We evaluate the proposed IRGA system in heterogeneous complex scenarios and present start-of-the-art comparisons on three datasets. Extensive experiments demonstrate that the IRGA system achieves improved performance simultaneously in several different metrics. [ABSTRACT FROM AUTHOR]

Published
2023
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