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IEEE International Conference on Data Mining (ICDM)

作者： Shiwei Tong Qi Liu Wei Huang Zhenya Hunag Enhong Chen Chuanren Liu Haiping Ma Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science & School of Computer Science and Technology University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science &School of Computer Science and Technology University of Science and Technology of China CN Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science &School of Computer Science and Technology University of Science and Technology of China Business Analytics and Statistics University of Tennessee TN Business Analytics and Statistics University of Tennessee Anhui University CN iFLYTEK Research iFLYTEK CO. LTD CN State Key Laboratory of Cognitive Intelligence iFLYTEK Research iFLYTEK CO. LTD.

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Knowledge Tracing (KT) is a fundamental but challenging task in online education that traces learners' evolving knowledge states. Much attention has been drawn to this area and several works such as Bayesian and Deep Knowledge Tracing have been proposed. Recent works have explored the value of relations among concepts and proposed to introduce knowledge structure into KT tasks. However, the propagated influence among concepts, which has been shown to be a key factor in human learning by the educational theories, is still under-explored. In this paper, we propose a new framework called Structure-based Knowledge Tracing (SKT), which exploits the multiple relations in knowledge structure to model the influence propagation among concepts. In the SKT framework, we consider both the temporal effect on the exercising sequence and the spatial effect on the knowledge structure. We take advantages of two novel formulations in modeling the influence propagation on the knowledge structure with multiple relations. For undirected relations such as similarity relations, the synchronization propagation method is adopted, where the influence propagates bidirectionally between neighbor concepts. For directed relations such as prerequisite relations, the partial propagation method is applied, where the influence can only unidirectionally propagate from a predecessor to a successor. Meanwhile, we employ the gated functions to update the states of concepts temporally and spatially. We demonstrate the effectiveness and interpretability of SKT with extensive experiments.

关键词： Neural networks Education Logic gates Synchronization Mathematical model Data mining Task analysis

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RETRACTED ARTICLE: Feature extraction of EEG images by using soft computing methods

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Soft Computing 2023年第2期28卷 665-665页

作者： Zeng, Xianfeng Huang, Changjiang Lai, Yiqiang School of Computer Science Guangdong University of Foreign Studies South China Business College Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China College of Computer Science Guangzhou College of Applied Science and Technology Guangzhou China

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Adaptive feature selection with weighted fuzzy rough sets for noisy data

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Fuzzy Sets and Systems 2025年 518卷

作者： Xiaoling Yang Hongmei Chen Tianrui Li Shan Feng Jihong Wan Yiyu Yao School of Mathematical Sciences Sichuan Normal University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science University of Regina Regina Canada National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University China

The existence of noise in data may decrease the performance of the classification model. An intuitive assumption suggests that samples with a higher local density have a greater probability of being correctly classified. Moreover, there are differences in the importance of features. It should further increase the influence of important features on the classification task. Motivated by these issues, we propose a sample-driven adaptive feature selection method. It aims to the impact of noise and obtain the adaptive importance of features. In this paper, the Weighted Fuzzy Rough Sets model (WFRS) is proposed by utilizing more reliable samples to address noisy data. Firstly, the approximation margin is derived by applying the WFRS model within the original feature space from the sample's perspective. It serves as a basis for deriving the feature weights. On this basis, the weighted feature space is obtained. This space compresses the features with the lower weight and stretches the features with higher weight. Then, we obtain a specific fuzzy relation to maximize the within-class samples and minimize the between-class samples in the weighted feature space. Further, an evaluation function of features is constructed to model the uncertainty of fuzzy positive and non-negative regions. A Sample-driven adaptive Feature selection algorithm based on the Weighted fuzzy rough sets model (SFW) is designed to capture the varying importance of features by considering the local density of samples. The weighted features are subsequently selected to facilitate the downstream classification tasks. Experimental results show the WFRS model's robustness, the effectiveness of SFW, and its superiority compared to alternative methods.

关键词： Uncertainty analysis

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Sequential reinforced 360-degree video adaptive streaming with cross-user attentive network

arXiv

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arXiv 2020年

作者： Fu, Jun Chen, Zhibo Chen, Xiaoming Li, Weiping CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China School of Computer Science and Engineering Beijing Technology and Business University China

In the tile-based 360-degree video streaming, predicting user’s future viewpoints and developing adaptive bitrate (ABR) algorithms are essential for optimizing user’s quality of experience (QoE). Traditional single-user based viewpoint prediction methods fail to achieve good performance in long-term prediction, and the recently proposed reinforcement learning (RL) based ABR schemes applied in traditional video streaming can not be directly applied in the tile-based 360-degree video streaming due to the exponential action space. Therefore, we propose a sequential reinforced 360-degree video streaming scheme with cross-user attentive network. Firstly, considering different users may have the similar viewing preference on the same video, we propose a cross-user attentive network (CUAN), boosting the performance of long-term viewpoint prediction by selectively utilizing cross-user information. Secondly, we propose a sequential RL-based (360SRL) ABR approach, transforming action space size of each decision step from exponential to linear via introducing a sequential decision structure. We evaluate the proposed CUAN and 360SRL using trace-driven experiments and experimental results demonstrate that CUAN and 360SRL outperform existing viewpoint prediction and ABR approaches with a noticeable margin. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction

arXiv

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arXiv 2022年

作者： Dang, Yuchen Chen, Ziqi Li, Heng Shu, Hai Center for Data Science New York University New YorkNY United States Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai China Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Province Shenzhen China Department of Biostatistics School of Global Public Health New York University New YorkNY United States

Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five ensemble models in forecasting sunspot numbers. In particular, we propose an ensemble model called XGBoost-DL, which uses XGBoost as a two-level nonlinear ensemble method to combine the deep learning models. Our XGBoost-DL achieves the best forecasting performance (RMSE = 25.70 and MAE = 19.82) in the comparison, outperforming the best non-deep learning model SARIMA (RMSE = 54.11 and MAE = 45.51), the best deep learning model Informer (RMSE = 29.90 and MAE = 22.35) and the NASA's forecast (RMSE = 48.38 and MAE = 38.45). Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025 for Solar Cycle 25 and 164.62 in November 2035 for Solar Cycle 26, similar to but later than the NASA's at 137.7 in October 2024 and 161.2 in December 2034. An open-source Python package of our XGBoost-DL for the sunspot number prediction is available at https://***/yd1008/ts_ensemble_sunspot. Copyright © 2022, The Authors. All rights reserved.

关键词： Solar energy

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Statistical Movement Classification based on Gram-Schmidt Transform

Statistical Movement Classification based on Gram-Schmidt Tr...

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Electronic Information technology and Smart Agriculture (ICEITSA), International Conference on

作者： Changhua Zhao Chunqiao Mi Yuanquan Shi School of Computer Science and Engineering Huaihua University Huaihua Hunan P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua Hunan P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan P. R. China

ISBN: (纸本)9781665413015

In this work, a technique for automatic classification of finger motion signals was introduced by using Gram-Schmidt orthonormalization. Because the motion signal has nonlinear and non-stationary processing characteristics, the Gram-Schmidt orthonormalization method is used to analyze the motion signal. By batch processing the high correlation between variables, the classification results are obtained. At the same time, combined with the idea of partial least squares, a new identity measure is proposed to select the best parameters. Finally, SVM is used to classify the movement types of the research objects. According to the test sample records of two finger motion signals (thumb and pinky), the average accuracy is 96.65%. The average accuracy of data activation (thumb and pinky) was 100%. Experiments show that the operation speed of Gram-Schmidt classification method is significantly improved, and it has better interpretability and prediction ability.

关键词： Support vector machines Dimensionality reduction Thumb Transforms Predictive models Time measurement Stability analysis

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ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data

arXiv

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arXiv 2024年

作者： Shu, Zixin Hua, Rui Yan, Dengying Lu, Chenxia Xu, Ning Li, Jun Zhu, Hui Zhang, Jia Zhao, Dan Hui, Chenyang Ye, Junqiu Liao, Chu Hao, Qi Ye, Wen Luo, Cheng Wang, Xinyan Cheng, Chuang Li, Xiaodong Liu, Baoyan Zhou, Xiaji Zhang, Runshun Xu, Min Zhou, Xuezhong Institute of Medical Intelligence School of Computer and Information Technology Beijing Jiaotong University Beijing100063 China Hubei Key Laboratory of the Theory and Application Research of Liver and Kidney in Traditional Chinese medicine Hubei Provincial Hospital of Traditional Chinese Medicine Wuhan430061 China National Data Center of Traditional Chinese Medicine China Academy of Chinese Medical Sciences Beijing100700 China Clinical College of Traditional Chinese Medicine Hubei University of Chinese Medicine Wuhan430000 China Department of Laboratory Medicine Hubei Provincial Hospital of Traditional Chinese Medicine Wuhan430061 China Department of Infectious Diseases Hubei Provincial Hospital of Traditional Chinese Medicine Wuhan430061 China Guang'anmen Hospital China Academy of Chinese Medical Sciences Beijing100053 China Information Technology Center the First Affiliated Hospital College of Medicine Zhejiang University Hangzhou310003 China

Background: Symptom phenotypes are one of the key types of manifestations for diagnosis and treatment of various disease conditions. However, the diversity of symptom terminologies is one of the major obstacles hindering the analysis and knowledge sharing of various types of symptom-related medical data particularly in the fields of Traditional Chinese Medicine (TCM). Objective: This study aimed to construct an Integrated Ontology of symptom phenotypes (ISPO) to support the data mining of Chinese EMRs and real-world study in TCM field. Methods: To construct an integrated ontology of symptom phenotypes (ISPO), we manually annotated classical TCM textbooks and large-scale Chinese electronic medical records (EMRs) to collect symptom terms with support from a medical text annotation system. Furthermore, to facilitate the semantic interoperability between different terminologies, we incorporated public available biomedical vocabularies by manual mapping between Chinese terms and English terms with cross-references to source vocabularies. In addition, we evaluated the ISPO using independent clinical EMRs to provide a high-usable medical ontology for clinical data analysis. Results: By integrating 78,696 inpatient cases of EMRs, 5 biomedical vocabularies, 21 TCM books and dictionaries, ISPO provides 3,147 concepts, 23,475 terms, and 55,552 definition or contextual texts. Adhering to the taxonomical structure of the related anatomical systems of symptom phenotypes, ISPO provides 12 top-level categories and 79 middle-level sub-categories. The validation of data analysis showed the ISPO has a coverage rate of 95.35%, 98.53% and 92.66% for symptom terms with occurrence rates of 0.5% in additional three independent curated clinical datasets, which can demonstrate the significant value of ISPO in mapping clinical terms to ontologies. Conclusions: ISPO delivers an integrated controlled vocabulary for symptom phenotypes that would enhance semantic interoperability among heterogene

关键词： Mapping

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Hybrid chaotic encryption algorithm for securing DICOM systems

引用

International Journal of Performability Engineering 2019年第5期15卷 1436-1444页

作者： Jiao, Ge Zhou, Sheng Li, Lang Zou, Yi College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Environment Protection and Safety Engineering University of South China Hengyang421002 China

The DICOM system contains patients' personal privacy and may present a risk of information leakage in telemedicine diagnostics. In order to solve the security problem of the DICOM system, we propose a medical image encryption method based on the cross-diffusion of the logistic map and the Chebyshev map. We first parse the DICOM image and read the data elements, and then we use the logistic map and the Chebyshev map to generate a key. Then, we cross-use these two sequences for further image encryption diffusion. Finally, we conduct in-depth experiments and comparisons with existing algorithms to validate the characteristics of high security and low correlation of adjacent pixels of the proposed encryption algorithm. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Medical imaging

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Adversarial Relighting Against Face Recognition

arXiv

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arXiv 2021年

作者： Zhang, Qian Guo, Qing Gao, Ruijun Juefei-Xu, Felix Yu, Hongkai Feng, Wei School of Computer Science and Technology College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin300305 China State Administration of Cultural Heritage China Nanyang Technological University Singapore Alibaba Group United States Cleveland State University ClevelandOH United States

Deep face recognition (FR) has achieved significantly high accuracy on several challenging datasets and fosters successful real-world applications, even showing high robustness to the illumination variation that is usually regarded as a main threat to the FR system. However, in the real world, illumination variation caused by diverse lighting conditions cannot be fully covered by the limited face dataset. In this paper, we study the threat of lighting against FR from a new angle, i.e., adversarial attack, and identify a new task, i.e., adversarial relighting. Given a face image, adversarial relighting aims to produce a naturally relighted counterpart while fooling the state-of-the-art deep FR methods. To this end, we first propose the physical model-based adversarial relighting attack (ARA) denoted as albedo-quotient-based adversarial relighting attack (AQ-ARA). It generates natural adversarial light under the physical lighting model and guidance of FR systems and synthesizes adversarially relighted face images. Moreover, we propose the auto-predictive adversarial relighting attack (AP-ARA) by training an adversarial relighting network (ARNet) to automatically predict the adversarial light in a one-step manner according to different input faces, allowing efficiency-sensitive applications. More importantly, we propose to transfer the above digital attacks to physical ARA (Phy-ARA) through a precise relighting device, making the estimated adversarial lighting condition reproducible in the real world. We validate our methods on three state-of-the-art deep FR methods, i.e., FaceNet, ArcFace, and CosFace, on two public datasets. The extensive and insightful results demonstrate our work can generate realistic adversarial relighted face images fooling face recognition tasks easily, revealing the threat of specific light directions and strengths. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Study on Information Diffusion Analysis in Social Networks and Its applications

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International Journal of Automation and computing 2018年第4期15卷 377-401页

作者： Biao Chang Tong Xu Qi Liu En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer ScienceUniversity of Science and Technology of China

Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximiza- tion and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffu- sion analysis are listed for further study.

关键词： Information diffusion influence evaluation influence maximization information source detection social network.

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