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IEEE International Conference on Data Mining (ICDM)

作者： Wei Tong Shiwei Tong Wei Hunag Liyang He Jianhui Ma Qi Liu Enhong Chen School of Computer Science and Technology Anhui Province Key Laboratory of Big Data Analysis and Application & School of Data Science University of Science and Technology of China University of Science and Technology of China China

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

In education systems, Finding Similar Exercises (FSE) is the key step for both exercise retrieval and duplicate detection. Recently, more and more attention has been drawn into this area and several works have been proposed, to utilize the exercise content (e.g., texts or images) or the labeled knowledge concepts. Such approaches, however, have failed to take knowledge hierarchy into account. To this end, we advance a novel knowledge-aware multimodal network, namely KnowNet, for finding similar exercises in large-scale online education systems by integrating the knowledge hierarchy into the heterogeneous exercise data and learning a relation-aware semantic representation. Specifically, we first propose a Content Representation Layer (CRL) to learn a unified semantic representation of the heterogeneous exercise content. Then, we design a Hierarchy Fusion Layer (HFL) to exploit the knowledge hierarchy. By combining the knowledge hierarchy, HFL can not only retrieve the relation-aware semantic representation but also provide an interpretable view to investigate the similarity of exercises. Finally, we adopt a Similarity Score Layer (SSL) for returning similar exercises. Extensive experiments demonstrate the effectiveness and interpretability of KnowNet.

关键词： Knowledge engineering Conferences Semantics Education Data mining

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Corrosion behavior of Mg-3Gd-1Zn-0.4Zr alloy with and without stacking faults

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Journal of Magnesium and Alloys 2019年第2期7卷 240-248页

作者： Xiaobo Zhang Jianwei Dai Ruifeng Zhang Zhixin Ba Nick Birbilis Jiangsu Key Laboratory of Advanced Structural Materials and Application Technology School of Materials Science and EngineeringNanjing Institute of TechnologyNanjing 211167China School of Materials Science and Engineering Central South UniversityChangsha 410083China College of Engineering and Computer Science Australian National UniversityActon ACT 2000Australia

To develop biodegradable magnesium alloy with desirable corrosion properties,a low Gd-containing Mg-3Gd-1Zn-0.4Zr(wt%,GZ31K)alloy was *** as-cast ingot was solution treated and then hot *** were characterized by scanning electron microscopy(SEM).Corrosion behavior of the alloy under each condition was studied by hydrogen evolution and quasi in-situ corrosion *** has been found that the as-cast alloy is composed ofα-Mg,stacking faults(SFs)at the outer edge of the matrix grains,and eutectic phase along the grain *** solution treatment,the SFs disappear and precipitates rich in Zn and Zr elements form in the grain interior and *** microstructure is significantly refined after *** evolution tests show that the as-cast alloy exhibits the best corrosion resistance,and the solution-treated alloy has the worst corrosion *** rate of the alloy under each condition decreases first and then increases with prolonging immersion *** experiments demonstrate thatα-Mg was corroded preferentially,the eutectic phase and precipitates exhibit better corrosion *** as-extruded alloy demonstrates uniform corrosion due to fine and homogeneous microstructure.

关键词： Magnesium alloys Stacking faults Corrosion resistance Uniform corrosion

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A new classification method of SAR image based on OGMRF-RC model 5

A new classification method of SAR image based on OGMRF-RC m...

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IET International Radar Conference 2020, IET IRC 2020

作者： Li, Yuan Guo, Zhishun Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475000 China College of Environment and Planning Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Synthetic Aperture Radar (SAR) image classification is a key technique and research focus in the application of remote sensing. This paper proposes a new classification method of SAR image based on Object-based Gaussian-Markov Random Field model with Region Coefficients (OGMRF-RC), the new method use Fuzzy Probability (FP) represents the region's category probability, FP is obtained by regional posterior probability and edge information of neighborhood. Use FP to replace the unique class label of the region, which avoids the parameters estimation inaccurate of the feature field caused by the unique initial class label. And the parameter calculation method of feature field takes into account the feature information of all regions in the image. In this paper, we take the area around Baliwan in Kaifeng as study area, Sentinel-1 is used as the remote sensing data sources. FCM, K-means, classic MRF and OGMRF-RC are used as contrast methods, the experimental result demonstrates the accuracy of our method is better than these methods. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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O2iJoin： An Efficient Index-Based Algorithm for Overlap Interval Join

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Journal of computer Science & technology 2018年第5期33卷 1023-1038页

作者： Ji-Zhou Luo Sheng-Fei Shi Guang Yang Hong-Zhi Wang Jian-Zhong Li School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China Guangdong Key Laboratory of Popular High Performance Computers Key Laboratory of Service Computing and Application Shenzhen 518000 China

Time intervals are often associated with tuples to represent their valid time in temporal relations, where overlap join is crucial for various kinds of queries. Many existing overlap join algorithms use indices based on tree structures such as quad-tree, B＋-tree and interval tree. These algorithms usually have high CPU cost since deep path traversals are unavoidable, which makes them not so competitive as data-partition or plane-sweep based algorithms. This paper proposes an efficient overlap join algorithm based on a new two-layer flat index named as Overlap Interval Inverted Index （i.e., O2i Index）. It uses an array to record the end points of intervals and approximates the nesting structures of intervals via two functions in the first layer, and the second layer uses inverted lists to trace all intervals satisfying the approximated nesting structures. With the help of the new index, the join algorithm only visits the must-be-scanned lists and skips all others. Analyses and experiments on both real and synthetic datasets show that the proposed algorithm is as competitive as the state-of-the-art algorithms.

关键词： overlap interval join temporal relation overlap inverted index join algorithm

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On the value of imbalance loss functions in enhancing deep learning-based vulnerability detection

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Expert Systems with applications 2025年 291卷

作者： Xiaoxue Ma Yanzhong He Jacky Keung Cheng Tan Chuanxiang Ma Wenhua Hu Fuyang Li School of Science and Technology Hong Kong Metropolitan University Hong Kong China School of Computer Science Wuhan University Wuhan Hubei China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan Hubei China Department of Computer Science City University of Hong Kong Hong Kong China School of Computer Science Hubei University Wuhan Hubei China Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University) Wuhan Hubei China

Software vulnerability detection is crucial in software engineering and information security, and deep learning has been demonstrated to be effective in this domain. However, the class imbalance issue, where non-vulnerable code snippets vastly outnumber vulnerable ones, hinders the performance of deep learning-based vulnerability detection (DLVD) models. Although some recent research has explored the use of imbalance loss functions to address this issue and enhance model efficacy, they have primarily focused on a limited selection of imbalance loss functions, leaving many others unexplored. Therefore, their conclusions about the most effective imbalance loss function may be biased and inconclusive. To fill this gap, we first conduct a comprehensive literature review of 119 DLVD studies, focusing on the loss functions used by these models. We then assess the effectiveness of nine imbalance loss functions alongside cross entropy (CE) loss (the standard balanced loss function) on two DLVD models across four public vulnerability datasets. Our evaluation incorporates six performance metrics, with results analyzed using the Scott-Knott effect size difference (ESD) test. Furthermore, we employ interpretable analysis to elucidate the impact of loss functions on model performance. Our findings provide key insights for DLVD, which mainly include the following: the LineVul model consistently outperforms the ReVeal model; label distribution aware margin (LDAM) loss achieves the highest Precision, while logit adjustment (LA) loss yields the best Recall; Class balanced focal (CB-Focal) loss excels in comprehensive performance on extremely imbalanced datasets; and LA loss is optimal for nearly balanced datasets. We recommend using LineVul with either CB-Focal loss or LA loss to enhance DLVD outcomes. Our source code and datasets are available at https://***/YanzhongHe/DLVD-ImbalanceLossEmpirical .

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Structure-Based Knowledge Tracing: An Influence Propagation View

Structure-Based Knowledge Tracing: An Influence Propagation ...

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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.

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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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