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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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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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GL-BKGNN: Graphlet-based Bi-Kernel Interpretable Graph Neural Networks

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Information Fusion 2025年 123卷

作者： Xu, Lixiang Jiang, Kang Niu, Xin Chen, Enhong Luo, Bin Yu, Philip S. School of Artificial Intelligence Hefei Institute of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei China Department of Computer Science University of Illinois at Chicago ChicagoIL United States

While graph neural networks (GNNs) have successfully applied generalized convolution operations to the graph domain, providing a direct method to explain the dependency between the output and the presence of certain features and structural patterns in the input graph remains challenging. Inspired by image filters in standard convolutional neural networks (CNN), we propose a neural framework that connects bi-kernel with GNNs, incorporating predefined rules and focusing on the interpretability of graph filters during training. We address graph kernels based on their differentiability, using backpropagation of differentiable graph kernels for end-to-end training to generate gradient information for model optimization. Simultaneously, we leverage the stronger stability of non-differentiable graph kernels to capture local critical subgraphs, achieving deep fusion of structural features for node updates. Extensive experiments demonstrate that our model achieves competitive performance on graph classification datasets while providing additional benefits in interpretability. © 2025

关键词： Graph neural networks

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Nitrite Stimulates Hono and Nox But Not N2o Emissions in Chinese Agricultural Soils During Nitrification

SSRN

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SSRN 2023年

作者： Song, Yaqi Wu, Dianming Ju, Xiaotang Dörsch, Peter Wang, Mengdi Wang, Ruhai Song, Xiaotong Deng, Lingling Wang, Rui Gao, Zhiwei Haider, Haroon Hou, Lijun Liu, Min Yu, Yuanchun College of Biology and the Environment Co-Innovation Center for the Sustainable Forestry in Southern China Nanjing Forestry University Nanjing210037 China School of Geographic Sciences East China Normal University Shanghai200241 China Shanghai202162 China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai200241 China College of Tropical Crops Hainan University Haikou570228 China Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life Sciences ÅsN-1432 Norway State Key Laboratory of Soil and Sustainable Agriculture Institute of soil Sciences Chinese Academy of Sciences Nanjing210008 China State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing100085 China Key Laboratory of Estuarine and Coastal Research East China Normal University Shanghai200241 China

The long-lived greenhouse gas nitrous oxide (N2O) and short-lived reactive nitrogen (Nr) gases such as ammonia (NH3), nitrous acid (HONO), and nitrogen oxides (NOx) are produced and emitted from fertilized soils and play a critical role for climate warming and air quality. However, only few studies have quantified the production and emission potentials for long- and short-lived gaseous N species simultaneously in agricultural soils. To link the gaseous N species to intermediate N compounds [ammonium (NH4+), hydroxylamine (NH2OH), and nitrite (NO2‒)] and estimate their temperature change potential, ex-situ dry-out experiments were conducted with three Chinese agricultural soils. We found that HONO and NOx (NO + NO2) emissions mainly depend on NO2‒, while NH3 and N2O emissions are stimulated by NH4+ and NH2OH, respectively. Addition of 3,4-dimethylpyrazole phosphate (DMPP) and acetylene significantly reduced HONO and NOx emissions, while NH3 emissions were significantly enhanced in an alkaline Fluvo-aquic soil. These results suggested that ammonia-oxidizing bacteria (AOB) and complete ammonia-oxidizing bacteria (comammox Nitrospira) dominate HONO and NOx emissions in the alkaline Fluvo-aquic soil, while ammonia-oxidizing archaea (AOA) are dominant in the acidic Mollisol. DMPP effectively mitigated the warming effect in the Fluvo-aquic soil and the Ultisol. In conclusion, our findings highlight NO2‒ significantly stimulates HONO and NOx emissions from dryland agricultural soils, dominated by nitrification. In addition, subtle differences of soil NH3, N2O, HONO, and NOx emissions indicated different N turnover processes, and should be considered in biogeochemical and atmospheric chemistry models. © 2023, The Authors. All rights reserved.

关键词： Soils

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Estimating fund-raising performance for start-up projects from a market graph perspective

arXiv

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arXiv 2021年

作者： Wu, Likang Li, Zhi Zhao, Hongke Liu, Qi Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China No.96 JinZhai Road Baohe District Anhui Hefei230026 China College of Management and Economics Tianjin University No.92 Weijin Road Nankai District Tianjin300072 China

In the online innovation market, the fund-raising performance of the start-up project is a concerning issue for creators, investors and platforms. Unfortunately, existing studies always focus on modeling the fund-raising process after the publishment of a project but the predicting of a project attraction in the market before setting up is largely unexploited. Usually, this prediction is always with great challenges to making a comprehensive understanding of both the start-up project and market environment. To that end, in this paper, we present a focused study on this important problem from a market graph perspective. Specifically, we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment. In addition, we discriminatively model the project competitiveness and market preferences by designing two graph-based neural network architectures and incorporating them into a joint optimization stage. Furthermore, to explore the information propagation problem with dynamic environment in a large-scale market graph, we extend the GME model with parallelizing competitiveness quantification and hierarchical propagation algorithm. Finally, we conduct extensive experiments on real-world data. The experimental results clearly demonstrate the effectiveness of our proposed model. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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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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Quality Meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing

Quality Meets Diversity: A Model-Agnostic Framework for Comp...

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IEEE International Conference on data Mining (ICDM)

作者： Haoyang Bi Haiping Ma Zhenya Huang Yu Yin Qi Liu Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and ApplicationSchool 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 Computer Science and Technology University of Science and Technology of China Anhui University CN IFLYTEK Research CN

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It shows the advantage of tailoring a personalized testing procedure for each examinee, which selects questions step by step, depending on her performance. While there are many efforts on developing CAT systems, existing solutions generally follow an inflexible model-specific fashion. That is, they need to observe a specific cognitive model which can estimate examinee's knowledge levels and design the selection strategy according to the model estimation. In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models. Meanwhile, this work also figures out CAT solution with addressing the problem of how to generate both high-quality and diverse questions simultaneously, which can give a comprehensive knowledge diagnosis for each examinee. Inspired by Active Learning, we propose a novel framework, namely Model-Agnostic Adaptive Testing (MAAT) for CAT solution, where we design three sophisticated modules including Quality Module, Diversity Module and Importance Module. Specifically, at one CAT selection step, Quality Module first quantifies the informativeness of questions and generates candidate subset with the highest quality. Then, Diversity Module selects one question at each step that maximizes the concept coverage. Additionally, we propose Importance Module to estimate the importance of concepts that optimizes the CAT selection. Under MAAT, we prove that the goal of maximizing both quality and diversity is NP-hard, but we provide efficient algorithms by exploiting the inherent submodular property. Extensive experimental results on two real-world datasets clearly demonstrate that our MAAT can support CAT with guaranteeing both quality and diversity

关键词： Adaptation models Cats Computational modeling Estimation Games Testing Recruitment

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STGCN: A Spatial-Temporal Aware Graph Learning Method for POI Recommendation

STGCN: A Spatial-Temporal Aware Graph Learning Method for PO...

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IEEE International Conference on data Mining (ICDM)

作者： Haoyu Han Mengdi Zhang Min Hou Fuzheng Zhang Zhongyuan Wang Enhong Chen Hongwei Wang Jianhui Ma Qi Liu Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Meituan-Dianping Group Stanford University

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Point-of-Interest (POI) recommendation helps users find their interested places to visit based on the time and user location. Unlike traditional recommendation tasks, POI recommendation is personalized, spatial-aware, and temporally dependent. Although many previous works have tried modeling spatial and temporal characteristics, most of them suffer from the following two limitations: For the spatial aspect, existing works only consider the user-POI distance or POI-POI distance. However, we find that a user prefers different regions at different times, which is known as user-region periodic pattern. For the temporal aspect, most works treat user and time as two independent factors. However, different users may prefer the same POI in different time periods, which is known as user-POI periodic pattern. To address the limitation of existing works, we propose a novel Spatial-Temporal aware Graph Convolutional Neural Network (STGCN) for POI recommendation. Specifically, we first design a user record multigraph to fuse all the context information into a unified graph. Then, we propose a time-based neighborhood sampling algorithm and take advantage of the flexible propagation mechanism of GCNs to learn the representations of each node at a specific time. Furthermore, multiple scoring functions are proposed to exploit user-region periodic pattern and user-POI periodic pattern, respectively. We also develop a time smoothing strategy to alleviate the data sparsity problem. Extensive experiments are conducted on two real-world datasets, and the experimental results demonstrate the effectiveness of our method.

关键词： Learning systems Heating systems Smoothing methods Image color analysis Fuses data mining Task analysis

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XCrossNet: Feature structure-oriented learning for click-through rate prediction

arXiv

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arXiv 2021年

作者： Yu, Runlong Ye, Yuyang Liu, Qi Wang, Zihan Yang, Chunfeng Hu, Yucheng Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei China Management Science and Information Systems Rutgers Business School Rutgers University Newark United States MOE Key Laboratory of Computational Linguistics School of Electronics Engineering and Computer Science Peking University Beijing China Tencent Inc Shenzhen China

Click-Through Rate (CTR) prediction is a core task in nowadays commercial recommender systems. Feature crossing, as the mainline of research on CTR prediction, has shown a promising way to enhance predictive performance. Even though various models are able to learn feature interactions without manual feature engineering, they rarely attempt to individually learn representations for different feature structures. In particular, they mainly focus on the modeling of cross sparse features but neglect to specifically represent cross dense features. Motivated by this, we propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner. XCrossNet as a feature structure-oriented model leads to a more expressive representation and a more precise CTR prediction, which is not only explicit and interpretable, but also time-efficient and easy to implement. Experimental studies on Criteo Kaggle dataset show significant improvement of XCrossNet over state-of-the-art models on both effectiveness and efficiency. © 2021, CC BY.

关键词： Forecasting

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Surface soil moisture inversion based on DEFS-GA-BP model using Sentinel-1/2 remote sensing data 5

Surface soil moisture inversion based on DEFS-GA-BP model us...

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IET International Radar Conference 2020, IET IRC 2020

作者： Zhang, Chenyang Min, Lin Zhao, Jianhui 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 Kaifeng475004 China Network Information Center Office Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

The major work is to retrieve the surface soil moisture under crop cover in this paper. This paper proposes an improved GA-BP model called as DEFS-GA-BP model. This model is proposed to retrieve the surface soil moisture distribution using Sentinel-1 and Sentinel-2 remote sensing data. After extracting nine polarization features and one roughness parameter from Sentinel-1 data, five vegetation indexes from Sentinel-2 data. The differential evolution feature selection (DEFS) algorithm is applied to select the optimal feature subset. By using the genetic algorithm (GA) to optimize the node weights, the back propagation (BP) neural network is trained using the optimal feature subset and then used to generate the surface soil moisture distribution. The results of inversion algorithm indicate that the proposed DEFS-GA-BP model has a better inversion precision than GA-BP model, and demonstrates its application potential in surface soil moisture inversion. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Genetic algorithms

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