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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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Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on Retentive Network 7

Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on ...

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7th International Conference on Computer Information Science and application Technology, CISAT 2024

作者： Su, Ruilin Zhang, Wanshan Yu, Dunhui Hubei University Wuhan China Wuhan430062 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China Hubei University Hubei Key Laboratory of Big Data Intelligent Analysis and Application Wuhan430062 China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan430062 China

ISBN: (纸本)9798350375107

Multi-hop Knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. Aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model's memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model's own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing. © 2024 IEEE.

关键词： Iterative decoding

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datasets of Visualization for Machine Learning

arXiv

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arXiv 2024年

作者： Liu, Can Jiang, Ruike Tan, Shaocong Yu, Jiacheng Yang, Chaofan Shao, Hanning Yuan, Xiaoru Key Laboratory of Machine Perception Ministry of Education School of Intelligence Science and Technology Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China

datasets of visualization play a crucial role in automating data-driven visualization pipelines, serving as the foundation for supervised model training and algorithm benchmarking. In this paper, we survey the literature on visualization datasets and provide a comprehensive overview of existing visualization datasets, including their data types, formats, supported tasks, and openness. We propose a what-why-how model for visualization datasets, considering the content of the dataset (what), the supported tasks (why), and the dataset construction process (how). This model provides a clear understanding of the diversity and complexity of visualization datasets. Additionally, we highlight the challenges faced by existing visualization datasets, including the lack of standardization in data types and formats and the limited availability of large-scale datasets. To address these challenges, we suggest future research directions. © 2024, CC BY.

关键词： Machine learning

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Polarization imaging bi-attentional recursive residual super-resolution method 9

Polarization imaging bi-attentional recursive residual super...

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9th Symposium on Novel Photoelectronic Detection Technology and applications

作者： Liu, Jiaqing Li, Yi Liang, Dong Xu, Guoming Zhou, Pucheng Ma, Jian School of Internet Anhui University Hefei China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei China Anhui Provincial Key Laboratory of Polarized Light Imaging Detection Hefei China School of Electrical and Electronics Engineering Anhui Wenda University of Information Engineering Hefei China

ISBN: (纸本)9781510664432

Due to the limitation of the detector, the spatial resolution of the polarization image obtained by space-modulated full-polarization computed imaging is low. In general, there are a lot of valuable high-frequency components in low resolution space, but it is difficult to reconstruct high frequency information by deep learning method. Through the research of the previous super-resolution reconstruction method based on deep learning, it is found that it is difficult to obtain better lifting effect only by stacking residual blocks to build a deeper network. A polarization imaging bi-attention recursive residual network super-resolution reconstruction method is proposed. In the network structure, bi-attention recursive residual group is used as the deep feature extraction module, and the bi-attention module contained in this module is used to adaptively learn image features of different channels and different transformation Spaces in the deep network. In order to provide the concentrated learning efficiency of high-frequency information, jump connections are introduced into the network structure, and the shallow feature extraction and reconstruction of images are completed by a convolution layer and a sub-pixel convolution algorithm respectively. The experimental verification is carried out under the actual imaging system and simulation data, and compared with other methods in terms of visual effect and quantitative results. Experimental results verify that the proposed method can effectively reconstruct the contour structure and texture details of the detection target in subjective image quality, and is superior to the comparison method in objective evaluation index. © 2023 SPIE.

关键词： Feature extraction

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Cooperative Classification and Rationalization for Graph Generalization

arXiv

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arXiv 2024年

作者： Yue, Linan Liu, Qi Liu, Ye Gao, Weibo Yao, Fangzhou Li, Wenfeng Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Douyin Vision Co. Ltd Beijing China

Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) data. Several approaches have been proposed to address this problem. Among them, one solution is to diversify training distributions in vanilla classification by modifying the data environment, yet accessing the environment information is complex. Besides, another promising approach involves rationalization, extracting invariant rationales for predictions. However, extracting rationales is difficult due to limited learning signals, resulting in less accurate rationales and diminished predictions. To address these challenges, in this paper, we propose a Cooperative Classification and Rationalization (C2R) method, consisting of the classification and the rationalization module. Specifically, we first assume that multiple environments are available in the classification module. Then, we introduce diverse training distributions using an environment-conditional generative network, enabling robust graph representations. Meanwhile, the rationalization module employs a separator to identify relevant rationale subgraphs while the remaining non-rationale subgraphs are de-correlated with labels. Next, we align graph representations from the classification module with rationale subgraph representations using the knowledge distillation methods, enhancing the learning signal for rationales. Finally, we infer multiple environments by gathering non-rationale representations and incorporate them into the classification module for cooperative learning. Extensive experimental results on both benchmarks and synthetic datasets demonstrate the effectiveness of C2R. Code is available at https://***/yuelinan/Codes-of-C2R. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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QuesNet: A unified representation for heterogeneous test questions

arXiv

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arXiv 2019年

作者： Yin, Yu Liu, Qi Huang, Zhenya Chen, Enhong Tong, Wei Wang, Shijin Su, Yu Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology School of Data Science University of Science and Technology of China IFLYTEK Research IFLYTEK CO. LTD. State Key Laboratory of Cognitive Intelligence

Understanding learning materials (e.g. test questions) is a crucial issue in online learning systems, which can promote many applications in education domain. Unfortunately, many supervised approaches suffer from the problem of scarce human labeled data, whereas abundant unlabeled resources are highly underutilized. To alleviate this problem, an effective solution is to use pre-trained representations for question understanding. However, existing pretraining methods in NLP area are infeasible to learn test question representations due to several domain-specific characteristics in education. First, questions usually comprise of heterogeneous data including content text, images and side information. Second, there exists both basic linguistic information as well as domain logic and knowledge. To this end, in this paper, we propose a novel pre-training method, namely QuesNet, for comprehensively learning question representations. Specifically, we first design a unified framework to aggregate question information with its heterogeneous inputs into a comprehensive vector. Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way. Here, a novel holed language model objective is developed to extract low-level linguistic features, and a domain-oriented objective is proposed to learn high-level logic and knowledge. Moreover,we showthat Ques- Net has good capability of being fine-tuned in many question-based tasks. We conduct extensive experiments on large-scale real-world question data, where the experimental results clearly demonstrate the effectiveness of QuesNet for question understanding as well as its superior applicability. Copyright © 2019, The Authors. All rights reserved.

关键词： Learning systems

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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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Fast extraction of winter wheat planting area in Huang-Huai-Hai Plain using high-resolution satellite imagery on a cloud computing platform

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International Journal of Agricultural and Biological Engineering 2022年第1期15卷 241-250页

作者： Dongyan Zhang Mengru Zhang Fenfang Lin Zhenggao Pan Fei Jiang Liang He Hang Yang Ning Jin National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions(Henan University) Ministry of EducationKaifeng 475004HenanChina School of Informatics and Engineering Suzhou UniversitySuzhou 234000AnhuiChina National Meteorological Center Beijing 100081China Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100101China

To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing *** Earth Engine(GEE),a cloud computing analysis platform based on global geospatial analysis,provides a new opportunity for rapid analysis of remote sensing *** this study,high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in *** random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020,and Sentinel-2 imagery was used to verify the *** spectral indices,texture,and terrain features of the image were derived,and their contribution to the classification accuracy of winter wheat was evaluated by *** the top nine features were selected to form an optimal feature *** the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier,the results show that the accuracy difference between the two feature classification schemes is small,but the classification effect of all feature sets is slightly better than the optimal feature *** overall classification accuracy of sample plots verification was 86%-95%,the Kappa coefficient was between 0.7 and 0.85,and the percentage error of the total area was 5.42%.The research demonstrates a reliable method for mapping a wide range of winter wheat planting area,and provides a good prospect for exploring the precise mapping of other crops,which is of great significance to crop monitoring and agricultural development.

关键词： Google Earth Engine regional scale winter wheat rapid mapping

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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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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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