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7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Ning, Yuting Liu, Ye Huang, Zhenya Bi, Haoyang Liu, Qi Chen, Enhong Zhang, Dan University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology China University of Science and Technology of China School of Data Science China IFLYTEK Research IFLYTEK CO. LTD

ISBN: (纸本)9781665441490

Computerized Adaptive Testing (CAT), aiming to provide personalized tests for each examinee, is an emerging task in the intelligent education field. A CAT system selects questions step by step according to the knowledge states of each examinee, which are estimated by Cognitive Diagnosis Models (CDM). Most existing methods depend on the performance of a single CDM, which is often unstable. Besides, they may select similar questions to generate a test, which to some extent ignores the diversity of selected questions. To this end, in this paper, we propose a novel framework, namely Ensembled Computerized Adaptive Testing (EnCAT). Specifically, EnCAT comprises two components, ensemble part and explore part. In the ensemble part, we ensemble multiple CDMs to determine whether a question is informative, which ensures the stability of CAT process. Then, in the explore part, we learn the question representation from the question content and design a mechanism to quantify the similarity of different questions, which avoids the selection of similar questions and is free of expensive human labeling. Finally, extensive experiments are conducted on a real-world dataset, where the experimental results demonstrate the effectiveness of our proposed EnCAT framework with good performance. © 2021 IEEE.

关键词： Computerized Adaptive Testing Diverse Ensemble Learning Stable

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Control Method for Connecting the Elevated Expressway with the Ground Intersection Based on Reflective Film Material Guidance 10

Control Method for Connecting the Elevated Expressway with t...

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10th Annual International Conference on Material Science and Environmental Engineering, MSEE 2022

作者： Li, Yuqi Zhan, Xuejuan Liang, Zijun Wang, Ruihan Xiao, Yun Peng, Min School of Urban Construction and Transportation Hefei University China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory China Key Laboratory of Traffic Information and Safety of Anhui Higher Education Institutes Anhui Sanlian University China

With the rapid development of elevated expressways, urban elevated expressways bear more and more traffic volume, and the adoption of environmental protection materials has become an inevitable trend in the development of elevated expressways. a control method for connecting the elevated expressway with the ground intersection based on reflective film material is presented. Firstly, the green reflective film material is used to guide the connecting section of the elevated expressway and the ground intersection. Then, the corresponding signal timing method is proposed to optimize the traffic flow time between the elevated expressway and the ground ***, through the analysis of the VISSIM traffic simulation software to evaluate the results show that the method is based on the reflective film material can effectively guide the elevated expressway with the ground intersection connection section, under the premise by signal optimization technology to further improve the traffic efficiency, effectively reduces the overhead elevated expressway with the ground intersection connection number of the conflicts, It provides a basis for the research on the connection section between the elevated expressway and the ground intersection based on reflective film material guidance. © Published under licence by IOP Publishing Ltd.

关键词： Computer software

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Dependency and Association Enhanced Finding Similar Exercises in Online Education System 8

Dependency and Association Enhanced Finding Similar Exercise...

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Tang, Zihang Wang, Hao Gao, Weibo Huang, Zhenya Liu, Qi Yu, Jun University of Science and Technology of China School of Data Science China University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology China IFLYTEK CO. LTD. IFLYTEK Research

ISBN: (纸本)9781665477352

Finding Similar Exercises (FSE) is a tough nut to crack in online education systems, especially among large scale exercise corpus. Given exercises with associated attributes, contents (texts and images) and knowledge concepts, most solutions learn exercise representations in isolation, where only contents or concepts are exploited. Though a few works try to combine concept-exercise associations for capturing long-range similarity among exercises, they just utilize a single relation among concepts but neglect the multiple dependencies. In this paper, we propose a novel Dependency and Association enhanced Finding Similar Exercises framework, namely DAFSE, which can consider both the concept-exercise associations and the multiple dependency relations among concepts, simultaneously. Specifically, we first build a concept-exercise-content multi-layer graph and embed each concept, exercise and content node through an Embedding Layer. Then we introduce Attentive Combination Layer to effectively preserve the long-range similarity as well as rich domain knowledge to exercise representations by propagating on the multi-layer graph. Finally, we use a Similarity Score Layer to assess the similarity of exercise pairs. Extensive experiments on real-world datasets demonstrate the effectiveness of DAFSE. © 2022 IEEE.

关键词： Online systems

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Long-Term and Short-Term Perception in Knowledge Tracing 1

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11th CCF Big data Conference, Bigdata 2023

作者： Chen, Zihang Zhu, Mengxiao Wang, Fei Shen, Shuanghong Huang, Zhenya Liu, Qi Anhui Province Key Laboratory of Science Education and Communication Institute of Advanced Technology and School of Humanities and Social Sciences University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science and School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9789819989799

ISBN: (纸本)9789819989782

Knowledge Tracing (KT) is a fundamental task in contemporary intelligent educational systems, which tracks the knowledge states of the learners based on their response sequences. KT is crucial for the effectiveness of computer-assisted intelligent educational systems, such as intelligent tutoring systems and educational resource recommendation systems. In recent years, KT models benefited from the deep learning approaches and improved dramatically compared with the traditional probabilistic approaches. However, deep learning based KT models also have significant limitations. For example, Recurrent Neural Network (RNN)-based KT models can not capture dependencies in long-term sequences effectively, and attention mechanism-based KT models rely on the positional encoding to perceive sequential information, which may disrupt the semantics of the original embeddings. Moreover, the data sparsity question remains a big challenge in existing KT models. This study is based on the observation that KT shows a stronger sequential dependence in the long term than in the short term. In this paper, we propose a novel KT model called "Long-term and Short-term perception in knowledge tracing (LSKT)", which uses multilayer perceptrons and attention mechanism to capture the long-term and short-term dependencies, and employs 2PL-IRT based embedding to alleviate the data sparsity question. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed LSKT model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Deep Learning Educational data Mining Intelligent Education Knowledge Tracing

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Variable lanes traffic optimization control method combined with colorful asphalt materials 9

Variable lanes traffic optimization control method combined ...

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9th International Conference on Mechanical Engineering, Materials, and Automation Technology, MMEAT 2023

作者： Wang, Ruihan Zhan, Xuejuan Liang, Zijun Li, Yuqi Xiao, Yun Peng, Min School of Urban Construction and Transportation Hefei University China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory China Key Laboratory of Traffic Information and Safety of Anhui Higher Education Institutes Anhui Sanlian University China

ISBN: (纸本)9781510668584

Along with our country's continuous growth, rising rush hours traffic congestion, many scholars proposed setting variable lanes solution, but due to the variable lanes different times the direction of the lane change, which made the driver can not be correctly into the right lane. So we propose a traffic optimization control method for variable lanes combined with colorful asphalt materials. Firstly, enumeration method is used to list all the direction schemes and phase schemes of the variable lanes, and the optimal scheme is obtained according to the constraints. In order to better distinguish the variable lanes from the conventional lanes, the variable lanes are paved with colorful asphalt environmental materials. Then the phase timing is calculated according to the determined variable lanes, phase scheme and traffic flow. Finally, through VISSIM traffic simulation software to evaluate the results, the results show that combining with colorful asphalt materials clearly calibrate the position of the variable lanes, effective rule on the variable lanes traffic space, and on this basis, through phase timing optimization technology to further improve the intersection traffic efficiency, effectively alleviate the intersection rush hours traffic congestion. It provides a basis for the research of variable lanes optimization control combined with colorful asphalt materials. © 2023 SPIE.

关键词： Traffic congestion

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Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective 37

Adaptive Normalization for Non-stationary Time Series Foreca...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Liu, Zhiding Cheng, Mingyue Li, Zhi Huang, Zhenya Liu, Qi Xie, Yanhu Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Shenzhen International Graduate School Tsinghua University China The First Affiliated Hospital of University of Science and Technology of China China

ISBN: (纸本)9781713899921

Deep learning models have progressively advanced time series forecasting due to their powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to make accurate predictions due to the existence of non-stationarity in real-world data, denoting the data distribution rapidly changes over time. To mitigate such a dilemma, several efforts have been conducted by reducing the non-stationarity with normalization operation. However, these methods typically overlook the distribution discrepancy between the input series and the horizon series, and assume that all time points within the same instance share the same statistical properties, which is too ideal and may lead to suboptimal relative improvements. To this end, we propose a novel slice-level adaptive normalization, referred to SAN, which is a novel scheme for empowering time series forecasting with more flexible normalization and denormalization. SAN includes two crucial designs. First, SAN tries to eliminate the non-stationarity of time series in units of a local temporal slice (i.e., sub-series) rather than a global instance. Second, SAN employs a slight network module to independently model the evolving trends of statistical properties of raw time series. Consequently, SAN could serve as a general model-agnostic plugin and better alleviate the impact of the non-stationary nature of time series data. We instantiate the proposed SAN on four widely used forecasting models and test their prediction results on benchmark datasets to evaluate its effectiveness. Also, we report some insightful findings to deeply analyze and understand our proposed SAN. We make our codes publicly available. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Time series

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Continuous and generalized visual odometry based on FLANN_PSC-RANSAC_LSTM

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 15-28页

作者： Li, Dengao Feng, Ran Wu, Fanming Zhao, Jinhua Zhao, Jumin College of Computer Science and Technology College of Data Science Taiyuan University of Technology Shanxi Taiyuan China College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan University of Technology Shanxi Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China

In the field of simultaneous localization and mapping (SLAM), visual odometry (VO) always has great application prospects. In recent years, with the progress in the field of machine learning, methods based on neural networks are constantly being updated and applied. In this paper, we propose a continuous and generalized monocular visual odometry method based on features and neural networks. First, the feature information of adjacent image sequences is extracted by matching and troubleshooting algorithm (FLANN_PSC-RANSAC), then it and the corresponding six-degree-of-freedom information are simultaneously input into the long short-term memory artificial neural network (LSTM) for model construction, which not only ensures the reliability of the mode but also eliminates the influence of illumination on the data. In the real environment test, it has been effectively proved in terms of trajectory recovery accuracy and generalization ability to different environments and different illuminations. © 2024 - IOS Press. All rights reserved.

关键词： Long short-term memory

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GUESR: A Global Unsupervised data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation 28th

GUESR: A Global Unsupervised Data-Enhancement with Bucket-C...

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28th International Conference on database Systems for Advanced applications, DASFAA 2023

作者： Han, Yongqiang Wu, Likang Wang, Hao Wang, Guifeng Zhang, Mengdi Li, Zhi Lian, Defu Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei China Huawei Technologies Co. Ltd. Hangzhou China Meituan-Dianping Group Beijing China

ISBN: (纸本)9783031306716

Sequential Recommendation is a widely studied paradigm for learning users’ dynamic interests from historical interactions for predicting the next potential item. Although lots of research work has achieved remarkable progress, they are still plagued by the common issues: data sparsity of limited supervised signals and data noise of accidentally clicking. To this end, several works have attempted to address these issues, which ignored the complex association of items across several sequences. Along this line, with the aim of learning representative item embedding to alleviate this dilemma, we propose GUESR, from the view of graph contrastive learning. Specifically, we first construct the Global Item Relationship Graph (GIRG) from all interaction sequences and present the Bucket-Cluster Sampling (BCS) method to conduct the sub-graphs. Then, graph contrastive learning on this reduced graph is developed to enhance item representations with complex associations from the global view. We subsequently extend the CapsNet module with the elaborately introduced target-attention mechanism to derive users’ dynamic preferences. Extensive experimental results have demonstrated our proposed GUESR could not only achieve significant improvements but also could be regarded as a general enhancement strategy to improve the performance in combination with other sequential recommendation methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph neural networks

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DARE: disentanglement-augmented rationale extraction 22

DARE: disentanglement-augmented rationale extraction

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Linan Yue Qi Liu Yichao Du Yanqing An Li Wang Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence and ByteDance

ISBN: (纸本)9781713871088

Rationale extraction can be considered as a straightforward method of improving the model explainability, where rationales are a subsequence of the original inputs, and can be extracted to support the prediction results. Existing methods are mainly cascaded with the selector which extracts the rationale tokens, and the predictor which makes the prediction based on selected tokens. Since previous works fail to fully exploit the original input, where the information of non-selected tokens is ignored, in this paper, we propose a Disentanglement-Augmented Rationale Extraction (DARE) method, which encapsulates more information from the input to extract rationales. Specifically, it first disentangles the input into the rationale representations and the non-rationale ones, and then learns more comprehensive rationale representations for extracting by minimizing the mutual information (MI) between the two disentangled representations. Besides, to improve the performance of MI minimization, we develop a new MI estimator by exploring existing MI estimation methods. Extensive experimental results on three real-world datasets and simulation studies clearly validate the effectiveness of our proposed method.

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Understanding and improving fairness in cognitive diagnosis

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Science China(Information Sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN 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 State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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