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A Model for Analyzing Students' Mastery of Knowledge Points

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A Model for Analyzing Students' Mastery of Knowledge Points

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xiao Wang Zengzhen Shao School of Information Science and Engineering Shandong Normal University Computer Application and Data Analysis Laboratory Jinan China

Deeply mining the detailed data in the process of students' examination plays an important role in studying students' learning characteristics and realizing individualized learning. Due to the association between test questions mostly given by experts, the number of choosing path length on each question in the process of answering questions can not be accurately collected. In this paper, we propose a model named MASMKP(A Model for Analyzing Students' Mastery of Knowledge Points) based on text analysis in order to combine the examination questions with the knowledge points they belong to. By analyzing the relationship between the questions and combining the number of choosing path length on each question in the process of answering the questions, we can get the students' mastery of the knowledge points. Depending on this model, the text features of test questions are extracted firstly, and the test questions are classified into knowledge points according to the classification model. Then the number of choosing path length on each question is converted to the number of choosing path length on each knowledge point. The number of choosing path length reflects the students' mastery of the knowledge point to a certain extent. Students are clustered according to their mastery of each knowledge point using k-means algorithm, then students with different characteristics are obtained and personalized teaching is realized. The experimental results show that this model can extract and classify the features of students, and the accuracy and validity of this model are verified.

关键词： Feature extraction Analytical models Classification algorithms Training data models Computational modeling

R2-Net: Relation of relation learning network for sentence semantic matching

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arXiv 2020年

作者： Zhang, Kun Wu, Le Lv, Guangyi Wang, Meng Chen, Enhong Ruan, Shulan School of Computer Science and Information Engineering Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China China

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially BERT. Despite their effectiveness, most of these models treat output labels as meaningless one-hot vectors, underestimating the semantic information and guidance of relations that these labels reveal, especially for tasks with a small number of labels. To address this problem, we propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. Specifically, we first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task for guiding R2-Net to consider more about relations. Meanwhile, a triplet loss is employed to distinguish the intra-class and inter-class relations in a finer granularity. Empirical experiments on two sentence semantic matching tasks demonstrate the superiority of our proposed model. As a byproduct, we have released the codes to facilitate other researches. © 2020, CC BY.

关键词： Natural language processing systems

An improved method of algal-bloom discrimination in Taihu Lake using Sentinel-1A data 6

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An improved method of algal-bloom discrimination in Taihu La...

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Wu, Lin Sun, Mengwei Min, Lin Zhao, Jianhui Li, Ning 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 Kaifeng475004 China

ISBN: (纸本)9781728129129

The algal bloom is a prominent manifestation of water pollution. Synthetic aperture radar (SAR) shows an advantage in water monitoring due to its characteristic of all-time and all-weather. The water regions where algae gather present dark in SAR image. However, dark regions may also be caused by other factors, such as low wind. This paper proposes an improved algal bloom discrimination method based on Artificial Neural Network (ANN) to recognize the dark regions of algal bloom. Taihu Lake is chosen as the research area in this study because of its serious bloom in recent years. By means of quasi-synchronous optical images, the dark region database of SAR images labeled algal bloom and non-algal bloom are obtained. Then the segmentation algorithm and region growing algorithm are used to acquire the feature from dark regions, and divided into training feature set and testing feature set. Finally, the training and testing feature set are used for ANN-based discrimination model construction and verification. According the experimental results, the overall accuracy reaches 80%, which indicates that ANN model has a good applicability in algal bloom recognition of SAR image. © 2019 IEEE.

关键词： Neural networks

Deep Technology Tracing for High-Tech Companies

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Deep Technology Tracing for High-Tech Companies

IEEE International Conference on data Mining (ICDM)

作者： Han Wu Kun Zhang Guangyi Lv Qi Liu Runlong Yu Weihao Zhao Enhong Chen Jianhui Ma Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China

Technological change and innovation are vitally important, especially for high-tech companies. However, factors influencing their future research and development (R&D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for high-tech companies. To this end, in this paper, we develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company. Specially, DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network. For one thing, PCR and CTR aim to capture competitive relations among enterprises and collaborative relations among technologies, respectively. For another, DTT is designed for modeling dynamic interactions between companies and technologies with the above relations involved. Finally, we evaluate our DTF framework on real-world patent data, and the experimental results clearly prove that DTF can precisely help to prospect future technology emphasis of companies by exploiting hybrid factors.

关键词：

Crowdfunding dynamics tracking: A reinforcement learning approach

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arXiv 2019年

作者： Wang, Jun Zhang, Hefu Liu, Qi Pan, Zhen Tao, Hanqing Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China

Recent years have witnessed the increasing interests in research of crowdfunding mechanism. In this area, dynamics tracking is a significant issue but is still under exploration. Existing studies either fit the fluctuations of time-series or employ regularization terms to constrain learned tendencies. However, few of them take into account the inherent decision-making process between investors and crowdfunding dynamics. To address the problem, in this paper, we propose a Trajectory-based Continuous Control for Crowdfunding (TC3) algorithm to predict the funding progress in crowdfunding. Specifically, actor-critic frameworks are employed to model the relationship between investors and campaigns, where all of the investors are viewed as an agent that could interact with the environment derived from the real dynamics of campaigns. Then, to further explore the in-depth implications of patterns (i.e., typical characters) in funding series, we propose to subdivide them into fast-growing and slow-growing ones. Moreover, for the purpose of switching from different kinds of patterns, the actor component of TC3 is extended with a structure of options, which comes to the TC3-Options. Finally, extensive experiments on the Indiegogo dataset not only demonstrate the effectiveness of our methods, but also validate our assumption that the entire pattern learned by TC3-Options is indeed the U-shaped one. Copyright © 2019, The Authors. All rights reserved.

关键词： Crowdsourcing

Fine-tuning by curriculum learning for non-autoregressive neural machine translation

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arXiv 2019年

作者： Guo, Junliang Tan, Xu Xu, Linli Qin, Tao Chen, Enhong Liu, Tie-Yan Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Microsoft Research

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher accuracy and are easier to train than NAT models, and both of them share the same model configurations, a natural idea to improve the accuracy of NAT models is to transfer a well-trained AT model to an NAT model through fine-tuning. However, since AT and NAT models differ greatly in training strategy, straightforward fine-tuning does not work well. In this work, we introduce curriculum learning into fine-tuning for NAT. Specifically, we design a curriculum in the fine-tuning process to progressively switch the training from autoregressive generation to non-autoregressive generation. Experiments on four benchmark translation datasets show that the proposed method achieves good improvement (more than 1 BLEU score) over previous NAT baselines in terms of translation accuracy, and greatly speed up (more than 10 times) the inference process over AT baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： Curricula

Research on the application of Students' Answered Record Analyze Model and Question Automatic Classify Based on K-Means Clustering Algorithm

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Research on the Application of Students' Answered Record Ana...

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Hongxu Sun Zengzhen Shao School of Information Science and Engineering Shandong Normal University Computer Application and Data Analysis Laboratory Jinan China School of Data Science and Computer Science Shandong Women's University Jinan China

In the era of smart education, computer assisted instruction (CAI) has been a hot research topic in the field of smart education. In the current study, most of the questions answered by students are summarized and classified by labor. The teachers classify the students' scores by hand according to the students' answer records in the examination, and then propose individualized instructional programs for students of the same type in view of their deficiencies in the knowledge points corresponding to the exam. Due to the large workload and the influence of teachers' experience, it is difficult to guarantee the efficiency and quality of manually classifying students' answered records. In the paper, we propose a model named CMD based on K-means clustering algorithm in order to analysis students' answer record. The model can automatically classify the test questions according to correct or wrong, modified times and the difficulty level of the students' answers. The experimental result shows that the CMD model can effectively realize the automatic classification of test questions, which not only reduces the labor consumption of teachers, but also has high accuracy.

关键词： Analytical models Education data models Clustering algorithms Computational modeling Classification algorithms data mining

Faster distributed deep net training: Computation and communication decoupled stochastic gradient descent

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arXiv 2019年

作者： Shen, Shuheng Xu, Linli Liu, Jingchang Liang, Xianfeng Cheng, Yifei Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China School of Data Science University of Science and Technology of China

With the increase in the amount of data and the expansion of model scale, distributed parallel training becomes an important and successful technique to address the optimization challenges. Nevertheless, although distributed stochastic gradient descent (SGD) algorithms can achieve a linear iteration speedup, they are limited significantly in practice by the communication cost, making it difficult to achieve a linear time speedup. In this paper, we propose a computation and communication decoupled stochastic gradient descent (CoCoDSGD) algorithm to run computation and communication in parallel to reduce the communication cost. We prove that CoCoD-SGD has a linear iteration speedup with respect to the total computation capability of the hardware resources. In addition, it has a lower communication complexity and better time speedup comparing with traditional distributed SGD algorithms. Experiments on deep neural network training demonstrate the significant improvements of CoCoD-SGD: when training ResNet18 and VGG16 with 16 Geforce GTX 1080Ti GPUs, CoCoD-SGD is up to 2-3x faster than traditional synchronous SGD. Copyright © 2019, The Authors. All rights reserved.

关键词： Program processors

Illuminating Recommendation by Understanding the Explicit Item Relations

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Journal of computer Science & Technology 2018年第4期33卷 739-755页

作者： Qi Liu Hong-Ke Zhao Le Wu Zhi Li En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230027 China School of Computer and Information Hefei University of Technology Hefei 230009 China School of Software Engineering University of Science and Technology of China Hefei 230051 China

Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from ＂implicit＂ to ＂explicit＂ views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.

关键词： recommender system item relation recommendation interpretability