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Coherence-based Automated Essay Scoring using Self-Attention

Coherence-based Automated Essay Scoring using Self-Attention

第十七届全国计算语言学学术会议暨第六届基于自然标注大数据的自然语言处理国际学术研讨会(CCL 2018)

作者： Xia Li Minping Chen Jianyun Nie Zhenxing Liu Ziheng Feng Yingdan Cai Key Laboratory of Language Engineering and Computing Guangdong university of foreign studies Guangzh School of Information Science and Technology/School of Cyber Security Guangdong university of foreig Department of Computer Science and Operations Research University of montreal MontrealCanada

Automated essay scoring aims to score an essay automatically without any human *** methods heavily rely on manual feature engi-neering,making it expensive to extract the *** recent studies used neural-network-based scoring models to avoid feature *** of them used CNN or RNN to learn the representation of the *** these mod-els can cope with relationships between words within a short distance,they are limited in capturing long-distance relationships across *** particular,it is difficult to assess the coherence of the essay,which is an essential criterion in essay *** this paper,we use self-attention to capture useful long-distance relationships between words so as to estimate a coherence *** tested our model on two datasets(ASAP and a new non-native speaker dataset).In both cases,our model outperforms the existing state-of-the-art models.

关键词： Self-Attention Automated Essay Scoring Neural networks

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A geometric image segmentation method based on a bi-convex, fuzzy, variational principle with teaching-learning optimization

A geometric image segmentation method based on a bi-convex, ...

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作者： Jin, Haiyan Li, Yaning Xing, Bei Wang, Lei Department of Computer Science and Engineering Xi'An University of Technology No. 5 South Jinhua Road Xi'an Shaanxi710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

This paper proposes an efficient, bi-convex, fuzzy, variational (BFV) method with teaching and learning based optimization (TLBO) for geometric image segmentation. Firstly, we adopt a bi-convex, object function to process a geometric image. Then, we introduce TLBO to maximally optimize the length-penalty item, which will be changed under the teaching phase and the learner phase of the TLBO. This makes the length penalty item closer to the target boundary. Therefore, the length-penalty item can be automatically adjusted according to the fitness function, namely the evaluation standards of the image quality. At last, we combine the length-penalty item with the numerical remedy mechanism to achieve better results. Compared with existing methods, simulations show that our method is more effective. © 2016 - IOS Press and the authors. All rights reserved.

关键词： Image segmentation

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A Two-grid Method for Linearizing and Symmetrizing the Steady-state Poisson-Nernst-Planck Equations

arXiv

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arXiv 2017年

作者： Li, Xuefang Yang, Ying Cheng, Hang School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin Guangxi541004 China School of Mathematics and Computing Science Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guangxi Key Laboratory of Cryptography and information Security Guilin University of Electronic Technology Guilin Guangxi541004 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin Guangxi541004 China

In this paper, a two-grid method is proposed to linearize and symmetrize the steady-state Poisson-Nernst-Planck equations. The computational system is decoupled to linearize and symmetrize equations by using this method, which can improve the computational efficiency compared with the finite element method. Error estimates are derived for the proposed method. The numerical results show that the two-grid method is effective.65N30, 92C40. Copyright © 2017, The Authors. All rights reserved.

关键词： Finite element method

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Overdue Prediction of Bank Loans Based on Deep Neural network

Overdue Prediction of Bank Loans Based on Deep Neural Networ...

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2017 International Symposium on Computer Science and Artificial Intelligence（ISCSAI 2017）

作者： Li Xin Sun Guozi Li Huakang Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Collaborative Innovation Center for Economics crime investigation and prevention technology State Key Laboratory of Mathematical Engineering and Advanced Computing

With the development of information technology, the application of big data in financial aspects becomes more and more deepening. However, in the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, are not benefit from the data *** paper proposes to use DNN algorithm to forecast the risk of user loan based on the difficulties of current overdue prediction and the excellent learning ability of DNN. This article uses user basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. Firstly, this paper record bank records according to the transaction type, respectively, to generate income and spending data. Secondly, to sum the user browsing behavior also, and to record the average of credit card bill. In addition, in order to reduce the effect of eigenvalue size on the result, all characteristics are standardized. Finally, users who lack user information are discarded and the above fields are spliced. The spliced fields are the basic input for DNN. From the experimental results, DNN algorithm inc rease over 6% prediction than kNN, Bayes algorithm.

关键词： Overdue Prediction Bank Loans DNN

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Deep hybrid residual learning with statistic priors for single image super-resolution

Deep hybrid residual learning with statistic priors for sing...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Risheng Liu Xiangyu Wang Xin Fan Haojie Li Zhongxuan Luo Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Department of Computing The Hong Kong Polytechnic University DUT-RU International School of Information & Software Engineering Dalian University of Technology

ISBN: (纸本)9781509060689

This paper considers single image super-resolution (SISR), which is an important low-level vision task and has various applications in multimedia society. Recently, deep neural networks have archived good performance on this field. But most of existing deep models are based on the fully data-dependent network architecture, thus missing majority of domain-knowledge of the super-resolution task. To address this limitation, we develop a new hybrid residual learning approach to leverage priors of SISR within the maximum a posteriori framework for network architecture design. We demonstrate that it can incorporate both image priors and data fidelity into the network, leading to a novel cascaded residual learning system for SISR process. Extensive experimental results on real-world images show that the proposed algorithm performs favorably against state-of-the-art methods.

关键词： Image resolution network architecture Radio frequency Training Degradation Dictionaries Convolution

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Leveraging geometric correlation for input-adaptive facial landmark regression

Leveraging geometric correlation for input-adaptive facial l...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yuyao Feng Risheng Liu Xin Fan Kang Huyan Zhongxuan Luo Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT-RU International School of Information & Software Engineering Dalian University of Technology Department of Computing The Hong Kong Polytechnic University

ISBN: (纸本)9781509060689

Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions on controlled facial images can successfully capture the appearance variations, the performance of these appearance-based models are tightly related to the quantity and quality of the training data. In this paper, we develop a novel framework, named geometric correlated landmark regression (GCLR), to inherit the advantages but overcome limitations of these two categories of methods. Specifically, we first establish a landmark-to-landmark regression to estimate the geometry of facial images. By further incorporating a sparse coding term into the regression framework, we can successfully leverage the geometric correlations between the test image and the shape dictionary, thus significantly enhance the geometry regression performance. Experimental results on various challenging facial data sets verify the effectiveness and efficiency of GCLR.

关键词： Training Correlation Shape Face Geometry Databases Encoding

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Least squares twin SVM decision tree for multi-class classification

Least squares twin SVM decision tree for multi-class classif...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics

作者： Qing Yu Lihui Wang Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology China

ISBN: (纸本)9781509037117

In order to solve multi-class classification problem in real world, we improved TSVM in this paper. We combined LSTSVM with partial binary tree to improve classification efficiency. Binary tree hierarchy can solved the inseparable regional issues in OVO-SVM and OVA-SVM classification. Experimental results show that it improved the classification accuracy. It also has better speed-up ratio than the OVO-SVM, OVA-SVM. It also reduced the training time. The time advantage is more obvious, especially the data set is large.

关键词： data mining the least squares support vector machine partial binary tree multi-class classification

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Learning aggregated transmission propagation networks for haze removal and beyond

arXiv

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arXiv 2017年

作者： Liu, Risheng Fan, Xin Hou, Minjun Jiang, Zhiying Luo, Zhongxuan Zhang, Lei DUT-RU International School of Information Science and Engineering Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian Department of Computing Hong Kong Polytechnic University

Single image dehazing is an important low-level vision task with many applications. Early researches have inves-tigated different kinds of visual priors to address this problem. However, they may fail when their assumptions are not valid on specific images. Recent deep networks also achieve relatively good performance in this task. But unfortunately, due to the disappreciation of rich physical rules in hazes, large amounts of data are required for their training. More importantly, they may still fail when there exist completely different haze distributions in testing images. By considering the collaborations of these two perspectives, this paper designs a novel residual architecture to aggregate both prior (i.e., domain knowledge) and data (i.e., haze distribution) information to propagate transmissions for scene ra-diance estimation. We further present a variational energy based perspective to investigate the intrinsic propagation behavior of our aggregated deep model. In this way, we actually bridge the gap between prior driven models and data driven networks and leverage advantages but avoid limitations of previous dehazing approaches. A lightweight learning framework is proposed to train our propagation network. Finally, by introducing a task-aware image separation formulation with a flexible optimization scheme, we extend the proposed model for more challenging vision tasks, such as underwater image enhancement and single image rain removal. Experiments on both synthetic and real-world images demonstrate the effectiveness and efficiency of the proposed framework. Copyright © 2017, The Authors. All rights reserved.

关键词： Domain Knowledge

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Multiple influence maximization in social networks 10

Multiple influence maximization in social networks

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10th International Conference on Ubiquitous Information Management and Communication, IMCOM 2016

作者： Sun, Honglin Gao, Xiaofeng Chen, Guihai Gu, Jian Wang, Yongjian Shanghai Key Lab. of Scalable Computing and Systems Dept. of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of Information Network Security of Ministry of Public Security Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9781450341424

Influence Maximization, a technique of social analysis to help marketers select a small group of users to promote their products with the goal of maximizing their influence spread, has been studied for a long time. However, previous researches did not consider the scenario that a company may intend to promote serval kinds of products in the same social network, where each user has different preferences for different categories. Considering this scenario in this paper, we propose the Multiple Influence Maximization (MIM) problem based on the assumption that one seed user can accept serval kinds of goods for free, and at the same time non-seed users have enough purchasing power to accept different promotions from their social friends. To address this issue, we design the corresponding greedy algorithm framework with an approximate radio of 1-1/e if the influence function is submodular. Besides, to simplify the computation of influence spread and considering the properties of real social influence propagation, we propose a novel influence quantitative computation model in which influence function fortunately is submodular. Applying this model into our MIM problem, the experiment results show that the greedy algorithm out-performs serval traditional heuristics in influence spread without loss of performance. © 2015 ACM.

关键词： Method of moments

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Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach

Robust Multi-Exposure Image Fusion: A Structural Patch Decom...

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作者： Ma, Kede Li, Hui Yong, Hongwei Wang, Zhou Meng, Deyu Zhang, Lei Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computing. Hong Kong Polytechnic University Hong Kong Hong Kong Institute for Information and System Sciences and Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China Inst. for Info. and Syst. Sci. and Min. of Educ. Key Lab. of Intelligent Networks and Netwk. Secty. Xi'an Jiaotong University Xi'an710049 China

We propose a simple yet effective structural patch decomposition approach for multi-exposure image fusion (MEF) that is robust to ghosting effect. We decompose an image patch into three conceptually independent components: signal strength, signal structure, and mean intensity. Upon fusing these three components separately, we reconstruct a desired patch and place it back into the fused image. This novel patch decomposition approach benefits MEF in many aspects. First, as opposed to most pixel-wise MEF methods, the proposed algorithm does not require post-processing steps to improve visual quality or to reduce spatial artifacts. Second, it handles RGB color channels jointly, and thus produces fused images with more vivid color appearance. Third and most importantly, the direction of the signal structure component in the patch vector space provides ideal information for ghost removal. It allows us to reliably and efficiently reject inconsistent object motions with respect to a chosen reference image without performing computationally expensive motion estimation. We compare the proposed algorithm with 12 MEF methods on 21 static scenes and 12 deghosting schemes on 19 dynamic scenes (with camera and object motion). Extensive experimental results demonstrate that the proposed algorithm not only outperforms previous MEF algorithms on static scenes but also consistently produces high quality fused images with little ghosting artifacts for dynamic scenes. Moreover, it maintains a lower computational cost compared with the state-of-The-Art deghosting schemes.11The MATLAB code of the proposed algorithm will be made available online. Preliminary results of Section III-A [1] were presented at the IEEE International Conference on Image Processing, Canada, 2015. © 1992-2012 IEEE.

关键词： Image fusion

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