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13th Asian Conference on Computer Vision (ACCV)

作者： Wang, Ruxin Han, Congying Li, Mingqiang Guo, Tiande Univ Chinese Acad Sci Sch Math Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9783319544076;9783319544069

Single image super-resolution (SISR) reconstruction is currently a very fundamental and significant task in image processing. Instead of upscaling the image in spatial domain, we propose a novel SISR method based on edge preserving integrating the external gradient priors by deep learning method (auto-encoder network) and internal gradient priors using non-local total variation (NLTV). The gradient domain effectively reflects the high frequency details and edge information of nature image to some extent. The joint perspective exploits the complementary advantages of external and internal gradient prior knowledge for reconstructing the HR image. The experimental results demonstrate the effectiveness of our approach over several state-of-art SISR methods.

关键词： Optical resolving power

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TopPPR: Top-k Personalized PageRank Queries with Precision Guarantees on Large Graphs 18

TopPPR: Top-k Personalized PageRank Queries with Precision G...

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44th ACM SIGMOD International Conference on management of data

作者： Wei, Zhewei He, Xiaodong Xiao, Xiaokui Wang, Sibo Shang, Shuo Wen, Ji-Rong Renmin Univ China Sch Infromat Beijing Peoples R China Natl Univ Singapore Sch Comp Singapore Singapore Univ Queensland Brisbane Qld Australia King Abdullah Univ Sci & Technol CEMSE Thuwal Saudi Arabia Renmin Univ China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Renmin Univ China Key Lab Data Engn & Knowledge Engn MOE Beijing Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781450317436

Personalized PageRank (PPR) is a classic metric that measures the relevance of graph nodes with respect to a source node. Given a graph G, a source node s, and a parameter k, a top-k PPR query returns a set of k nodes with the highest PPR values with respect to s. This type of queries serves as an important building block for numerous applications in web search and social networks, such as Twitter's Who-To-Follow recommendation service. Existing techniques for top-k PPR, however, suffer from two major deficiencies. First, they either incur prohibitive space and time overheads on large graphs, or fail to provide any guarantee on the precision of top-k results (i.e., the results returned might miss a number of actual top-k answers). Second, most of them require significant pre-computation on the input graph G, which renders them unsuitable for graphs with frequent updates (e.g., Twitter's social graph). To address the deficiencies of existing solutions, we propose TopPPR, an algorithm for top-k PPR queries that ensure at least rho precision (i.e., at least rho fraction of the actual top-k results are returned) with at least 1-1/n probability, where rho is an element of (0, 1] is a user-specified parameter and n is the number of nodes in G. In addition, TopPPR offers non-trivial guarantees on query time in terms of rho, and it can easily handle dynamic graphs as it does not require any preprocessing. We experimentally evaluate TopPPR using a variety of benchmark datasets, and demonstrate that TopPPR outperforms the state-of-the-art solutions in terms of both efficiency and precision, even when we set rho = 1 (i.e., when TopPPR returns the exact top-k results). Notably, on a billion-edge Twitter graph, TopPPR only requires 15 seconds to answer a top-500 PPR query with rho = 1.

关键词： Personalized PageRank Top-k Queries

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A Novel Parsing-Based Automatic Domain Terminology Extraction Method 18th

A Novel Parsing-Based Automatic Domain Terminology Extractio...

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18th International Conference on Computational Science (ICCS)

作者： Liu, Ying Zhang, Tianlin Quan, Pei Wen, Yueran Wu, Kaichao He, Hongbo Univ Chinese Acad Sci Sch Comp & Control Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Renmin Univ China Sch Lab & Human Resources Beijing 100872 Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China

ISBN: (纸本)9783319937137;9783319937120

As domain terminology plays a crucial role in the study of every domain, automatic domain terminology extraction method is in real demand. In this paper, we propose a novel parsing-based method which generates the domain compound terms by utilizing the dependent relations between the words. Dependency parsing is used to identify the dependent relations. In addition, a multi-factor evaluator is proposed to evaluate the significance of each candidate term which not only considers frequency but also includes the influence of other factors affecting domain terminology. Experimental results demonstrate that the proposed domain terminology extraction method outperforms the traditional POS-base method in both precision and recall.

关键词： Domain terminology extraction Dependency parsing Multi-factor evaluation

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Online Robust Lagrangian Support Vector Machine against Adversarial Attack 6th

Online Robust Lagrangian Support Vector Machine against Adve...

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6th International Conference on Information Technology and Quantitative management (ITQM)

作者： Ma, Yue He, Yiwei Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

In adversarial environment such as intrusion detection and spam filtering, the adversary intruder or spam advertiser may attempt to produce contaminate training instance and manipulate the learning of classifier. In order to keep good classification performance, many robuster learning methods have been proposed to deal with the adversarial attack. Support Vector Machines(SVMs) is a kind of successful approach in the adversarial classification tasks and the investigation of robust SVMs is very popular. However, in many real application, the data including stain instance is coming dynamically. Batch learning which needs retraining when encountering new samples, will consume more computing resources. In this paper, we propose a robust Lagrangian support vector machine (RLSVM) with modified kernel matrix and explore the online learning algorithm on it. The experimental results show the robustness of RLSVM against label noise produced by adversaries under the online adversarial environment. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： adversarial attack poison attack label noise online learning Lagrangian SVM

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When to Learn What: Deep Cognitive Subspace Clustering 18

When to Learn What: Deep Cognitive Subspace Clustering

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26th ACM Multimedia Conference (MM)

作者： Jiang, Yangbangyan Yang, Zhiyong Xu, Qianqian Cao, Xiaochun Huang, Qingming Chinese Acad Sci State Key Lab Informat Secur Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9781450356657

Subspace clustering aims at clustering data points drawn from a union of low-dimensional subspaces. Recently deep neural networks are introduced into this problem to improve both representation ability and precision for non-linear data. However, such models are sensitive to noise and outliers, since both difficult and easy samples are treated equally. On the contrary, in the human cognitive process, individuals tend to follow a learning paradigm from easy to hard and less to more. In other words, human beings always learn from simple concepts, then absorb more complicated ones gradually. Inspired by such learning scheme, in this paper, we propose a robust deep subspace clustering framework based on the principle of human cognitive process. Specifically, we measure the easinesses of samples dynamically so that our proposed method could gradually utilize instances from easy to more complex ones in a robust way. Meanwhile, a promising solution is designed to update the weights and parameters using an alternative optimization strategy, followed by a theoretical analysis to demonstrated the rationality of the proposed method. Experimental results on three popular benchmark datasets demonstrate the validity of the proposed method.

关键词： Subspace clustering Self-paced learning Deep learning

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Recommending New Features from Mobile App Descriptions

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2019年第4期28卷 22-22页

作者： Jiang, He Zhang, Jingxuan Li, Xiaochen Ren, Zhilei Lo, David Wu, Xindong Luo, Zhongxuan Dalian Univ Technol Sch Software Dalian 116000 Liaoning Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116000 Liaoning Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Singapore Management Univ Sch Informat Syst Singapore Singapore Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Anhui Peoples R China Mininglamp Acad Sci Mininglamp Technol Minist Educ Beijing Peoples R China

The rapidly evolving mobile applications (apps) have brought great demand for developers to identify new features by inspecting the descriptions of similar apps and acquire missing features for their apps. Unfortunately, due to the huge number of apps, this manual process is time-consuming and unscalable. To help developers identify new features, we propose a new approach named SAFER. In this study, we first develop a tool to automatically extract features from app descriptions. Then, given an app, we leverage the topic model to identify its similar apps based on the extracted features and API names of apps. Finally, we design a feature recommendation algorithm to aggregate and recommend the features of identified similar apps to the specified app. Evaluated over a collection of 533 annotated features from 100 apps, SAFER achieves a Hit@15 score of up to 78.68% and outperforms the baseline approach KNN+ by 17.23% on average. In addition, we also compare SAFER against a typical technique of recommending features from user reviews, i.e., CLAP. Experimental results reveal that SAFER is superior to CLAP by 23.54% in terms of Hit@15.

关键词： Mobile applications feature recommender system domain analysis topic model

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SamEn-SVR: using sample entropy and support vector regression for bug number prediction

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IET SOFTWARE 2018年第3期12卷 183-189页

作者： Zhang, Wen Du, Yuhang Yoshida, Taketoshi Wang, Qing Li, Xiangjun Beijing Univ Technol Sch Econ & Management Beijing 100124 Peoples R China Beijing Univ Chem Technol Res Ctr Big Data Sci Beijing 100029 Peoples R China Japan Adv Inst Sci & Technol Sch Knowledge Sci 1-1 Ashahidai Nomi City Ishikawa 9231292 Japan Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China Xian Univ Sch Informat Engn Xian 710065 Shaanxi Peoples R China

Monitoring and predicting the trend of bug number time series of a software system is crucial for both software project managers and software end-users. For software managers, accurate prediction of bug number of a software system will assist them in making timely decisions, such as effort investment and resource allocation. For software end-users, knowing possible bug number of their systems ahead will enable them to adopt timely actions in coping with the loss caused by possible system failures. This study proposes an approach called SamEn-SVR to combine sample entropy and support vector regression (SVR) to predict software bug number using time series analysis. The basic idea is to use template vectors with the smallest complexity as input vectors for SVR classifiers to ensure predictability of time series. By using Mozilla Firefox bug data, we conduct extensive experiments to compare the proposed approach and state-of-the-art techniques including auto-regressive integrated moving average (ARIMA), X12 enhanced ARIMA and polynomial regression to predict bug number time series. Experimental results demonstrate that the proposed SamEn-SVR approach outperforms state-of-the-art techniques in bug number prediction.

关键词： autoregressive moving average processes program debugging support vector machines regression analysis entropy time series pattern classification vectors software management sample entropy support vector regression bug number prediction bug number time series software system software project managers software end-users software bug number time series analysis template vectors input vectors SVR classifiers Mozilla Firefox bug data SamEn-SVR approach

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Evaluating Doctor Performance: Ordinal Regression-Based Approach

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JOURNAL OF MEDICAL INTERNET RESEARCH 2018年第7期20卷 e240-e240页

作者： Shi, Yong Li, Peijia Yu, Xiaodan Wang, Huadong Niu, Lingfeng Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci 80 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Dev & Planning Res Inst Ningbo Zhejiang Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing Peoples R China

Background: Doctor's performance evaluation is an important task in mobile health (mHealth), which aims to evaluate the overall quality of online diagnosis and patient outcomes so that customer satisfaction and loyalty can be attained. However, most patients tend not to rate doctors' performance, therefore, it is imperative to develop a model to make doctor's performance evaluation automatic. When evaluating doctors' performance, we rate it into a score label that is as close as possible to the true one. Objective: This study aims to perform automatic doctor's performance evaluation from online textual consultations between doctors and patients by way of a novel machine learning method. Methods: We propose a solution that models doctor's performance evaluation as an ordinal regression problem. In doing so, a support vector machine combined with an ordinal partitioning model (SVMOP), along with an innovative predictive function will be developed to capture the hidden preferences of the ordering labels over doctor's performance evaluation. When engineering the basic text features, eight customized features (extracted from over 70,000 medical entries) were added and further boosted by the Gradient Boosting Decision Tree algorithm. Results: Real data sets from one of the largest mobile doctor/patient communication platforms in China are used in our study. Statistically, 64% of data on mHealth platforms lack the evaluation labels from patients. Experimental results reveal that our approach can support an automatic doctor performance evaluation. Compared with other auto-evaluation models, SVMOP improves mean absolute error (MAE) by 0.1, mean square error (MSE) by 0.5, pairwise accuracy (PAcc) by 5%;the suggested customized features improve MAE by 0.1, MSE by 0.2, PAcc by 3%. After boosting, performance is further improved. Based on SVMOP, predictive features like politeness and sentiment words can be mined, which can be further applied to guide the development of mHealth

关键词： performance evaluation ordinal regression mHealth support vector machines ordinal partitioning

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Large-Scale Linear NPSVM via One Permutation Hashing

Large-Scale Linear NPSVM via One Permutation Hashing

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International Joint Conference on Neural Networks (IJCNN)

作者： Tang, Jingjing Tian, Yingjie Liu, Dalian Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Beijing Union Univ Dept Basic Course Teaching Beijing 100101 Peoples R China

ISBN: (纸本)9781509060146

Nonparallel support vector machine (NPSVM) is a novel nonparallel classifier for binary classification with large amounts of theoretical and practical advantages. How to train NPSVM efficiently on data with huge dimensions has not been studied. Recently, a variety of minwise hashing algorithms such as b-bit minwise hashing, connected bit minwise hashing and f - fractional bit minwise hashing are effectively applied to obtain a compact representation of the original data. However, they still have serious defects that the generation of k random permutations is time-consuming and the processing of the original dataset damages data structure. Fortunately, a simple and effective solution called one permutation hashing appears to avoid the disadvantages of the expensive preprocessing cost and the destruction of the original dataset. In this paper, we combine one permutation hashing scheme with linear NPSVM to speed up the training and testing phases for classification on large-scale and high dimensional datasets. Both theoretical analyses and experiments demonstrate that our algorithm achieves massive advantages in accuracy, efficiency and energy-consumption.

关键词： Minwise hashing one permutation similarity estimation large-scale linear NPSVM

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A complex network for studying the transmission mechanisms in stock market

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2017年 484卷 345-357页

作者： Long, Wen Guan, Lijing Shen, Jiangjian Song, Linqiu Cui, Lingxiao Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

This paper introduces a new complex network to describe the volatility transmission mechanisms in stock market. The network can not only endogenize stock market's volatility but also figure out the direction of volatility spillover. In this model, we first use BEKK-GARCH to estimate the volatility spillover effects among Chinese 18 industry sectors. Then, based on the ARCH coefficients and GARCH coefficients, the directional shock networks and variance networks in different stages are constructed separately. We find that the spillover effects and network structures changes in different stages. The results of the topological stability test demonstrate that the connectivity of networks becomes more fragile to selective attacks than stochastic attacks. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Complex network BEKK Volatility spillover effects Endogenous

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