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Robust unsupervised feature selection by nonnegative sparse subspace learning

Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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software Defect Distribution Prediction Model Based on NPE-SVM

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China Communications 2018年第5期15卷 173-182页

作者： Hua Wei Chun Shan Changzhen Hu Huizhong Sun Min Lei School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China China Information Technology Security Evaluation Center Beijing 100085China Beijing Key Laboratory of Software Security Engineering Technology School of SoftwareBeijing Institute of TechnologyBeijing 100081China Information Security Center Beijing University of Posts and TelecommunicationsBeijing 100876China Guizhou University Guizhou Provincial Key Laboratory of Public Big DataGuizhou Guiyang 550025China

During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper proposed a novel software defect prediction model based on neighborhood preserving embedded support vector machine(NPESVM) algorithm. The model uses SVM as the basic classifier of software defect distribution prediction model, and the NPE algorithm is combined to keep the local geometric structure of the data unchanged in the process of dimensionality reduction. The problem of precision reduction of SVM caused by data loss after attribute reduction is avoided. Compared with single SVM and LLE-SVM prediction algorithm, the prediction model in this paper improves the F-measure in aspect of software defect distribution prediction by 3%~4%.

关键词： data redundancy SVM NPE algorithm dimensionality reduction

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lMFF: Efficient and Scalable layered Materials Force Field on Heterogeneous Many-Core Processors

lMFF: Efficient and Scalable layered Materials Force Field o...

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Supercomputing Conference

作者： Ping Gao Xiaohui Duan Jiaxu Guo Jin Wang Zhenya Song Lizhen Cui Xiangxu Meng Xin Liu Wusheng Zhang Ming Ma Guohui Li Dexun Chen Haohuan Fu Wei Xue Weiguo Liu Guangwen Yang School of Software Shandong University Jinan China National Supercomputing Center in Wuxi Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China College of Computer Science and Technology Jilin University Changchun China Department of Engineering Mechanics Center for Nano and Micro Mechanics Tsinghua University Beijing China Ministry of Natural Resources First Institute of Oceanography and Key Laboratory of Marine Science and Numerical Modeling Qingdao China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Jinan China Engineering Research Center of Digital Media Technology Ministry of Education Jinan China Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China Department of Earth System Science Ministry of Education Key Lab for Earth System Modeling Tsinghua University Beijing China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

LAMMPS is one of the most popular Molecular Dynamic (MD) packages and is widely used in the field of physics, chemistry and materials simulation. Layered Materials Force Field (LMFF) is our expansion of the LAMMPS potential function based on the Tersoff potential and inter-layer potential (ILP) in LAMMPS. LMFF is designed to study layered materials such as graphene and boron hexanitride. It is universal and does not depend on any platform. We have also carried out a series of optimizations on LMFF and the optimization work is carried out on the new generation of Sunway supercomputer, called SWLMFF. Experiments show that our implementation is efficient, scalable and portable. When generic LMFF is ported to Intel Xeon Gold 6278C, $2\times$ performance improvement is achieved. For the optimized SWLMFF, the overall performance improvement is nearly $200-330\times$ compared to the original ILP and Tersoff potentials. And SWLMFF has good parallel efficiency of 95%-100% under weak scaling with 2.7 million atoms on a single process. The maximum atomic system simulated by SWLMFF is close to $2^{31}$ atoms. And nanosecond simulations in one day can be realized.

关键词： Program processors Atomic layer deposition High performance computing Force Graphene Supercomputers Optimization

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SHREC 2019 - Monocular image based 3D model retrieval 12

SHREC 2019 - Monocular image based 3D model retrieval

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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer Graphics, EG 2019

作者： Li, Wenhui Liu, Anan Nie, Weizhi Song, Dan Li, Yuqian Wang, Weijie Xiang, Shu Zhou, Heyu Bui, Ngoc-Minh Cen, Yunchi Chen, Zenian Chung-Nguyen, Huy-Hoang Diep, Gia-Han Do, Trong-Le Doubrovski, Eugeni L. Duong, Anh-Duc Geraedts, Jo M.P. Guo, Haobin Hoang, Trung-Hieu Li, Yichen Liu, Xing Liu, Zishun Luu, Duc-Tuan Ma, Yunsheng Nguyen, Vinh-Tiep Nie, Jie Ren, Tongwei Tran, Mai-Khiem Tran-Nguyen, Son-Thanh Tran, Minh-Triet Vu-Le, The-Anh Wang, Charlie C.L. Wang, Shijie Wu, Gangshan Yang, Caifei Yuan, Meng Zhai, Hao Zhang, Ao Zhang, Fan Zhao, Sicheng Shool of Electrical and Information Engineering Tianjin University China University of Science VNU-HCM Viet Nam State Key Laboratory of Virtual Reality Technology and System Beihang University China Delft University of Technology Netherlands University of Information Technology VNU-HCM Viet Nam Nanjing University China SuoAo Technology Center SAEE University of Science and Technology Beijing China Chinese University of Hong Kong Hong Kong School of Software Dalian University of Technology China Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Ocean University of China China

ISBN: (纸本)9783038680772

Monocular image based 3D object retrieval is a novel and challenging research topic in the field of 3D object retrieval. Given a RGB image captured in real world, it aims to search for relevant 3D objects from a dataset. To advance this promising research, we organize this SHREC track and build the first monocular image based 3D object retrieval benchmark by collecting 2D images from ImageNet and 3D objects from popular 3D datasets such as NTU, PSB, ModelNet40 and ShapeNet. The benchmark contains classified 21,000 2D images and 7,690 3D objects of 21 categories. This track attracted 9 groups from 4 countries and the submission of 20 runs. To have a comprehensive comparison, 7 commonly-used retrieval performance metrics have been used to evaluate their retrieval performance. The evaluation results show that the supervised cross domain learning get the superior retrieval performance (Best NN is 97.4 %) by bridging the domain gap with label information. However, there is still a big challenge for unsupervised cross domain learning (Best NN is 61.2%), which is more practical for the real application. Although we provided both view images and OBJ file for each 3D model, all the participants use the view images to represent the 3D model. One of the interesting work in the future is directly using the 3D information and 2D RGB information to solve the task of monocular Image based 3D model retrieval. © 2019 The Author(s)

关键词： 3D modeling

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Revisting the impact of regression models for predicting the number of defects 30

Revisting the impact of regression models for predicting the...

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30th International Conference on software engineering and Knowledge engineering, SEKE 2018

作者： Wu, Man Ye, Sizhe Li, Chunhua Ma, Ziyi Fu, Zhongwang Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China

ISBN: (纸本)1891706446

Predicting the number of faults in software modules can be more helpful instead of predicting the modules being faulty or non-faulty. Chen et al. (SEKE 397-402, 2015) and Rathore et al. (Soft Computing 21: 7417-7434, 2017) empirically investigate the feasibility of some regression algorithms for predicting the number of defects. The experimental results showed that the decision tree regression algorithm performed best in terms of average absolute error (AAE), average relative error (ARE) and root mean square error (RMSE). However, they did not consider the imbalanced data distribution problem in defect datasets and employed improper performance measures for evaluating the regression models to evaluate the performance of models for predicting the number of defects. Hence, we revisit the impact of different regression algorithms for predicting the number of defects using Fault-Percentile-Average (FPA) as the performance measure. The experiments on 31 datasets from PROMISE repository show that the prediction performance of models for predicting the number of defects built by different regression algorithms are various, and the gradient boosting regression algorithm and the Bayesian ridge regression algorithm can achieve better performance. © 2018 Universitat zu Koln. All rights reserved.

关键词： Defects

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Research on computer Forecast Model Using BP Neural Network and Pearson Correlation Coefficient

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Journal of Physics: Conference Series 2021年第1期2033卷

作者： Ji Li Baihan Wang Haorui Li Institute of statistics and applied mathematics Anhui University of Finance & Economics Bengbu Anhui 233000 College of Information and Electrical Engineering China Agricultural University Beijing 100000 School of Software Taiyuan University of Technology Jinzhong Shanxi 030600

Driven by such special needs as "individuation, fashion and beauty", the production model of the new retail enterprise is gradually moving towards multiple varieties and small batches, which makes the variety of ornaments and toys in the retail stores in the shopping malls more dazzling, and at the same time, makes inventory management in the retail industry more difficult. How to give an accurate demand forecast based on the historical sales data with complex levels and various categories at the regional level, small category level and even the store skc (single monochrome) level is a problem that most new retail enterprises need to focus on and think about. First of all, we study the impact of various related factors on the sales volume of the target skc during the four holidays of National Day, Double Eleven, Double Twelve and New Year's Day in 2018. We first reviewed the literature and data to identify the influencing factors. We believe that factors such as product characteristics, inventory information and holidays will affect the sales volume. Secondly, we test the correlation coefficient of Pearson and Spearman on the sales volume and each factor, and obtain the correlation strength between each factor and the price, we think that those with strong correlation have a greater impact on the sales volume. Finally, we get the specific impact of each factor on the sales volume through regression fitting between each relevant factor and the sales volume. Secondly, we need to forecast the sales volume of a given region's target subcategory every month for three months, give the average absolute percentage error between each and the forecast value, and forecast the top ten subcategories of sales volume at the same time, first, we analyze the data and adopt the neural network model to forecast, give the monthly forecast data, and consider the impact of relevant factors, so that the results are more accurate. Finally, the monthly forecast value of MAPE is given.

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3DFR: A Swift 3D Feature Reductionist Framework for Scene Independent Change Detection

arXiv

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arXiv 2019年

作者： Vipparthi, Santosh Kumar Mishra, Abhishek Dhar, Vansh Mandal, Murari Vision Intelligence Lab Department of Computer Science and Engineering Jaipur India School of Computing and Information Technology Manipal University Jaipur

In this paper we propose an end-to-end swift 3D feature reductionist framework (3DFR) for scene independent change detection. The 3DFR framework consists of three feature streams: a swift 3D feature reductionist stream (AvFeat), a contemporary feature stream (ConFeat) and a temporal median feature map. These multilateral foreground/background features are further refined through an encoder-decoder network. As a result, the proposed framework not only detects temporal changes but also learns high-level appearance features. Thus, it incorporates the object semantics for effective change detection. Furthermore, the proposed framework is validated through a scene independent evaluation scheme in order to demonstrate the robustness and generalization capability of the network. The performance of the proposed method is evaluated on the benchmark CDnet 2014 dataset. The experimental results show that the proposed 3DFR network outperforms the state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Multi-Modal Curriculum Learning over Graphs

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ACM Transactions on Intelligent Systems and technology 2019年第4期10卷 1–25页

作者： Chen Gong Jian Yang Dacheng Tao PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China UBTECH Sydney Artificial Intelligence Centre and the School of Computer Science Faculty of Engineering and Information Technologies the University of Sydney Sydney Australia

Curriculum Learning (CL) is a recently proposed learning paradigm that aims to achieve satisfactory performance by properly organizing the learning sequence from simple curriculum examples to more difficult ones. Up to now, few works have been done to explore CL for the data with graph structure. Therefore, this article proposes a novel CL algorithm that can be utilized to guide the label Propagation (LP) over graphs, of which the target is to “learn” the labels of unlabeled examples on the graphs. Specifically, we assume that different unlabeled examples have different levels of difficulty for propagation, and their label learning should follow a simple-to-difficult sequence with the updated curricula. Furthermore, considering that the practical data are often characterized by multiple modalities, every modality in our method is associated with a “teacher” that not only evaluates the difficulties of examples from its own viewpoint, but also cooperates with other teachers to generate the overall simplest curriculum examples for propagation. By taking the curriculums suggested by the teachers as a whole, the common preference (i.e., commonality) of teachers on selecting the simplest examples can be discovered by a row-sparse matrix, and their distinct opinions (i.e., individuality) are captured by a sparse noise matrix. As a result, an accurate curriculum sequence can be established and the propagation quality can thus be improved. Theoretically, we prove that the propagation risk bound is closely related to the examples’ difficulty information, and empirically, we show that our method can generate higher accuracy than the state-of-the-art CL approach and LP algorithms on various multi-modal tasks.

关键词： Curriculum learning label propagation multi-modal learning semi-supervised learning

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ChainFileSynch: An Innovate File Synchronization for Cloud Storage with Blockchain

ChainFileSynch: An Innovate File Synchronization for Cloud S...

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International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Yunpeng Wang Xiao Sun Fangdong Zhu Fan Zhang Min Zhang Haisheng Cao Southwest China Institute of Electronic Technology Chengdu China College of Computer Sichuan University Chengdu China School of Software Engineering of Chengdu University of Information Technology Chengdu China

Cloud storage environment based on distributed operation requires high consistency for data copies stored on many servers and client terminals. At present, various data and file synchronization tools can adapt to most application scenarios, but there are still some problems, such as high efficient data synchronization, rapid verification of synchronization results, free access to synchronized files, high availability of the master node, etc. This paper proposes an innovative cloud storage's folders and files synchronization Model: ChainFileSynch, which contains a fast cloud storage file synchronization architecture, process and processing algorithm based on Blockchain. The architecture makes full use of Blockchain distribution and traceability: the consensus mechanism and the like implement synchronization of cloud storage based on cloud file transactions. Compared to other traditional cloud synchronization technologies, ChainFileSynch improves cloud storage synchronization efficiency, data synchronization verification and network reliability.

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Corrigendum to “Rational Protocols and Attacks in Blockchain System”

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Security and Communication Networks 2021年第1期2021卷

作者： Tao Li Yuling Chen Yanli Wang Yilei Wang Minghao Zhao Haojia Zhu Youliang Tian Xiaomei Yu Yixian Yang State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou 550025 *** College of Computer Science and Technology Guizhou University Guiyang Guizhou 550025 *** School of Information Science and Engineering Qufu Normal University Rizhao 276825 *** School of Software Tsinghua University Beijing 100084 *** School of Information Science and Engineering Shandong Normal University Jinan 250014 ***

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