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data augmentation in microscopic images for material data mining

npj Computational Materials 2020年第1期6卷 601-609页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China School of Materials Science and Technology Liaoning Technical UniversityLiaoning 114051China The Institute of Statistical Mathematics Research Organization of Information and SystemsTachikawaTokyo 190-8562Japan National Intellectual Property Administration Beijing 100088China Faculty of Engineering Sciences University of BurundiBujumburaBurundi Faculty of Engineering and Technology Palestine Technical University–KadoorieTulkaremPalestine College of Information Science and Engineering China University of PetroleumBeijingChina Key Lab of Petroleum Data Mining China University of PetroleumBeijingChina

Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise ***,we develop a novel transfer learning strategy to address problems of small or insufficient *** strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining *** a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real *** results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real *** the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.

关键词： mining instance competitive

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Automatic data distribution in large-scale OLTP applications

International Journal of Database Theory and Application

引用

International Journal of database Theory and Application 2014年第4期7卷 37-46页

作者： Wang, Xiaoyan Fan, Xu Chen, Jinchuan Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information and Electrical Engineering Ludong University Yantai 264025 China School of Information Renmin University of China Beijing 100872 China

With the dramatic increase of data volume, automatic data distribution has been one of the key techniques and intractable problem for distributed systems. This work summarizes the problem of data distribution and abstracts it as a general triangle model called DaWN. Based on data and workload analysis, it presents a novel strategy called ADDS for automatic data distribution in OLTP applications. According to the results of a series of experiments over TPC-C datasets and transactions, the proposed approach shows effective improvements.© 2014 SERSC.

关键词： data handling

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PSSL: Self-supervised learning for personalized search with contrastive sampling

arXiv

引用

arXiv 2021年

作者： Zhou, Yujia Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China School of Information Renmin University of China Université de Montréal MontréalQC Canada Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE

Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors. Previous studies have made great progress in extracting personal signals from the query log and learning user representations. However, neural personalized search is extremely dependent on sufficient data to train the user model. data sparsity is an inevitable challenge for existing methods to learn high-quality user representations. Moreover, the overemphasis on final ranking quality leads to rough data representations and impairs the generalizability of the model. To tackle these issues, we propose a Personalized Search framework with Self-supervised Learning (PSSL) to enhance data representations. Specifically, we adopt a contrastive sampling method to extract paired self-supervised information from sequences of user behaviors in query logs. Four auxiliary tasks are designed to pre-train the sentence encoder and the sequence encoder used in the ranking model. They are optimized by contrastive loss which aims to close the distance between similar user sequences, queries, and documents. Experimental results on two datasets demonstrate that our proposed model PSSL achieves state-of-the-art performance compared with existing *** Codes 68P20 Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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One chatbot per person: Creating personalized chatbots based on implicit user profiles

arXiv

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arXiv 2021年

作者： Ma, Zhengyi Dou, Zhicheng Zhu, Yutao Zhong, Hanxun Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China School of Information Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE Université de Montréal MontréalQC Canada

Personalized chatbots focus on endowing chatbots with a consistent personality to behave like real users, give more informative responses, and further act as personal assistants. Existing personalized approaches tried to incorporate several text descriptions as explicit user profiles. However, the acquisition of such explicit profiles is expensive and time-consuming, thus being impractical for large-scale real-world applications. Moreover, the restricted predefined profile neglects the language behavior of a real user and cannot be automatically updated together with the change of user interests. In this paper, we propose to learn implicit user profiles automatically from large-scale user dialogue history for building personalized chatbots. Specifically, leveraging the benefits of Transformer on language understanding, we train a personalized language model to construct a general user profile from the user's historical responses. To highlight the relevant historical responses to the input post, we further establish a key-value memory network of historical post-response pairs, and build a dynamic post-aware user profile. The dynamic profile mainly describes what and howthe user has responded to similar posts in history. To explicitly utilize users' frequently used words, we design a personalized decoder to fuse two decoding strategies, including generating a word from the generic vocabulary and copying one word from the user's personalized vocabulary. Experiments on two real-world datasets show the significant improvement of our model compared with existing methods. Our code is available at https://***/zhengyima/DHAP. Copyright © 2021, The Authors. All rights reserved.

关键词： User profile

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Drivers for strategic choice of cloud computing as online service in SMEs

Drivers for strategic choice of cloud computing as online se...

引用

International Conference on Information Systems, ICIS 2012

作者： Li, Min Yu, Yan Zhao, J. Leon Li, Xin University of Science and Technology China-City University of Hong Kong Joint Advanced Research Center 166 Renai Road Dushu Lake Higher Education Town Suzhou China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China 59 Zhongguancun Street Haidian District Beijing China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

ISBN: (纸本)9781627486040

Cloud Computing Service (CCS) paradigm is changing IT strategy of organizations in the digital world. CCS that requires few upfront investments and uses lease-based pricing is especially relevant to the Small and Medium Enterprises (SMEs), which have limited resources and may not know their true valuation for the IT prior to adoption. Thus, this research aims to investigate the influential factors of SMEs' strategic choice of CCS as online service. Relying upon Technology-Organization-Environment (TOE) paradigm, we identify both generic and context-specific factors from the three aspects and explain how the identified factors affect SMEs' CCS strategic choices. We hope this research can make contributions to innovation diffusion theory and IT strategy literature. We also hope the research with progress going on can generate insights for the CCS vendors who care about the sector of SME as well as the government administrators to make appropriate policies or supports for SMEs.

关键词： Cloud computing

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Hybrid Learning Network: A Novel Architecture for Fast Learning

引用

Procedia Computer Science 2017年 122卷 622-628页

作者： Ying Liu Chao Xiang School of Computer and Engineering University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

There’re many effective architectures of the artificial neural network(ANN). For which the training is a hard work. The cost for training an ANN increases exponentially when the ANN gets deeper or wider. We therefore propose a novel architecture, the Hybrid Learning Network(HLN), to achieve a fast learning with good stablity. The HLN can learn from both labeled data and unlabeled data at the same time in a hybrid learning manner. It uses a Self Organizing Map unified by the specially designed nonlinear function as the sparsity mask for a hidden layer to improve the training speed. We experiment our architecture on a synthetic dataset to test its regression capability against the traditional architecture, the result is promising.

关键词： hybrid learning neural network sparsity mask self organizing map fast learning

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Deepfake Network Architecture Attribution

arXiv

引用

arXiv 2022年

作者： Yang, Tianyun Huang, Ziyao Cao, Juan Li, Lei Li, Xirong Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class classification on several Generative Adversarial Network (GAN) models and obtain high accuracies. While encouraging, these works are restricted to model-level attribution, only capable of handling images generated by seen models with a specific seed, loss and dataset, which is limited in real-world scenarios when fake images may be generated by privately trained models. This motivates us to ask whether it is possible to attribute fake images to the source models’ architectures even if they are finetuned or retrained under different configurations. In this work, we present the first study on Deepfake Network Architecture Attribution to attribute fake images on architecture-level. Based on an observation that GAN architecture is likely to leave globally consistent fingerprints while traces left by model weights vary in different regions, we provide a simple yet effective solution named DNA-Det for this problem. Extensive experiments on multiple cross-test setups and a large-scale dataset demonstrate the effectiveness of DNA-Det. Our source code and dataset can be found here: https://***/ICTMCG/DNA-Det Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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knowledge Science, engineering and Management 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Robert Buchmann Claudiu Vasile Kifor Jian Yu

ISBN: (数字)9783319120966

ISBN: (纸本)9783319120959

This book constitutes the refereed proceedings of the 7th International Conference on knowledge Science, engineering and Management, KSEM 2014, held in Sibiu, Romania, in October 2014. The 30 revised full papers presented together with 5 short papers and 3 keynotes were carefully selected and reviewed from 77 submissions. The papers are organized in topical sections on formal semantics; content and document analysis; concept and lexical analysis; clustering and classification; metamodeling and conceptual modeling; enterprise knowledge; knowledge discovery and retrieval; formal knowledge processing; ontology engineering and management; knowledge management; and hybrid knowledge systems.

关键词： Artificial Intelligence data Mining and knowledge Discovery Information Storage and Retrieval Software engineering database Management Information Systems Applications (incl. Internet)

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A property of reductions in Fuzzy Variable Precision Rough Set model

A property of reductions in Fuzzy Variable Precision Rough S...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Eric C. C. Tsang Su-Yun Zhao Cai-Li Zhou Faculty of Information Technology Macau University of Science and Technology Taipa Macao China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China China School of Mathematics and Computer Sciences Hebei University China

In this paper, we use strict mathematics reasoning to discover the relation between the threshold and reduction in Fuzzy Variable Precision Rough Sets (FVPRS), i.e., the reductions act as a nested structure with the monotonously increasing threshold. By using the nested structure of reductions, we could design algorithms to quickly find different reductions when a reduction is required. Here `different' means the reductions obtained using different thresholds.

关键词： Rough sets Approximation methods Fuzzy sets Educational institutions Computational modeling Machine learning Cybernetics

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Automatic detection of integer sign vulnerabilities

Automatic detection of integer sign vulnerabilities

引用

International Conference on Information and Automation (ICIA)

作者： Bin Liang Hui Liu Wenchang Shi Zhiyong Shan Renmin University of China Beijing CN College of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China) MOE Beijing 100872 China

The integer sign vulnerability is a comparatively new and subtle type of vulnerabilities, they can compromise system security. Especially, if a sign vulnerability occurs in operating system kernel, it may result in very serious invalid read/write operations to kernel memory area. Unfortunately, little attention has been paid to static detecting them automatically. This paper presents a novel approach to detecting sign vulnerabilities in Linux kernel using type qualifier technique. We introduce three pairs of type qualifier and corresponding lattices to identify some key kernel data and relationships between them. Based on an extended type inference tool, we are able to effectively detect known and unknown sign vulnerabilities from elaborately preprocessed Linux kernel files. Our experiences demonstrate that type qualifier technique can be applied to detect sign vulnerabilities effectively.

关键词： Kernel Linux Lattices Indexes Arrays Security Buffer overflow

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