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arXiv 2018年

作者： Dong, Jianfeng Li, Xirong Xu, Chaoxi Ji, Shouling He, Yuan Yang, Gang Wang, Xun College of Computer and Information Engineering Zhejiang Gongshang University Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China College of Computer Science Zhejiang University Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Alibaba Group

This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is required. The majority of existing methods are concept based, extracting relevant concepts from queries and videos and accordingly establishing associations between the two modalities. In contrast, this paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Dual encoding is conceptually simple, practically effective and end-to-end. As experiments on three benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the proposed solution establishes a new state-of-the-art for zero-example video retrieval. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

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Link prediction in author collaboration network based on BP neural network 3

Link prediction in author collaboration network based on BP ...

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3rd International Conference on Mechanical, Electronic and Information Technology engineering, ICMITE 2017

作者： Chen, Chaoqun Deng, Sanhong Lu, Jing School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Lab of Data Engineering and Knowledge Service Nanjing210023 China Jiangsu Institute of Geological Survey Nanjing210018 China

Recently, more and more authors have been encouraged for collaboration because it often produces good results. However, the author collaboration network contains experts in various research directions within various fields, and it is difficult for individual authors to decide which authors are best suited to their expertise. This paper uses the relationships among authors to predict new relationships that may arise, recommending each author with the collaborators they may be interested in. The data source comes from 4-year data in DBLP from 2001 to 2004. After data cleaning, the training set and test set are constructed and then used BP neural network to build model. At the same time, this article compares the performance with Logistic Regression, SVM and Random Forest. The experiment shows that the BP neural network can get better result, and it is feasible to predict links in the author collaboration network. © The Authors, published by EDP Sciences, 2017.

关键词： Forecasting

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Automatic Chinese Multiple-Choice Question Generation for Human Resource Performance Appraisal

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Procedia Computer Science 2018年 139卷 165-172页

作者： Pei Quan Yong Shi Lingfeng Niu Ying Liu Tianlin Zhang School of Computer and Control Engineering University of Academy of Sciences Beijing 100190 China School of Economics and Management 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 Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA

Performance appraisal has always been an important research topic in human resource management. A reasonable performance appraisal plan lays a solid foundation for the development of an enterprise. Especially as globalization and technology advance, in order to meet the fast-changing strategic goals and increasing cross-functional tasks, enterprises face new challenges in performance appraisal. How to improve employees’ ability to accept new knowledge efficiently and constantly has been an urgent problem for enterprises. In this paper, we propose an automatic method which generation multiple-choice questions by utilizing the relations between different terminology. Graphical model is used to extract core concept from different corpus while word embedding technology is used to indicate the relevant relations. Experimental results demonstrate that the proposed question generation method outperforms the traditional manual method in both efficiency and confusion.

关键词： Human resource management multiple-choice question generation natural language processing word embedding

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NODE IMPORTANCE MEASURE FOR SCIENTIFIC RESEARCH COLlabORATION FROM HYPERNETWORK PERSPECTIVE

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2016年第2期23卷 397-404页

作者： Xiao, Quan Jiangxi Univ Finance & Econ Sch Informat Technol Yuping West St 556 Nanchang 330032 Jiangxi Peoples R China Jiangxi Key Lab Data & Knowledge Engn Yuping West St 556 Nanchang 330032 Jiangxi Peoples R China

Collaboration has become main stream and trend in interdisciplinary fields. In research collaboration organizations, to evaluate the contributions of researchers to the organization and then to identify core researchers is an important issue to carry out performance appraisal and crisis management of brain drain. Scientific research collaboration network is a basic model to investigate this question, but under the context of increasingly complex collaborative behaviour, it shows its limitations for semantic representations. In this paper, by introducing hypernetwork, a more powerful modelling tool than traditional network, and taking scientific paper co-authorship as object to construct scientific research collaboration hypernetwork (SRCH), we measure the importance of researchers in two aspects, as collaborative relationship structure and collaborative achievement value from a hypernetwork perspective. An additive weighting method with adjustable parameters is utilized to integrate the evaluation indicators of the two aspects, and then the synthetical importance evaluation of researchers is obtained. Analysis of data instance verifies that our node importance measure for scientific research collaboration from hypernetwork perspective is reasonable and effective.

关键词： adjustable parameter hyper network node importance scientific research collaboration network

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Author Correction:data augmentation in microscopic images for material data mining

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npj Computational Materials 2020年第1期6卷 476-476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 Beijing Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing China School of Materials Science and Engineering University of Science and Technology Beijing Beijing China School of Materials Science and Technology Liaoning Technical University Liaoning China The Institute of Statistical Mathematics Research Organization of Information and Systems Tachikawa Tokyo Japan National Intellectual Property Administration Beijing China Faculty of Engineering Sciences University of Burundi Bujumbura Burundi Faculty of Engineering and Technology Palestine Technical University – Kadoorie Tulkarem Palestine College of Information Science and Engineering China University of Petroleum Beijing China Key Lab of Petroleum Data Mining China University of Petroleum Beijing China

The original version of this article omitted the following from the Acknowledgements:“This work was supported by Beijing Top Discipline for Artificial Intelligent Science and engineering,University of Science and Tec... 详细信息

关键词： HTML knowledge mining

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Hybrid Learning Network: A Novel Architecture for Fast Learning

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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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An empirical investigation of the older adults' continuance intention towards SNSs

An empirical investigation of the older adults' continuance ...

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作者： Liu, Wei Jiangxi Key Laboratory of Data and Knowledge Engineering Institute of Information Resources Management School of Information Technology Jiangxi University of Finance and Economics Nanchang360013 China

Although Social Network Services (SNSs) continuance usage has recently emerged as an important issue in information systems adaption, the research into older adults' continuance intention towards SNS is still very low, and the acceptance-discontinuance anomaly phenomenon (i.e. users discontinue using e-learning after initially accepting it) is a common occurrence. This study joins the perceived risk and perceived enjoyment to our prior research model which integrated the Expectation-Confirmation Model (ECM), the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB) to hypothesise a theoretical model to explain and predict the older adults' intentions to continue using SNSs. The hypothesised model is validated empirically using a sample collected from 320 older adults who had prior experience with SNSs and was tested against the proposed research model using structure equation modelling. The results demonstrate that satisfaction has the most significant effect on older adults' continuance intention, followed by perceived usefulness, perceived risk, subject norm and perceived behavioural control. The results of these findings for SNSs practitioners are discussed at the end of this work. © Copyright 2016 Inderscience Enterprises Ltd.

关键词： Social networking (online)

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arXiv

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arXiv 2017年

作者： Dong, Jianfeng Li, Xirong Xu, Duanqing College of Computer Science and Technology Zhejiang University Hangzhou310027 China Key Lab of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing100872 China

—In order to retrieve unlabeled images by textual queries, cross-media similarity computation is a key ingredient. Although novel methods are continuously introduced, little has been done to evaluate these methods together with large-scale query log analysis. Consequently, how far have these methods brought us in answering real-user queries is unclear. Given baseline methods that use relatively simple text/image matching, how much progress have advanced models made is also unclear. This paper takes a pragmatic approach to answering the two questions. Queries are automatically categorized according to the proposed query visualness measure, and later connected to the evaluation of multiple cross-media similarity models on three test sets. Such a connection reveals that the success of the state-of-the-art is mainly attributed to their good performance on visual-oriented queries, which account for only a small part of real-user queries. To quantify the current progress, we propose a simple text2image method, representing a novel query by a set of images selected from large-scale query log. Consequently, computing cross-media similarity between the query and a given image boils down to comparing the visual similarity between the given image and the selected images. Image retrieval experiments on the challenging Clickture dataset show that the proposed text2image is a strong baseline, comparing favorably to recent deep learning alternatives. Copyright © 2017, The Authors. All rights reserved.

关键词： Image retrieval

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Creating knowledge and Wisdom via Big data Analytics: Preface for ITQM 2017

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Procedia Computer Science 2017年 122卷 1-9页

作者： Ahuja, Vandana Shi, Yong Khazanchi, Deepak Abidi, Naseem Tian, Yingjie Berg, Daniel Tien, James M. Jaypee Business School A-10 Sector-62 Noida Uttar Pradesh201 307 India School of Economics and Management University of Chinese Academy of Sciences Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha Chinese Academy of Sciences OmahaNE68182 United States College of Engineering University of Miami Coral GablesFL33124 United States

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Predicting visual features from text for image and video caption retrieval

arXiv

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arXiv 2017年

作者： Dong, Jianfeng Li, Xirong Snoek, Cees G.M. College of Computer Science and Technology Zhejiang University Hangzhou310027 China Key Lab of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing100872 China Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands

This paper strives to find amidst a set of sentences the one best describing the content of a given image or video. Different from existing works, which rely on a joint subspace for their image and video caption retrieval, we propose to do so in a visual space exclusively. Apart from this conceptual novelty, we contribute Word2VisualVec, a deep neural network architecture that learns to predict a visual feature representation from textual input. Example captions are encoded into a textual embedding based on multi-scale sentence vectorization and further transferred into a deep visual feature of choice via a simple multi-layer perceptron. We further generalize Word2VisualVec for video caption retrieval, by predicting from text both 3-D convolutional neural network features as well as a visual-audio representation. Experiments on Flickr8k, Flickr30k, the Microsoft Video Description dataset and the very recent NIST TrecVid challenge for video caption retrieval detail Word2VisualVec's properties, its benefit over textual embeddings, the potential for multimodal query composition and its state-of-the-art results. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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