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IEEE International Conference on Multimedia and Expo (ICME)

作者： Deqiang Kong Jingyuan Tang Zhenfeng Zhu Jian Cheng Yao Zhao Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China Institute of Information Science Beijing Jiaotong University Beijing China CASIA National Lab of Pattern Recognition Beijing China

ISBN: (纸本)9781509060689

Personalized recommendation aims to use the historical behavior of users to recommend new items that are likely to be of interest to them. Due to a tiny improvement of it can lead to a huge profits, lots of giant e-commerce companies, such as Amazon, Alibaba and eBay, have put their great effort on this field. In this paper, we formulate the problem of personalized recommendation as a tree based regression task and present a de-biased DART ensemble model. To generate compact personalized profile of user, a decision tree based sparse encoding approach is first proposed to refine those raw behaviors, like clicks, views, and purchases from user-item interaction. Considering the problem of class imbalance in training the predictive models, resampling based de-biased DART model is utilized to form multiple weak predictors for recommendation. Thus, based on these learnt weak predictors, a stronger predictor can be built. Experimental results on real commercial dataset demonstrate the effectiveness of the proposed personalized recommendation model.

关键词： Decision trees Vegetation Encoding Predictive models Companies Correlation Market research

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Characteristics analysis and optimization design of entities collaboration for cloud manufacturing

Characteristics analysis and optimization design of entities...

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作者： Li, Wenxiang Zhu, Chunsheng Wei, Xia Rodrigues, Joel J.P.C. Wang, Kun School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Instituto de Telecomunicações University of Beira Interior Covilhã Portugal Santa Rita do SapucaíMG Brazil University of Salamanca Salamanca Spain Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing China University ITMO St. Petersburg Russia

By applying the cloud manufacturing paradigm in the regional enterprise cluster, the enterprises or facilities may collaborate extensively for efficiently utilizing the manufacturing resources. It is valuable to explore and design the strategies of facilities selection for the autonomic control of the collaboration behaviors in production. In this paper, we model the collaboration relations in the regional enterprise cluster as a generalized social collaboration network and explore the dynamic growth process of the Facilities Collaboration network for different strategies of facilities selection, including the random selection with and without preference, and the balanced selection with and without preference. With performance indexes such as network size, the distribution of node degree and act degree, clustering coefficient, the average shortest distance, and the number of n-cliques, we present and analyze the characteristics of these strategies for cloud manufacturing. Next, based on these characteristics, we propose 2 mechanisms for self-optimization in facilities collaboration, including the dynamic weighing of facilities and the concentrated processing of successive subtasks in the process. We also analyze the mechanisms' effects on the characteristics of Facilities Collaboration network and the performance in manufacturing. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Dynamics

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Analysis of the paragraph vector model for information retrieval 16

Analysis of the paragraph vector model for information retri...

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2016 ACM International Conference on the Theory of Information Retrieval, ICTIR 2016

作者： Ai, Qingyao Yang, Liu Guo, Jiafeng Croft, W. Bruce College of Information and Computer Sciences University of Massachusetts Amherst AmherstMA United States CAS Key Lab. of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781450344975

Previous studies have shown that semantically meaningful representations of words and text can be acquired through neural embedding models. In particular, paragraph vector (PV) models have shown impressive performance in some natural language processing tasks by estimating a document (topic) level language model. Integrating the PV models with traditional language model approaches to retrieval, however, produces unstable performance and limited improvements. In this paper, we formally discuss three intrinsic problems of the original PV model that restrict its performance in retrieval tasks. We also describe modifications to the model that make it more suitable for the IR task, and show their impact through experiments and case studies. The three issues we address are (1) the unregulated training process of PV is vulnerable to short document overfitting that produces length bias in the final retrieval model;(2) the corpus-based negative sampling of PV leads to a weighting scheme for words that overly suppresses the importance of frequent words;and (3) the lack of word-context information makes PV unable to capture word substitution relationships. © 2016 ACM.

关键词： Natural language processing systems

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Advancing image understanding in poor visibility environments: A collective benchmark study

arXiv

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arXiv 2019年

作者： Yang, Wenhan Yuan, Ye Ren, Wenqi Liu, Jiaying Scheirer, Walter J. Wang, Zhangyang Zhang, Taiheng Zhong, Qiaoyong Xie, Di Pu, Shiliang Zheng, Yuqiang Qu, Yanyun Xie, Yuhong Chen, Liang Li, Zhonghao Hong, Chen Jiang, Hao Yang, Siyuan Liu, Yan Qu, Xiaochao Wan, Pengfei Zheng, Shuai Zhong, Minhui Su, Taiyi He, Lingzhi Guo, Yandong Zhao, Yao Zhu, Zhenfeng Liang, Jinxiu Wang, Jingwen Chen, Tianyi Quan, Yuhui Xu, Yong Liu, Bo Liu, Xin Sun, Qi Lin, Tingyu Li, Xiaochuan Lu, Feng Gu, Lin Zhou, Shengdi Cao, Cong Zhang, Shifeng Chi, Cheng Zhuang, Chubin Lei, Zhen Li, Stan Z. Wang, Shizheng Liu, Ruizhe Yi, Dong Zuo, Zheming Chi, Jianning Wang, Huan Wang, Kai Liu, Yixiu Gao, Xingyu Chen, Zhenyu Guo, Chang Li, Yongzhou Zhong, Huicai Huang, Jing Guo, Heng Yang, Jianfei Liao, Wenjuan Yang, Jiangang Zhou, Liguo Feng, Mingyue Qin, Likun Wangxuan Institute of Computer Technology Peking University Beijing100080 China Department of Computer Science and Engineering Texas A&M University TX77843 United States State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Mechanical Engineering Zhejiang University Hangzhou310027 China Hikvision Research Institute Hangzhou310051 China Mtlab Meitu Inc. Beijing100080 China Insitute of Information Science Beijing Jiaotong University Beijing100044 China Department of Computer Science and Technology Tongji University Shanghai201804 China XPENGMOTORS Beijing China School of Computer Science and Engineering at South China University of Technology Guangzhou510006 China Tencent AI Lab Shenzhen518000 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Northeastern University Shenyang110819 China Nanyang Technological University Singapore639798 Big Data Center State Grid Corporation of China Beijing China Winsense Inc. Beijing100080 China University of Chinese Academy of Sciences Beijing100049 China Department of Informatics Technical University of Munich Garching85748 Germany Institute of Microelectronics Chinese Academy of Sciences Beijing100190 China China Electric Power Research Institute Beijing100031 China Westlake University Hangzhou310024 China Department of Computer Science Durham University Durham United Kingdom Australian National University ActonACT0200 Australia Sunway-AI Co. Ltd Zhuhai China Chinese Academy of Sciences R&D Center for Internet of Things Wuxi214200 China

Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG2+challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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Constructing a science of Cyber-Resilience for Military Systems

Constructing a Science of Cyber-Resilience for Military Syst...

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2017 NATO IST-153/RWS-21 Workshop on Cyber Resilience, IST-153 2017

作者： Alexeev, Alexander Henshel, Diane S. Levitt, Karl McDaniel, Patrick Rivera, Brian Templeton, Steven Weisman, Mike Data Analysis and Modeling Indiana University School of Public and Environmental Affairs 1315 E 10th Street BlooomingtonIN47405 Indiana University School of Public and Environmental Affairs 1315 E 10th Street BloomingtonIN47405 Computer Science College of Engineering University of California Davis 1 Shields Ave. DavisCA05616 School of Electrical Engineering and Computer Science Pennsylvania State University Information Sciences and Technology Building University ParkPA16802-6823 Network Science Division Tactical Network Assurance Branch U.S. Army Research Laboratory 2800 Powder Mill Road AdelphiMD20783 Computer Security Research Lab Department of Computer Science University of California Davis One Shields Avenue DavisCA95616 Network Science Division Network Security Branch U.S. Army Research Laboratory ICF International 2800 Powder Mill Road AdelphiMD20783

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An Integrated Tone Mapping for High Dynamic Range Image Visualization

An Integrated Tone Mapping for High Dynamic Range Image Visu...

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作者： Lei Liang Jeng-Shyang Pan Yongjun Zhuang Department of Computer Science and Technology Shenzhen Graduate School Harbin Institute of Technology Qihan Research Qihan Technology Co. LTD Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Techology

There are two type tone mapping operators for high dynamic range(HDR) image visualization. HDR image mapped by perceptual operators have strong sense of reality, but will lose local details. Empirical operators can maximize local detail information of HDR image, but realism is not strong. A common tone mapping operator suitable for all applications is not available. This paper proposes a novel integrated tone mapping framework which can achieve conversion between empirical operators and perceptual operators. In this framework, the empirical operator is rendered based on improved saliency map, which simulates the visual attention mechanism of the human eye to the natural scene. The results of objective evaluation prove the effectiveness of the proposed solution.

关键词： image An Integrated Tone Mapping for High Dynamic Range Image Visualization HDR Mapping

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A Performance Evaluation of Classification Algorithms for Big data

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Procedia Computer science 2017年 122卷 1100-1107页

作者： Mo Hai You Zhang Yuejin Zhang School of Information Central University of Finance and Economics Beijing China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China School of Information Systems &Management Heinz College Carnegie Mellon University Pittsburgh PA 1521 USA

The performance of two typical classification algorithms in Spark: random forest and naïve bayes are evaluated by using four metrics: classification accuracy, speedup, scaleup and sizeup. Experiments are performed on dataset and clusters of different scale. The results show that: (1) the accuracy of the two algorithms is high; (2) the increase of speedup is not linear. For the dataset with different size, the numbers of nodes is different when the speedup is the maximal; (3) the scaleup of random forest reaches its peak when the number of nodes is 2, and after that the scaleup decreases with the increase of the number of nodes;(4) for random forest, when the number of nodes is 2, the sizeup increases sharply with the increase of the size of dataset, and when the number of nodes is greater than 2, the sizeup increases more slowly with the increase of the size of dataset; for naïve bayes, when the number of nodes is smaller than 6, the sizeup increases with the increase of the size of dataset, when number of nodes is 6 and the size of dataset is larger than that of Sogou_5, the change of the sizeup is not obvious with the increase of the size of dataset.

关键词： Spark Random forest Naïve bayes Classification accuracy Speedup Scaleup Sizeup

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An Improved Algorithm for Multivariate Polynomial Interpolation

An Improved Algorithm for Multivariate Polynomial Interpolat...

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2017 Asia-Pacific Computer science and Application Conference

作者： Min Tang Guoqiang Deng School of Mathematics and Computing Science Guangxi Key Laboratory of Cryptography and Information SecurityGuilin University of Electronic Technology Shanghai Key Lab of Trustworthy Computing East China Normal University School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationGuilin University of Electronic Technology

In this paper,we present an improved method based on Zippel's algorithm for multivariate polynomial interpolation,and a testing point technique is proposed to verify the recovered *** with Zippel's algorithm,the new method not only returns an exact target polynomial but takes less computation time as *** experiments show the effectiveness of the improved method.

关键词： multivariate polynomial interpolation Zippel's algorithm testing point

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An Improved Algorithm for Multivariate Polynomial Interpolation

An Improved Algorithm for Multivariate Polynomial Interpolat...

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第二届亚太计算机科学与应用学术会议

In this paper, we present an improved method based on Zippel's algorithm for multivariate polynomial interpolation, and a testing point technique is proposed to verify the recovered polynomial. Compared with Zippel's algorithm, the new method not only returns an exact target polynomial but takes less computation time as well. The experiments show the effectiveness of the improved method.

关键词： multivariate polynomial interpolation Zippel's algorithm testing point

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Investigation on Automatic data Acquisition of Well Logging Curve based on Color Recognition

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Journal of Physics: Conference Series 2019年第2期1237卷

作者： Han Wang Zhihui Ye Dong Chen Dandan Zhu Shouding Li College of Safety and Ocean Engineering China University of PetroleumBeijing Beijing 102249 China State Key Laboratory of Petroleum Resources and Prospecting College of Petroleum Engineering China University of PetroleumBeijing Beijing 102249 China Beijing Key Lab of Petroleum Data Mining Department of Computer Science and Technology Beijing 102249 China Institute of Geology and Geophysics Chinese Academy of Sciences Key Laboratory of Shale Gas and Geoengineering Institute of Geology and Geophysics Chinese Academy of Sciences Beijing 100029 China University of Chinese Academy of Sciences Beijing 100083 China Institutions of Earth Science Chinese Academy of Sciences Beijing 100029 China

Well logging data are important to evaluate rock and fluid properties underground. Many well logging data, as functions of well depth, are conserved as figures in the literature. Digitizing technics should be applied when utilizing the well logging data. In order to address this problem, a method was proposed to automatically identify and acquire the well logging data from well logging curves. Based on the color recognition, this algorithm is able to extract curve features based on pixels. Comparing to other data digitizing methods, the background grid lines could be easily filtered and removed. The algorithm is verified by digitizing some typical well logging curves. This main contribution of this work is to provide a new and efficient way for data digitizing from well logging curves.

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