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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

Deep User Modeling for Content-based Event Recommendation in Event-based Social Networks

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Deep User Modeling for Content-based Event Recommendation in...

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Zhibo Wang Yongquan Zhang Honglong Chen Zhetao Li Feng Xia Jiangsu Key Lab. of Big Data Security & Intelligent Processing NJUPT P. R. China School of Cyber Science and Engineering Wuhan University P. R. China College of Information and Control Engineering China University of Petroleum P. R. China College of Information Engineering Xiangtan University P. R. China School of Software Dalian University of Technology P. R. China

ISBN: (纸本)9781538641293

Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, ***, and the experimental results show that DUMER outperforms the compared benchmarks.

关键词： Recommender systems Machine learning Social network services Feature extraction Context modeling Conferences Probabilistic logic

Effect analysis of factors based on neural network in non-contact electrostatic discharge

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Effect analysis of factors based on neural network in non-co...

IEEE International Symposium on Electromagnetic Compatibility (EMC-Beijing)

作者： Huang Jun Ruan Fangming Su Ming Wang Heng Yang Xiangdong David Pommerenke Beijing Oriental Institute of Metrology and Test Beijing China Department of Big Data and Electronic Sciences Guizhou Normal University Guiyang China EMC Lab of Missouri University of Science & Technology US

Discharge parameters in non-contact electrostatic discharge(ESD) are affected by various factors, including electrode moving speed to the target, gas pressure, temperature, humidity. Mechanism of non-contact electrostatic discharge was analyzed based on a neural network model and compared to current waveform in non-contact electrostatic discharge measured with new measurement system of electrostatic discharge. Neural network method was used to adjust the weight of discharge parameters in non-contact electrostatic discharge based on discharge current waveform affected by electrode moving speed, gas pressure, temperature and humidity, so as to compare with the experiment results waveforms met to the requirement of international standard IEC61000-4-2, and to analyze the main parameters that affect discharge currents in non-contact electrostatic discharge events.

关键词： Discharges (electric) Electrostatic discharges Fitting Electrodes Neurons Standards Neural networks

A Margin-based MLE for Crowdsourced Partial Ranking

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arXiv 2018年

作者： Xu, Qianqian Xiong, Jiechao Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Cas Beijing100190 Tencent Ai Lab Shenzhen518057 School of Mathematical Sciences Peking University Beijing100871 China DeepWise Ai Lab Beijing100085 Inst. of Info. Engin. Cas Beijing100093 China University of Chinese Academy of Sciences Beijing100049 China Key Lab of Big Data Mining and Knowledge Management Cas Beijing100190 Department of Mathematics Hong Kong University of Science and Technology Hong Kong Hong Kong

A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order. However, in real-world scenarios, some items are intrinsically ambiguous in comparisons, which may very well be an inherent uncertainty of the data. In this case, the conventional total order ranking can not capture such uncertainty with mere global ranking or utility scores. In this paper, we are specifically interested in the recent surge in crowdsourcing applications to predict partial but more accurate (i.e., making less incorrect statements) orders rather than complete ones. To do so, we propose a novel framework to learn some probabilistic models of partial orders as a margin-based Maximum Likelihood Estimate (MLE) *** prove that the induced MLE is a joint convex optimization problem with respect to all the parameters, including the global ranking scores and margin parameter. Moreover, three kinds of generalized linear models are studied, including the basic uniform model, Bradley-Terry model, and Thurstone-Mosteller model, equipped with some theoretical analysis on FDR and Power control for the proposed methods. The validity of these models are supported by experiments with both simulated and real-world datasets, which shows that the proposed models exhibit improvements compared with traditional state-ofthe-art algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise

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arXiv 2017年

作者： Qian, Chao Bian, Chao Jiang, Wu Tang, Ke Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Shenzhen Key Lab of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

In many real-world optimization problems, the objective function evaluation is subject to noise, and we cannot obtain the exact objective value. Evolutionary algorithms (EAs), a type of general-purpose randomized optimization algorithm, have been shown to be able to solve noisy optimization problems well. However, previous theoretical analyses of EAs mainly focused on noise-free optimization, which makes the theoretical understanding largely insufficient for the noisy case. Meanwhile, the few existing theoretical studies under noise often considered the one-bit noise model, which flips a randomly chosen bit of a solution before evaluation;while in many realistic applications, several bits of a solution can be changed simultaneously. In this paper, we study a natural extension of one-bit noise, the bit-wise noise model, which independently flips each bit of a solution with some probability. We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds. The analysis on LeadingOnes under bit-wise noise can be easily transferred to one-bit noise, and improves the previously known results. Since our analysis discloses that the (1+1)-EA can be efficient only under low noise levels, we also study whether the sampling strategy can bring robustness to noise. We prove that using sampling can significantly increase the largest noise level allowing a polynomial running time, that is, sampling is robust to noise. Copyright © 2017, The Authors. All rights reserved.

关键词： Computational complexity

Evaluating the data visualization for demanding marine operations 16

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Evaluating the data visualization for demanding marine opera...

16th Techno-Ocean, Techno-Ocean 2016

作者： Zhuge, Xu Wang, Hao Strazdins, Girts Big Data Lab Faculty of Engineering and Natural Sciences Norwegian University of Science and Technology Postboks 1517 Aalesund6025 Norway

ISBN: (纸本)9781509024452

Complexity and specificity of monitoring sensor data from marine operations brought a majority of challenges of how to evaluate the visualizations of such sensor data. It's urgently needed to develop and implement new evaluation methods to assess the visualization of maritime data. In this paper, we explore both quantitative and qualitative evaluation approaches to define evaluative metrics of our interactive visualization techniques, and present a case study carrying out by domain researches in real setting. This work is an important part to complement the visual analytics for maritime operations. © 2016 IEEE.

关键词： data visualization

Investigating how well contextual features are captured by bi-directional recurrent neural network models

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arXiv 2017年

作者： Chawla, Kushal Sahu, Sunil Kumar Anand, Ashish Adobe Research Big Data Experience Lab BangaloreKarnataka India National Center for Text Mining University of Manchester United Kingdom Department of Computer Science and Engineering IIT GuwahatiAssam India

Learning algorithms for natural language processing (NLP) tasks traditionally rely on manually defined relevant contextual features. On the other hand, neural network models using an only distributional representation of words have been successfully applied for several NLP tasks. Such models learn features automatically and avoid explicit feature engineering. Across several domains, neural models become a natural choice specifically when limited characteristics of data are known. However, this flexibility comes at the cost of interpretability. In this paper, we define three different methods to investigate ability of bi-directional recurrent neural networks (RNNs) in capturing contextual features. In particular, we analyze RNNs for sequence tagging tasks. We perform a comprehensive analysis on general as well as biomedical domain datasets. Our experiments focus on important contextual words as features, which can easily be extended to analyze various other feature types. We also investigate positional effects of context words and show how the developed methods can be used for error analysis. Copyright © 2017, The Authors. All rights reserved.

关键词： Recurrent neural networks

The Past, Present, and Future of the Brain Imaging data Structure (BIDS)

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arXiv 2023年

作者： Poldrack, Russell A. Markiewicz, Christopher J. Appelhoff, Stefan Ashar, Yoni K. Auer, Tibor Baillet, Sylvain Bansal, Shashank Beltrachini, Leandro Bénar, Christian G. Bertazzoli, Giacomo Bhogawar, Suyash Blair, Ross W. Bortoletto, Marta Boudreau, Mathieu Brooks, Teon L. Calhoun, Vince D. Castelli, Filippo Maria Clement, Patricia Cohen, Alexander L. Cohen-Adad, Julien D'Ambrosio, Sasha de Hollander, Gilles de la Iglesia-Vayá, María de la Vega, Alejandro Delorme, Arnaud Devinsky, Orrin Draschkow, Dejan Duff, Eugene Paul DuPre, Elizabeth Earl, Eric Esteban, Oscar Feingold, Franklin W. Flandin, Guillaume Galassi, Anthony Gallitto, Giuseppe Ganz, Melanie Gau, Rémi Gholam, James Ghosh, Satrajit S. Giacomel, Alessio Gillman, Ashley G. Gleeson, Padraig Gramfort, Alexandre Guay, Samuel Guidali, Giacomo Halchenko, Yaroslav O. Handwerker, Daniel A. Hardcastle, Nell Herholz, Peer Hermes, Dora Honey, Christopher J. Innis, Robert B. Ioanas, Horea-Ioan Jahn, Andrew Karakuzu, Agah Keator, David B. Kiar, Gregory Kincses, Balint Laird, Angela R. Lau, Jonathan C. Lazari, Alberto Legarreta, Jon Haitz Li, Adam Li, Xiangrui Love, Bradley C. Lu, Hanzhang Maumet, Camille Mazzamuto, Giacomo Meisler, Steven L. Mikkelsen, Mark Mutsaerts, Henk Nichols, Thomas E. Nikolaidis, Aki Nilsonne, Gustav Niso, Guiomar Norgaard, Martin Okell, Thomas W. Oostenveld, Robert Ort, Eduard Park, Patrick J. Pawlik, Mateusz Pernet, Cyril R. Pestilli, Franco Petr, Jan Phillips, Christophe Poline, Jean-Baptiste Pollonini, Luca Raamana, Pradeep Reddy Ritter, Petra Rizzo, Gaia Robbins, Kay A. Rockhill, Alexander P. Rogers, Christine Rokem, Ariel Rorden, Chris Routier, Alexandre Saborit-Torres, Jose Manuel Salo, Taylor Schirner, Michael Smith, Robert E. Spisak, Tamas Sprenger, Julia Swann, Nicole C. Szinte, Martin Takerkart, Sylvain Thirion, Bertrand Thomas, Adam G. Torabian, Sajjad Varoquaux, Gael Voytek, Bradley Welzel, Julius Wilson, Martin Yarkoni, Tal Gorgolewski, Krzysztof J. Department of Psychology Stanford University StanfordCA United States Max Planck Institute for Human Development Berlin Germany University of Colorado Anschutz Medical Campus AuroraCO United States School of Psychology University of Surrey Guildford United Kingdom McConnell Brain Imaging Centre Montréal Neurological Institute McGill University Montréal Canada Department of Bioengineering University of California San Diego La Jolla CA United States School of Physics Astronomy Cardiff University Wales United Kingdom Institut de Neurosciences des Systèmes Marseille France Neurophysiology Lab IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy Center for Mind/Brain Sciences - CIMeC University of Trento TN Rovereto Italy Brigham and Women’s Hospital BostonMA United States Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States Rackspace Technology San AntonioTX United States NeuroPoly Lab Polytechnique Montréal MontréalQC Canada Georgia State Georgia Tech Emory AtlantaGA United States University of Florence Sesto Fiorentino Italy Bioretics srl Cesena Italy Department of Medical Imaging Ghent University Hospital Ghent Belgium Department of Diagnostic Sciences Ghent University Ghent Belgium Department of Neurology Boston Children's Hospital BostonMA United States Dipartimento di Scienze della Salute dell'Università degli Studi di Milano Milan Italy Department of Clinical and Experimental Epilepsy University College London United Kingdom Zurich Center for Neuroeconomics Department of Economics University of Zurich Zurich Switzerland UMIB-FISABIO Valencia Spain The University of Texas at Austin AustinTX United States SCCN University of California San Diego La Jolla CA United States Department of Neurology NYU Langone Medical Center New YorkNY United States Department of Experimental Psychology University of Oxford Oxford United Kingdom UK Dementia Research Ins

The Brain Imaging data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS. © 2023, CC BY.

关键词： data structures

Uniform Information Exchange in Multi-channel Wireless Ad Hoc Networks 17

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Uniform Information Exchange in Multi-channel Wireless Ad Ho...

International Conference on Autonomous Agents and Multiagent Systems

作者： Dongxiao Yu Li Ning Yong Zhang Hai Jin Yuexuan Wang Francis C. M. Lau Shengzhong Feng Services Computing Technology and System Lab Big Data Technology and System Lab Clusters and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Center for High Performance Computing Shenzhen Institutes of Advanced Technology CAS Department of Computer Science The University of Hong Kong

ISBN: (纸本)9781510855076

Information exchange is a basic primitive for maintaining the smooth running of a network or a system with multiple communicating agents. Given k packets initially stored at k nodes respectively, the problem is to disseminate the k packets to the whole network with the objective of minimizing the time used. We study this problem in single-hop multi-channel networks of n nodes, and target on devising uniform distributed protocols that do not rely on any prior knowledge of network parameters, such as the network size n or the number of packet holders k. Uniform protocols have better scalability and are more suitable for implementation in reality Specifically, we propose a uniform distributed protocol that with high probability accomplishes the dissemination in O(k/F+F·log n) rounds, assuming F available channels. This protocol is asymptotically optimal when k is large (k ≥ F~2 log n), and provides the best possible linear speedup with multiple channels comparing with the results using a single channel. To the best of our knowledge, this is the first uniform protocol for information exchange in multi-channel networks.

关键词： Information exchange Multi-channel networks Distributed algorithm Uniform protocol information exchange distributed algorithms MULTICHANNEL Packet Uniform Uniforms Mobile ad hoc networks distributed protocol network parameters single-channel