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A dynamic mining algorithm for multi-granularity user’s learning preference based on ant colony optimization 1

2nd IFIP TC 12 International Conference on Intelligence science, ICIS 2017

作者： Liu, Shengjun Chen, Shengbing Meng, Hu Anhui USTC-GZ Information Technology Co. Ltd. Hefei230031 China Key Lab of Network and Intelligent Information Processing Department of Computer Science and Technology Hefei University Hefei230601 China HEFEI City Cloud Data Center Co. Ltd. Hefei230094 China

ISBN: (数字)9783319681214

ISBN: (纸本)9783319681207

Mining user’s learning preference is one of the key issues in the personalized online learning system, which is of great significance technology for modern educational. In this paper, using the hierarchical characteristics of the knowledge points in the course domain, we defined the equivalence relation and equivalence of knowledge points, and defined the structure of the knowledge points quotient space. Then, the functions of support, pheromone concentration and preference were defined on various levels, and an improved ant colony optimization was proposed to handle the multi granularity data structure of quotient space. An algorithm of multi-granularity Learning Preference Mining based on Ant Colony Optimization (ACO-LPM) was proposed to address the problems about too many learning knowledge points and too few user’s test data in the online personalized learning system. The pheromone has the characteristic of dynamic evaporation, so, the preference patterns mined by ACO-LPM can be changed with the change of user interest in real time. The experimental results show that the algorithm can mining the user’s learning preferences in online learning system effectively and efficiently. © IFIP International Federation for Information Processing 2017.

关键词： Ant colony optimization

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Model inconsistent but correlated noise: Multi-view subspace learning with regularized Mixture of Gaussians

arXiv

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arXiv 2018年

作者： Yong, Hongwei Meng, Deyu Li, Jinxing Zuo, Wangmeng Zhang, Lei Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xian Jiaotong University School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China

Multi-view subspace learning (MSL) aims to find a low-dimensional subspace of the data obtained from multiple views. Different from single view case, MSL should take both common and specific knowledge among different views into consideration. To enhance the robustness of model, the complexity, non-consistency and similarity of noise in multi-view data should be fully taken into consideration. Most current MSL methods only assume a simple Gaussian or Laplacian distribution for the noise while neglect the complex noise configurations in each view and noise correlations among different views of practical data. To this issue, this work initiates a MSL method by encoding the multi-view-shared and single-view-specific noise knowledge in data. Specifically, we model data noise in each view as a separated Mixture of Gaussians (MoG), which can fit a wider range of complex noise types than conventional Gaussian/Laplacian. Furthermore, we link all single-view-noise as a whole by regularizing them by a common MoG component, encoding the shared noise knowledge among them. Such regularization component can be formulated as a concise KL-divergence regularization term under a MAP framework, leading to good interpretation of our model and simple EM-based solving strategy to the problem. Experimental results substantiate the superiority of our method. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

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Influence maximization with ε-almost submodular threshold functions 17

Influence maximization with ε-almost submodular threshold f...

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Proceedings of the 31st International Conference on Neural Information Processing Systems

作者： Qiang Li Wei Chen Xiaoming Sun Jialin Zhang CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Microsoft Research

ISBN: (纸本)9781510860964

Influence maximization is the problem of selecting k nodes in a social network to maximize their influence spread. The problem has been extensively studied but most works focus on the submodular influence diffusion models. In this paper, motivated by empirical evidences, we explore influence maximization in the non-submodular regime. In particular, we study the general threshold model in which a fraction of nodes have non-submodular threshold functions, but their threshold functions are closely upper- and lower-bounded by some submodular functions (we call them ε-almost submodular). We first show a strong hardness result: there is no l/nγ/c approximation for influence maximization (unless P = NP) for all networks with up to nγ ε-almost submodular nodes, where γ is in (0,1) and c is a parameter depending on ε. This indicates that influence maximization is still hard to approximate even though threshold functions are close to submodular. We then provide (l - ε)ℓ(1 - 1/ε) approximation algorithms when the number of ε-almost submodular nodes is ℓ. Finally, we conduct experiments on a number of real-world datasets, and the results demonstrate that our approximation algorithms outperform other baseline algorithms.

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Cubic medium field equation public key cryptosystem

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International Journal of network Security 2018年第3期20卷 472-477页

作者： Lu, Gang Xue, Linyuan Nie, Xuyun Qin, Zhiguang Liu, Bo School of Information and Software Engineering University of Electronic Science and Technology of China 4 Jianshe North Rd 2nd Section Chenghua Qu Chengdu610054 China State Key Laboratory of Information Security Institute of Information Engineering Beijing100093 China Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China

Medium Field Equation (MFE) multivariate public key cryptosystems were broken by High Order Linearization Equation (HOLE) attack. In order to avoid HOLE attack, we proposed an improvement of MFE, Cubic MFE public key encryption scheme. In our construction, multiplications of three second order matrices were used to get a set of cubic polynomials in the central map. Through theoretical analysis and computer experiments, the Cubic MFE is shown to be secure against HOLE attack and other existing attacks. © International Journal of network Security.

关键词： Linearization

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Dynamic priority algorithm for modern tram based on MADM 2

Dynamic priority algorithm for modern tram based on MADM

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2nd IEEE International Conference on Intelligent Transportation Engineering, ICITE 2017

作者： Wu, Jiaqi Yu, Yang Lin, Xiaoyong College of Transportation Science and Technology Nanjing Tech University Nanjing210009 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing210003 China

ISBN: (纸本)9781509062720

Analyzing the influence on traffic flow at intersection according to the driving characteristics of modern tram with different lane layout types. Proposing a method of dynamic priority algorithm (DPA) based on the idea of multiple attribute decision making (MADM) to analyze intersection delay. The VAP module based on MATlab in Vissim is used to simulate the induction control of the modern tram through the concrete intersection, mainly analyzing delay of the intersection with different lane layout types of modern trams. Taking the intersection of Middle Jiangdong Road and Hexi Avenue in Nanjing as an example, the traffic delay caused by different lane layout of modern tram is analyzed by using absolute priority and dynamic priority algorithm (DPA) respectively. © 2017 IEEE.

关键词： MATlab

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Secure Medical data Collection via Local Differential Privacy

Secure Medical Data Collection via Local Differential Privac...

引用

IEEE International Conference on Computer and Communications

作者： Zhiqiang Wang Pingchuan Ma Ruming Wang Jianyi Zhang Yaping Chi Yanzhe Ma Tao Yang Information Technology Research Base of Civil Aviation Administration of China Civil Aviation University of China Tianjin China Beijing Electronic Science and Technology Institute Beijing China Hainan University Haikou China Ministry of Public Security Key Lab of Information Network Security Shanghai China

ISBN: (纸本)9781538683408

As the volume of medical data mining increases, so do the need to preserve patient privacy. And the exposure of medical data may degrade the level of health care service and reduce the trust of patients. Local Differential Privacy (LDP) was proposed to solve problems in the context of local privacy, by which data collectors are hardly to get exact individual information. We present a secure medical data collection framework and apply our framework on synthetic data at different scale. Finally, we evaluate the performance of our work. Our experimental results show that both privacy and validity of medical data are aligned.

关键词： Privacy Diseases Differential privacy Security Hospitals Diabetes

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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GRB 221009A: the B.O.A.T Burst that Shines in Gamma Rays

arXiv

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arXiv 2024年

作者： Axelsson, M. Ajello, M. Arimoto, M. Baldini, L. Ballet, J. Baring, M.G. Bartolini, C. Bastieri, D. Becerra Gonzalez, J. Bellazzini, R. Berenji, B. Bissaldi, E. Blandford, R.D. Bonino, R. Bruel, P. Buson, S. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Cheung, C.C. Chiaro, G. Cibrario, N. Ciprini, S. Cozzolongo, G. Cristarella Orestano, P. Crnogorcevic, M. Cuoco, A. Cutini, S. Ammando, F.D. De Gaetano, S. di Lalla, N. Dinesh, A. Di Tria, R. Di Venere, L. Domínguez, A. Fegan, S.J. Ferrara, E.C. Fiori, A. Franckowiak, A. Fukazawa, Y. Funk, S. Fusco, P. Galanti, G. Gargano, F. Gasbarra, C. Germani, S. Giacchino, F. Giglietto, N. Giliberti, M. Gill, R. Giordano, F. Giroletti, M. Granot, J. Green, D. Grenier, I.A. Guiriec, S. Gustafsson, M. Hashizume, M. Hays, E. Hewitt, J.W. Horan, D. Kayanoki, T. Kuss, M. Laviron, A. Li, J. Liodakis, I. Longo, F. Loparco, F. Lorusso, L. Lott, B. Lovellette, M.N. Lubrano, P. Maldera, S. Malyshev, D. Manfreda, A. Martí-Devesa, G. Martinelli, R. Martinez Castellanos, I. Mazziotta, M.N. McEnery, J.E. Mereu, I. Meyer, M. Michelson, P.F. Mirabal, N. Mitthumsiri, W. Mizuno, T. Monti-Guarnieri, P. Monzani, M.E. Morishita, T. Morselli, A. Moskalenko, I.V. Negro, M. Niwa, R. Omodei, N. Orienti, M. Orlando, E. Paneque, D. Panzarini, G. Persic, M. Pesce-Rollins, M. Petrosian, V. Pillera, R. Piron, F. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Ryde, F. Sánchez-Conde, M. Saz Parkinson, P.M. Serini, D. Sgrò, C. Sharma, V. Siskind, E.J. Spandre, G. Spinelli, P. Suson, D.J. Tajima, H. Tak, D. Thayer, J.B. Torres, D.F. Valverde, J. Zaharijas, G. Lesage, S. Briggs, M.S. Burns, E. Bala, S. Bhat, P.N. Cleveland, W.H. Dalessi, S. de Barra, C. Gibby, M. Giles, M.M. Hamburg, R. Hristov, B.A. Hui, C.M. Kocevski, D. Mailyan, B. Malacaria, C. McBreen, S. Poolakkil, S. Roberts, O.J. Scotton, L. Veres, P. von Kienlin, A. Wilson-Hodge, C.A. Wood, J. Department of Physics KTH Royal Institute of Technology AlbaNova StockholmSE-106 91 Sweden The Oskar Klein Centre for Cosmoparticle Physics AlbaNova StockholmSE-106 91 Sweden Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Faculty of Mathematics and Physics Institute of Science and Engineering Kanazawa University Kakuma Ishikawa Kanazawa920-1192 Japan Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-YvetteF-91191 Cedex France Rice University Department of Physics and Astronomy MS-108 P. O. Box 1892 HoustonTX77251 United States Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy Università degli studi di Trento via Calepina 14 Trento38122 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova Via F. Marzolo 8 PadovaI-35131 Italy Center for Space Studies and Activities "G. Colombo" University of Padova Via Venezia 15 PadovaI-35131 Italy Instituto de Astrofísica de Canarias Universidad de La Laguna Dpto. Astrofísica La Laguna Tenerife 38200 Spain Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy California State University Los Angeles Department of Physics and Astronomy Los AngelesCA90032 United States Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari via Amendola 173 BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Laboratoire Leprince-Ringuet CNRS IN2P3 Éc

We present a complete analysis of Fermi Large Area Telescope (LAT) data of GRB 221009A, the brightest Gamma-Ray Burst (GRB) ever detected. The burst emission above 30 MeV detected by the LAT preceded by 1 s the low-en... 详细信息

关键词： Gamma rays

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Sparse DNNs with improved adversarial robustness 18

Sparse DNNs with improved adversarial robustness

引用

Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Yiwen Guo Chao Zhang Changshui Zhang Yurong Chen Intel Labs China Academy for Advanced Interdisciplinary Studies Center for Data Science Peking University Institute for Artificial Intelligence Tsinghua University (THUAI) State Key Lab of Intelligent Technologies and Systems Beijing National Research Center for Information Science and Technology (BNRis) Department of Automation Tsinghua University

Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting dense models into sparse ones, pruning appears to be a promising solution to reducing the computation/memory cost. This paper studies classification models, especially DNN-based ones, to demonstrate that there exists intrinsic relationships between their sparsity and adversarial robustness. Our analyses reveal, both theoretically and empirically, that nonlinear DNN-based classifiers behave differently under l2 attacks from some linear ones. We further demonstrate that an appropriately higher model sparsity implies better robustness of nonlinear DNNs, whereas over-sparsified models can be more difficult to resist adversarial examples.

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Part-based visual tracking via structural support correlation filter

arXiv

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arXiv 2018年

作者： Ji, Zhangjian Feng, Kai Qian, Yuhua School of Computer and Information Technology Shanxi University Taiyuan China Key Lab. of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China

Recently, part-based and support vector machines (SVM) based trackers have shown favorable performance. Nonetheless, the time-consuming online training and updating process limit their real-time applications. In order to better deal with the partial occlusion issue and improve their efficiency, we propose a novel part-based structural support correlation filter tracking method, which absorbs the strong discriminative ability from SVM and the excellent property of part-based tracking methods which is less sensitive to partial occlusion. Then, our proposed model can learn the support correlation filter of each part jointly by a star structure model, which preserves the spatial layout structure among parts and tolerates outliers of parts. In addition, to mitigate the issue of drift away from object further, we introduce inter-frame consistencies of local parts into our model. Finally, in our model, we accurately estimate the scale changes of object by the relative distance change among reliable parts. The extensive empirical evaluations on three benchmark datasets: OTB2015, TempleColor128 and VOT2015 demonstrate that the proposed method performs superiorly against several state-of-the-art trackers in terms of tracking accuracy, speed and robustness. Copyright © 2018, The Authors. All rights reserved.

关键词： Support vector machines

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