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作者： Zhang, Mingwu Mu, Yi Shen, Jian Huang, Xinyi School of Computer Science Hubei University of Technology Wuhan430068 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University China School of Computing and Information Technology University of Wollongong NSW2522 Australia

A program obfuscator is a compiling algorithm that takes a program/circuit as input and generates a new garbled circuit to implement the same functionality as before while obtaining hard-to-understand in some sense, that is, infeasible to learn information from the garbled circuit. In order to obtain a practical application, an obfuscation should satisfy equivalent in functionality, polynomial slowdown in efficiency, and virtual black-box in security. In this paper, we model a stronger cryptographic obfuscation that does not only achieves the obfuscated circuit obscurity but also supports input re-key privacy. In order to implement the re-encryption obfuscation, we at first propose a key-privacy two-level encryption mechanism that implicitly supports the transformation from level-2 ciphertext into level-1 one, which provides an efficient method to re-encrypt the ciphertext without explicitly decryption procedure. Under the mechanism of two-level encryption and function of re-encryption, we construct an obfuscation of re-encryption that takes as input a probabilistic (keys) circuit and outputs a transformed circuit. We also give the proof that the obfuscator achieves the average-case security for the circuit family under the extended DBDH assumption and Decisional Linear assumption in the standard model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Timing circuits

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Performance analysis of parallel replica molecular dynamics

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AIP Conference Proceedings 2018年第1期1978卷

作者： Boyao Zhang Jue Wang Yangde Feng Ji Xiang Lei Wang 1Computer Network Information Center Chinese Academy of Science Department of High Performance Computing Technology and Application Development Beijing China 2University of Chinese Academy of Sciences Beijing China 3State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China 4School of cybersecurity Chinese Academy of Sciences China

Molecular dynamics simulation is an important tool for studying materials microstructure evolution under radiation effects. It is difficult to simulate atomic diffusion in materials owing to the time-scale limitations of the molecular dynamics method. Accelerated molecular dynamics has been developed as a solution and the parallel replica method is the simplest and most accurate of the accelerated dynamics techniques. The simulation time scale can reach several orders more than the direct molecular dynamics, while retaining the complete atomic detail. In parallel replica method, the entire system is replicated on each of M available parallel or distributed processors, it is in line with the characteristics of high-performance parallel computing and can be optimized from the program level.

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Learning to segment with image-level annotations

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Pattern Recognition 2016年 59卷 234-244页

作者： Wei, Yunchao Liang, Xiaodan Chen, Yunpeng Jie, Zequn Xiao, Yanhui Zhao, Yao Yan, Shuicheng Institute of information Science Beijing Jiaotong University Beijing 100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing 100044 China School of Advanced Computing Sun Yat-Sen University Guangzhou 510006 China Vision and Machine Learning Lab National University of Singapore 117583 Singapore People׳s Public Security University of China Beijing 100038 China

Recently, deep convolutional neural networks (DCNNs) have significantly promoted the development of semantic image segmentation. However, previous works on learning the segmentation network often rely on a large number of ground-truths with pixel-level annotations, which usually require considerable human effort. In this paper, we explore a more challenging problem by learning to segment under image-level annotations. Specifically, our framework consists of two components. First, reliable hypotheses based localization maps are generated by incorporating the hypotheses-aware classification and cross-image contextual refinement. Second, the segmentation network can be trained in a supervised manner by these generated localization maps. We explore two network training strategies for achieving good segmentation performance. For the first strategy, a novel multi-label cross-entropy loss is proposed to train the network by directly using multiple localization maps for all classes, where each pixel contributes to each class with different weights. For the second strategy, the rough segmentation mask can be inferred from the localization maps, and then the network is optimized based on the single-label cross-entropy loss with the produced masks. We evaluate our methods on the PASCAL VOC 2012 segmentation benchmark. Extensive experimental results demonstrate the effectiveness of the proposed methods compared with the state-of-the-arts. © 2016 Elsevier Ltd

关键词： Deep learning Semantic segmentation Weakly supervised

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An improved Fuzzy C-Means algorithm based on MapReduce

An improved Fuzzy C-Means algorithm based on MapReduce

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Qing Yu Zhimin Ding Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China

In order to solve the problem that the Fuzzy C-Means algorithm is sensitive to the initial clustering center, we use the Canopy algorithm to carry out the quick and rough clustering. At the same time, to avoid the blindness of the Canopy algorithm, we put forward an improved Canopy-FCM algorithm based on a max-min principle. In allusion to the problem that the FCM algorithm has high time complexity, this article use the parallel computing frame of MapReduce to design and realize the improved Canopy-FCM algorithm. Experimental results show: the improved Canopy-FCM algorithm based on MapReduce has better clustering quality and running speed than the Canopy-FCM and the FCM algorithm based on MapReduce, and the improved Canopy-FCM algorithm based on Hadoop has better speed-up ratio than the Canopy-FCM based on the Standalone mode.

关键词： Clustering algorithms Algorithm design and analysis Data collection Convergence Classification algorithms Blindness Time complexity

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Paralleled Extraction Method for Content of Invoice Image Based on TPL

Paralleled Extraction Method for Content of Invoice Image Ba...

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2015 International Conference on Advanced Management Science and Information Engineering(AMSIE 2015)

作者： H.Y.Jin B.Xing X.H.Hei School of Computer Science & Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

The paper mainly uses a mixed strategy of data and task parallelism to realize parallel extraction date information of *** at the deficiency of ordinary pipeline mode for parallel extraction date information from invoice,we explore a new method of load balanced pipeline based on multiple producers,using parallelism model of mixed data and task provided by TPL(the Task Parallel Library).Finally,it shows this load balanced pipeline program can almost get a linear speedup,which can reach 7.2x in a 8 cores system.

关键词： paralleled TPL pipeline load balanced

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Research on Storage and Retrieval Method of Mass Data for High-Speed Train

Research on Storage and Retrieval Method of Mass Data for Hi...

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International Conference on Computational Intelligence and security

作者： Bin Wang Fan Li Xinhong Hei Weigang Ma Lei Yu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China

In order to ensure the safeties of high-speed train, a lot of sensors are installed in the train to real-time monitor its operation status. These sensors collect amounts of data which may be several GBs for one time operation of one train. Such big amount data need to be stored and managed with high efficiency. In this paper, a hybrid storage architecture by combine database and file system is proposed, in which the separation on the row and column is performed respectively when storing the raw monitored data. Further, an improved compression algorithm is used to compress double float data in the monitored data. The simulation result shows that the proposed method is effective and feasible.

关键词： Memory Indexes File systems Monitoring Sensors Servers

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A method of oversized image generation based on stitching JPEG file streams

International Journal of Signal Processing, Image Processing...

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International Journal of Signal Processing, Image Processing and Pattern Recognition 2015年第8期8卷 159-168页

作者： Sun, Qindong Hua, Wu Tian, Minying Wang, Yan School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Network Computing and Security Xi’an University of Technology Xi’an China

Due to memory limitation, the displaying and processing of the oversized images have been a bottleneck for various applications, which is a hot research spot in the image processing field. The traditional methods are almost based on the BMP format to generate oversized images, of which the time and space complexity are very high. In this paper, we have deeply analyzed the encoding scheme of JPEG image and proposed an oversized image generation method based on stitching JPEG file streams. According to this method, the oversized images are block plotted and then transformed and saved as JPEG format files respectively. In the end, the block JPEG files are then combined into an integrated JPEG image. The experiment results show that, our method can effectively improve the speed of oversized image generation and reduce the memory consumption. The time and space performance of our method are better than existing methods. © 2015 SERSC.

关键词： Image enhancement

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Research on Identification and Addressing of the Internet of Things

Research on Identification and Addressing of the Internet of...

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International Conference on P2P, Parallel, Grid, Cloud and Internet computing (3PGCIC)

作者： Rui Ma Yue Liu Chun Shan Xiao Lin Zhao Xu An Wang School of Software Beijing Institute of Technology Beijing China Key Laboratory of Information and Network Security Engineering University of Chinese Armed Police Force Xi'an Shanxi China

ISBN: (纸本)9781467394741

It is a key issue that identification and addressing the entity which access the Internet through the wireless network with various short distance transmission protocols. To solve the problem, combining with the existing identification and addressing technology of the Internet and the IOT, this paper proposes a method based on distributed ID. This method can be divided into two stages. It first designs the structure of distributed ID. Using the distributed address allocation algorithm, it implements the automatic allocation of the distributed ID as well as the distributed ID resolution among the global IOT. After that, an addressing scheme is designed to meet demands of the IOT addressing. It first defines the structure of addressing and then implements the routing addressing algorithm which combines Cluster-Tree with Ad-hoc On-demand Distance Vector Routing. This scheme could improve the routing efficiency as well as achieve lower cost, lower energy consumption and higher reliability of the addressing. By the simulation on NS-2 platform, the experimental results highlight the feasibility and effectiveness of the proposed method from three aspects: the correctness of identification allocation, the effectiveness of addressing scheme, and the stability of data transmission.

关键词： Routing Resource management Clustering algorithms Internet Encoding Protocols Ad hoc networks

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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