2019年06月17日  点击:[]


时 间:2019年6月20日—6月22日


会议主题:兹通知纽约州立大学石溪分校、中国联通网络技术研究院大数据中心、大连理工大学等单位,主要围绕智能网络安全现状进行讨论,讨论内容如下:1)多媒体数据安全问题;2)移动大数据安全问题; 3)内容中心网络下安全缓存问题。本次会议将邀请相关研究方向专家学者,报告最新研究成果,对共同感兴趣的问题深入交换看法,探寻未来可能的合作研究,同时给相关年轻学者和博士生一个了解当前智能网络安全现状及其研究成果的机会。相信此次研讨会的召开将促进无线通信环境下智能网络安全解决方案研究经验的交流,进一步推动这一领域研究进展。



9:00 - 9:50

开幕式 吴国伟(综合楼5楼第一会议室)

10:00 - 11:20

Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation


13:30 - 14:15


陈振宇 (教学楼A401)

14:20 - 15:10


曾宇杰 (教学楼A401)

15:15 - 16:00


江滨耀 (教学楼A401)

16:00 - 17:00

Privacy-PreservingRetrievals inCloudEnvironment

郭成 (教学楼A401)


9:00 - 10:30

运行商大数据体系与应用 程新洲 韩玉辉

10:30 - 11:30

内容中心网络下安全机制概述 姚琳

13:30 - 14:30

边缘网络中的大数据处理 夏秋粉

14:35 - 15:35

未来智能云网络 徐子川

15:40 - 16:40

内容中心网络下安全协作缓存 李兆洋


9:00 - 10:30

网络空间安全研究所成果展示 (综合楼)

10:30 - 11:30

参会专家、教师、学生座谈 (综合楼5楼第一会议室)


Xin Wang,Dr. Wang is currently the director of the Wireless Networking and Systems lab of the department of Electrical and Computer Engineering of the State University of New York (SUNY) at Stony Brook. She was a Member of Technical Staff in the area of mobile and wireless networking at Bell Labs Research, Lucent Technologies, New Jersey between 2001 and 2003. Dr. Wang has been conducting and leading research work in the design of network architectures, protocols and algorithms. The work of her group falls into a few directions, including advanced wireless network architecture, mobile cloud computing and distributed computing, and big data analysis and deep learning. Dr. Wang obtained her PhD from Columbia University, BS and MS from Beijing University of Post and Telecommunications, respectively. She is a recipient of NSF career award in 2005 and theONRChief of Naval Research (CNR) Challenge award in 2011.

She currently serves as an associate editor of IEEE Transactions of Mobile Computing (TMC). She also serves TPC chair or program committee members in many technical conferences, including ACM MobiCom, IEEE Infocom, IEEE ICDCS, and IEEE PerCom. Her research group has published more than 100 papers in highly reputed conferences and journals, including ACM Sigmetrics, ACM MobiCom, USENIX NSDI, IEEE ICNP, IEEE Infocom, IEEE ICDCS, IEEE Percom, IEEE TON, IEEE TMC, IEEE JSAC, IEEE TC, and IEEE TDSC.

报告题目:Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation

State-of-the-art techniques in Generative Adversarial Networks(GANs) have shown remarkable success in image-to-image translationfrom peer domain X to domain Y using paired image data.However, obtaining abundant paired data is a non-trivial and expensiveprocess in the majority of applications. When there is aneed to translate images across n domains, if the training is performedbetween every two domains, the complexity of the trainingwill increase quadratically. Moreover, training with data from twodomains only at a time cannot benefit from data of other domains,which prevents the extraction of more useful features and hindersthe progress of this research area. In this work, we propose a generalframework for unsupervised image-to-image translation acrossmultiple domains, which can translate images from domain X toany a domain without requiring direct training between the twodomains involved in image translation. A byproduct of the frameworkis the reduction of computing time and computing resources,since it needs less time than training the domains in pairs as is donein state-of-the-art work. Our proposed framework consists of apair of encoders along with a pair of GANs which learns high-levelfeatures across different domains to generate diverse and realisticsamples from. Our framework shows competing results on manyimage-to-image tasks compared with state-of-the-art techniques.

程新洲,毕业于北京邮电大学,教授级高级工程师,北京邮电大学兼职教授,研究生导师,IEEE ISCIT、IUCC等国际会议技术委员会主席,教育部学位论文通讯评议专家,中国联通网络技术研究院大数据研发中心主任,中国联通集团专家级战略人才。主要从事大数据技术研究及应用等工作,近年来主持和参与多项国家及集团级重点项目,项目技术成果先后获国资委中央企业创新创意大赛优秀奖、工信部/中国信通院大数据星河奖、中国信息协会年度安全可控优秀解决方案奖,以及省部级优秀工程设计、优秀咨询、创新奖、科学技术奖10余项。在国内外期刊会议发表论文80余篇,专利30余项,出版专著2部。




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