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西南大学青年学者含弘科技论坛

发布时间:2017-05-03 来源:本站原创 作者:本站编辑   浏览次数:

西南大学青年学者含弘科技论坛

计算机与信息科学学院分论坛学术报告

时间:201754日(星期四)下午3:10-55日(星期五)上午8:30-

地点:西南大学计算机与信息科学学院0114学术报告厅

报告一:

题目:Data-driven Cybersecurity: Methodology and Applications

报告人:张军,博士后,澳大利亚迪肯大学

报告人简介:

张军博士正领导一个博士研究生为主体的20人团队,主要从事空间安全、大数据分析及隐私保护等的研究与开发。张军获全额博士奖学金在澳大利亚卧龙岗大学(澳洲计算机专业排名第一)学习3年并获得博士学位。由于优异的学术成果,张军于2009年获得中国优秀海外留学生奖(全球一共300名)。他如今是迪肯空间安全研究中心的教务长。在顶级国际期刊和会议上已经发表学术论文70余篇,比如IEEE Transactions on Parallel and Distributed Systems (TPDS)IEEE Transactions on Information Forensics and Security (TIFS)IEEE Transactions on Dependable and Secure ComputingTDSC)。

内容摘要:

Cybersecurity has been becoming an important and attractive area for research and industry. This talk will firstly introduce a new research methodology, data-driven cybersecurity. Then, two data-driven cybersecurity applications, Internet traffic identification and Twitter spam detection, will be presented. In the application of Internet traffic identification, we will talk about a recent research progress, correlation based statistical traffic classification. In the application of twitter spam detection, we will introduce a recent work, real-time detection of drifted twitter spam.

 

报告二:

题目:Big Data - Big Application

报告人:陈金俊,教授,澳大利亚斯文本科技大学

报告人简介:

Dr Jinjun Chen is a Professor from Swinburne University of Technology, Australia. He is the Deputy Director of Swinburne Data Science Research Institute, and the Director of Swinburne Big Data Lab. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include scalability, big data, data science, cloud computing, data intensive systems, data privacy and security, and related various research topics. His research results have been published in more than 130 papers in international journals and conferences, including various IEEE/ACM Transactions.

 

He received UTS Vice-Chancellor's Awards for Research Excellence Highly Commended (2014), UTS Vice-Chancellor's Awards for Research Excellence Finalist (2013), Swinburne Vice-Chancellor’s Research Award (ECR) (2008), IEEE Computer Society Outstanding Leadership Award (2008-2009) and (2010-2011), IEEE Computer Society Service Award (2007), Swinburne Faculty of ICT Research Thesis Excellence Award (2007). He is an Associate Editor for ACM Computing Surveys, IEEE Transactions on Big Data, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cloud Computing, as well as other journals such as Journal of Computer and System Sciences, JNCA. He is the Chair of IEEE Computer Society’s Technical Committee on Scalable Computing (TCSC).

内容摘要:

Right now, Big Data, Data Science or Data Analytics are being on wide interest in industry and academia. During this talk, we will discuss two questions based on my research industry engagement practice.

 

The first one is business gain from such buzz words. This is a practical question from business. Based on my research, big data means big niche market opportunity for retail industry which can grow up to enhance major markets. For example, by analysing and generalising potential weak connection between previously sparse data sources such as flight booking data and supermarket user data, we can better expand or enhance the market for personal recommendation on flight booking.

  

The second is how researchers make a full potential to business. A say is "It's not who has the best algorithm that wins. It's who has the most data" by Andrew Ng (Coursea founder). Data is becoming an important resource equally important to oil. While various public datasets are available to academics or researchers for research evaluation, those datasets may not be suitable or useful and timely for researchers. One way to make full potential of big data is to intensively work with industry because they have timely data. More or less, every industry is doing data analysis yet just on their specific purposes. We will brief our research and collaboration with specific industries.

 

报告

题目:智慧城市背景下的局域物联网设计与实践

报告人:刘巍博士,日本国家信息通信技术研究院。

报告人简介:刘巍,于20062009年获重庆大学软件工程专业学士,硕士学位;2009-2011年,加入中国人民财产保险股份有限公司从事金融系统开发和数据分析工作;2012-2015年,受日本文部科学省奖学金资助就读于日本京都大学,2015年获京都大学通信与计算机工程专业博士学位;2015年至今,任职于日本国家信息通信技术研究院,主要从事智能物联网领域的研究,开发,和技术咨询工作。过去的5年中,他参与的物联网项目涉及多个不同的行业领域,如智慧养老,智能家居,基于移动互联网的商品推荐,和车联网等。这其中的大部分项目均与日本相关领域的著名企业合作实施,部分项目的研究和开发成果已经成功实现了商业化运营。在学术研究方面,他以第一作者的身份在国际知名的期刊杂志和会议上发表了超过10篇的论文。他在电气和电子工程师协会(IEEE)主办的IEEE CCWC 2017会议上获得最佳论文奖,并在国际电信联盟(ITU)主办的Kaleidoscope 2013会议上获得了最佳青年研究学者奖。

内容摘要:随着人们对生活品质要求的提高和近年物联网,人工智能等技术的发展,智慧城市已经成为当前信息通信行业中最受关注的研究和商业领域之一。本报告简述目前日本在智慧城市建设方面的最新进展,并以一个搭建在自动饮料售货机和出租车等社会公共设施上的实际无线物联网平台作为例子,介绍智慧城市背景下局域物联网的概念,整体架构和其在不同层次上(通信层,数据链路层,网络层,传输层,和应用层)的技术标准,研究进展,以及对应的商业应用场景。

 

报告

题目:I Know Your Secrets! An Emerging Charging Attack on Smartphones.

报告人:蒙威志,博士,丹麦科技大学

报告人简介:Dr. Weizhi Meng is currently an assistant professor in the Department of Applied Mathematics and Computer Science, Technical University of Denmark(DTU), Kongens Lyngby, Denmark. He received his B.Eng. degree in Computer Science from the Nanjing University of Posts and Telecommunications, China and obtained his Ph.D. degree in Computer Science from the City University of Hong Kong (CityU), Hong Kong in 2013. Prior to joining DTU, he worked as a research scientist in Infocomm Security (ICS) Department, Institute for Infocomm Research, Singapore, and as a senior research associate in CityU after graduation.

His primary research interests are cyber security and intelligent technology in security including intrusion detection, mobile security and authentication, HCI security, cloud security, trust computation, web security, malware and vulnerability analysis. He also shows a strong interest in applied cryptography. He won the Outstanding Academic Performance Award during his doctoral study, and is a recipient of the Hong Kong Institution of Engineers (HKIE) Outstanding Paper Award for Young Engineers/Researchers in 2014 and the Best Student Paper Award from the 10th International Conference on Network and System Security (NSS) in 2016.

内容摘要:

Charging threats are often ignored by the literature. In this talk, I introduce a new type of charging attack (called juice filming charging attack) based on a standard USB connector and HDMI, which can steal users' secrets through automatically video-capturing their inputs (e.g., unlock patterns, PIN code). The effectiveness of such attack relies on the observations that users are not aware of any risk when charging their phones in public places and that most users would interact with their phones during the charging period. As compared to malware and other type of attacks, the designed juice filming charing attacks can offer six major features: 1) be simple but quite efficient; 2) user unawareness; 3) does not need to install any apps on phone's side; 4) does not need to ask for any permissions; 5) cannot be detected by any current anti-malware software; 6) can be scalable and effective in both Android OS and iOS. Further, by utilizing an ORC technology, this attack can automatically analyze the obtained data. To implement this attack, a VGA/RGB frame grabber is employed and several user studies are performed to explore its feasibility and effectiveness. Several feasible countermeasures will be discussed in the end.

 

 

报告五:

题目:Structures and Dynamics of Complex Networks

报告人:高建喜,博士,美国东北大学

报告人简介:Dr. Jianxi Gao will be an assistant professor in Computer Science Department at Rensselaer Polytechnic Institute from this August. He is currently a research assistant professor in the Center for complex network research at Northeastern University. Dr. Gao received his Ph. D. degree at Shanghai Jiao Tong University from 2008 to 2012. During his Ph.D. he was a visiting scholar at Prof. H. Eugene Stanley’s lab at Boston University from 2009 to 2012. Dr. Gao’s major contribution includes the theory for robustness of networks of networks and resilience of complex networks. Since 2010, Dr. Gao has published over 20 journal papers in Nature, Nature Physics, Nature Communications, Proceedings of the National Academy of Sciences, Physical Review Letters and more, with over 18 hundreds citations on Google Scholar. Dr. Gao has been selected as the Editor board of Nature Scientific Reports, distinguished referee of EPL (2014-2016) and Elsevier (2016), and referee of Science, PNAS, PRL, PRX and more. His publications were reported over 70 times by international public and professional media.

内容摘要:Complex Networks exist in almost every aspect of science and technology. My research question focuses on how to understand, predict, control, and ultimately survive real-world complex systems in an ever-changing world facing the global challenges of climate change, weather extremes, and other natural and human induced disasters. I will present three recently works in the field of network science and complex systems: resilience, robustness, and control. (I) Resilience, a systems ability to adjust its activity to retain its basic functionality when errors and environmental changes occur, is a defining property of many complex systems. I will show a set of analytical tools with which to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive an effective one-dimensional dynamics that accurately predicts the systems resilience. The analytical results unveil the network characteristics that can enhance or diminish resilience, offering ways to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems resilient to both internal failures and environmental changes. (II) Increasing evidence shows that real-world systems interact with one another, and the real goal in network science shouldn't just understand individual networks, but deciphering the dynamical interactions in networks of networks (NONs). Malfunction of a few nodes in one network layer can cause cascading failures and catastrophic collapse of the entire system. I will show the general theoretical framework for analyzing the robustness of and cascading failures in NONs. The results of NONs have been surprisingly rich, and they differ from those of single networks that they present a new paradigm. (III) Controlling complex networks is the ultimate goal of understanding the dynamics of them. I will present a k-walk theory and greedy algorithm for target control of complex networks.

 

 

报告

题目:A quantitative framework for revealing disciplinary organizations of science

报告人:柯庆,印第安纳大学,博士

报告人简介:Qing Ke is a PhD Candidate at Indiana University, Bloomington, majored in Complex Systems. His research interest is computational social science, economics of science, and statistical learning. His research has been published in Physical Review E, PNAS, and PLoS ONE, and has received global media coverage, such as The New York Times, The Australian, El País, Le Monde, Scientific American, and Nature News.

内容摘要:Quantitative measurement is the cornerstone of scientific advancement. Here we present a framework for facilitating quantitative inquiries about scientific disciplines. We adapt a popular word embedding technique to the data of scholarly citation trails among 53 million scientific papers to learn continuous vector-space representation of scientific venues. We obtain a high dimensional map of science from the learnt embedding. Our map reveals the disciplinary organizations of science as exemplified in the direction and the spectrum of science by allowing arithmetic operations between venue vectors. Vector representations of scientific venues also facilitate downstream applications such as recommending similar venues and predicting discipline categories. We also introduce an effective measure that quantifies the interdisciplinarity of individual scientific paper. Based on our measure, we discover that interdisciplinary research indeed helps to attract attention, as manifested in the citations they receive. This is a joint work with Hao Peng and Yong-Yeol Ahn.