报告题目： Good Practices and Lessons Learned in Reviewing the State of the Art and Reporting Scientific Research
报 告 人：Riccardo Spolaor，PhD，助理教授 ，山东大学
报告时间：2021年04月22日（周四）下午16：10—17：30
报告地点：12教703
报告内容简介：
This talk explores the challenges in properly structuring a scientific paper and conducting a thorough review of the state of the art. In the first part of this talk, we analyze the parts and their contents that compose a typical structure of a paper for computer science. In addition to this, we overview how the review and publishing process works for conferences and journals. In the second part, we discuss the steps to approach a novel research topic, the role of the student and supervisor, the selection of a topic of interest, and the methods to properly explore and organize the related state of the art. Finally, we analyze the purpose of survey papers, understand when a survey is needed, and meaningful knowledge and insights to include when writing a survey paper.
报告专家简介：
Riccardo Spolaor is an Assistant Professor at the Shandong University, R. P. China. He previously worked at as a Research Associate at the University of Oxford, United Kingdom in 2018 and 2019. He obtained his Ph.D. in Brain, Mind, and Computer Science at the University of Padua, Italy, in 2018. He has published more than a dozen papers in high- level international journals and conferences, such as IEEE COMST, TIFS, TMC, S&P, etc. These research works have been cited collectively more than 400 times in the past five years, according to Google Scholar. In addition, his work has also been presented at the international hacking conference BlackHat Europe 2018 Briefings.
His main research interests are privacy and security issues on edge computing, mobile, and IoT networks. In particular, he investigates the applications of machine learning techniques to infer sensitive information via side-channel analysis. Most of the research that he carried out up to now is about the security of mobile devices via the application of machine learning-based analyses to network traffic and energy consumption traces.
