Machine Learning-Enabled Wireless Spectrum Data Analytics
Dr. Guoru Ding
IEEE Senior Member
National Mobile Communications Research Laboratory
Southeast University, Nanjing
Wireless spectrum is well recognized as the Black Gold in the 21th century. The requirement of obtaining wide-band wide-area wireless spectrum state in an accurate, timely, comprehensive, and cost-efficient manner poses critical technical challenges and drives the recent advances in wireless spectrum data analytics. This speech will present an overview of our recent work on machine learning-enabled wireless spectrum data analytics, which will mainly consist of three parts. The first part focuses on robust cooperative spectrum sensing, which is a reactive manner to obtain the wireless spectrum state via various signal detection and data fusion methods. Kernel-based learning will be introduced as an effective tool to tackle with Byzantine attacks. The second one will be online multi-dimensional spectrum prediction, which is a proactive manner to obtain the wireless spectrum state via kinds of statistical inference techniques. The low-rank and sparsity in spectrum matrix or spectrum tensor will be expoited to faciliate the active prediction. Deep learning will also be taliored to show its superority in spectrum prediction. The third part will depict several potential applications, including dynamic spectrum sharing in future wireless networks, intelligent spectrum management in UAV or Drones-enabled wireless communications, big spectrum data analytics-supported cogntive internet of things, to name a few.
Guoru Ding received the B.S. degree (Hons.) in electrical engineering from Xidian University, Xi'an, China, in 2008, and the Ph.D. degree (Hons.) in communications and information systems from the College of Communications Engineering, Nanjing, China, in 2014. Since 2014, he has been an Assistant Professor with the College of Communications Engineering and a Research Fellow with the National High Frequency Communications Research Center, China. Since 2015, he has been a Post-Doctoral Research Associate with the National Mobile Communications Research Laboratory, Southeast University, Nanjing. His research interests include cognitive radio networks, massive MIMO, machine learning, and big data analytics over wireless networks. Dr. Ding has published more than 80 peer-reviewed international journal and conference papers. Dr. Ding has acted as a Technical Program Committee Member of a number of international conferences, including the IEEE Global Communications Conference, the IEEE International Conference on Communications, and the IEEE Vehicular Technology Conference (VTC). He is currently a Voting Member of the IEEE 1900.6 Standard Association Working Group. He was a recipient of the Outstanding Ph.D. Thesis Awards from China Institute of Communications, Jiangsu Proviance, and Chinese People's Liberation Army and the Best Paper Awards from the EAI MLICOM 2016, the IEEE VTC 2014, and the IEEE WCSP 2009. He serves as a Guest Editor of the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (Special Issue on Spectrum Sharing and Aggregation for Future Wireless Networks) and an Associate Editor of the KSII Transactions on Internet and Information Systems.
Natural Language Processing: The Jewel on Artificial Intelligence Crown
Dr. Wanxiang Che
Research Center for Social Computing and Information Retrieval School of Computer Science and Technology Harbin Institute of Technology
The human intelligence can be divided into three stages: computation, perception and cognitive intelligence. Artificial intelligence is a field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”. For computation intelligence, humans have been completely defeated by machines. In recent years, with the help of deep learning and big data, machines are chipping away at human's lead in perception intelligence. However, the cognitive intelligence has always been thought of a unique ability of humans. And, the natural language or human language is the most important part of perception intelligence. So, the natural language processing, which employs computational techniques for the purpose of learning, understanding, and producing human language content, is the highest level of artificial intelligence. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, question and answering. Today’s researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services. We will describe successes and challenges in this rapidly advancing area.
Dr. Wanxiang Che, associate professor of school of computer science and technology at Harbin Institute of Technology (HIT) and visiting associate professor of Stanford University (at NLP group in 2012). His main research area lies in Natural Language Processing (NLP). He currently leads a national natural science foundation of China, a national 973 and a number of research projects. He has published more than 40 papers in high level journals and conferences, and published two textbooks. He and his team have achieved good results in a number of international technical evaluations, such as the first place of CoNLL 2009 and the fourth place of CoNLL 2017. He was an area co-chair of ACL 2016, publication co-chairs of ACL 2015 and EMNLP 2011. The Language Technology Platform (LTP), an open source Chinese NLP system he lead to develope, has been authorized to more than 600 institutes and individuals including Baidu, Tencent and so on. He achieved the outstanding paper award honorable mention of AAAI 2013, the first prize of technological progress award in Heilongjiang province in 2016, Google focused research award in 2015 and 2016, the first prize of Hanwang youth innovation award and first prize of the Qian Weichan Chinese information processing science and technology award in 2010.