Principal Scientist, Qatar Computing Research Institute, Qatar
Professor, College of Science and Engineering, Hamad bin Khalifa University, Qatar
Web search, web searching, search engines, information retrieval, information searching, information searching and retrieval, ecommerce, sponsored search, online marketing, online advertising, online branding, Google AdWords, PPC, Pay-per-click, keyword advertising, second screens, cross screens, dual screens, customer segmentation, audience segmentation, user segment, persona, personas, persona profiles, information science, library, web analytics, digital analytics .
Science wants numbers about people: The numbers are likely wrong
Science (as well as the industry, government, non-profits, and university sectors) want, use, and depend on numbers for situational awareness and then make decisions based on them. However, what if the numbers are wrong? Unfortunately, there is ample evidence that the numbers are often wrong for numbers concerning people and behaviors. As a case study, we ‘peel back the onion’ on the numbers from a large-scale web traffic study of 86 worldwide websites comparing two web analytics tools. One tool is a site-centric service, and one uses a triangulation of user, site, and network-centric methods. The resulting numbers for the three standard analytics metrics evaluated are statistically different. However, which set of numbers is ‘more’ right? A deductive analysis shows that both approaches are inaccurate and imprecise, even though advertising and other decisions worth hundreds of billions of US dollars annually are made using these numbers. The talk ends with recommendations to move forward for the reporting of numbers in this and related contexts.
Associate Professor, School of Electrical Engineering and Computer Science, University of Ottawa, Canada
AI-enabled wireless networks, 5G and 6G wireless communications, smart grid, electric vehicles, Internet of things and wireless sensor networks, O-RAN
AI-Enabled Wireless Networks: A Bridge from 5G to 6G
Future wireless networks are expected to support a multitude of services demanded by Enhanced Mobile Broadband (eMBB), Ultra-Reliable and Low-latency Communications (uRLLC), and massive Machine Type Communications (mMTC) users. Heterogeneous devices with different quality of service (QoS) demands will require intelligent and flexible allocation of network resources in response to network dynamics. For instance, a highly reliable and low-latency network is needed to enable rapid transfer of messages between connected autonomous vehicles. At the same time, the same physical infrastructure is expected to serve users with high-quality video demand or even mobile Augmented/Virtual Reality entertainment applications. Next-generation wireless networks are expected to accommodate such diverse use cases. In addition, resource efficiency, reliability, and robustness are becoming more stringent for 5G and beyond networks. To meet this, future wireless networks must incorporate a paradigm shift in network resource optimization, in which efficient and intelligent resource management techniques are employed.Artificial intelligence, or more specifically machine learning algorithms stand as promising tools to intelligently manage the networks such that network efficiency, reliability, robustness goals are achieved and quality of service demands are satisfied. The opportunities that arise from learning the environment parameters under varying behavior of the wireless channel, positions AI-enabled 5G and 6G, superior to preceding generations of wireless networks. In this keynote, we will provide an overview of the state-of-art in machine learning algorithms and their applications to wireless networks, in addition to their challenges and the open issues in terms of their applicability to various functions of future wireless networks.
Professor, College of Computer and Information Science, Northeastern University, Boston, USA
Pattern Recognition, Machine Learning, Computer Vision, Signal Processing, Computational Intelligence, Artificial Intelligence, Text Mining, Data Mining, Intelligent Computing, Genetic Algorithms
Intelligent Pattern Recognition and Applications to Imaging and e-Forensics
This talk is concerned with fundamental aspects of Intelligent Pattern Recognition (IPR) and applications. It basically includes the following: Basic Concept of Automata, Grammars, Trees, Graphs and Languages. Ambiguity and its Importance, Brief Overview of Artificial Intelligence (AI), Brief Overview of Pattern Recognition (PR), What is Intelligent Pattern Recognition (IPR)? Interactive Pattern Recognition Concept, Importance of Measurement and Ambiguity, How it works, Modeling and Simulation, Basic Principles and Applications to Computer Vision, Security, e-Forensics, Road Sign Design, biomedical diagnosis, Safer biomedical diagnosis, Traffic and Robot Driving with Vision, Ambiguous (design of Road Signs vs Unambiguous (Good) Road Signs, How to Disambiguate an Ambiguous Road Sign? What is Big Data? and more Examples and Applications of Learning and Greener World using Computer Vision. Finally, some future research directions are discussed.