The current opportunities and challenges of Web 3.0

With recent advancements in AI and 5G technologies,as well as the nascent concepts of blockchain and metaverse,a new revolution of the Internet,known as Web 3.0,is emerging. Given its significant potential impact on the internet landscape and various professional sectors,Web 3.0 has captured considerable attention from both academic and industry circles. This article presents an exploratory analysis of the opportunities and challenges associated with Web 3.0. Firstly, the study evaluates the technical differences between Web 1.0, Web 2.0, and Web 3.0, while also delving into the unique technical architecture of Web 3.0. Secondly, by reviewing current literature, the article highlights the current state of development surrounding Web 3.0 from both economic and technological perspective. Thirdly, the study identifies numerous research and regulatory obstacles that presently confront Web 3.0 initiatives. Finally, the article concludes by providing a forward-looking perspective on the potential future growth and progress of Web 3.0 technology

Neural Network Predicting Remote Vehicle Movement with Vehicle-to-Vehicle Data

This paper presents a neural network developed for predicting the path of a remote vehicle using post facto created vehicle-to-vehicle (V2V) data and uses that prediction to determine whether it is safe for the host vehicle to change lanes. The data was collected in a 2013 experiment involving various drivers traveling on public roads in Ann Arbor, MI. The trips were on suburban roads, city roads and divided highways over a two-day period. The vehicular satellite global positioning system (GPS) data from movement over this period was gathered and post-processed to find vehicle paths within 10 meters of one another. The path traces of the two vehicles were combined to simulate what a V2V network would have provided to properly equipped vehicles if such a network and vehicles existed on real road networks demonstrating natural driving behavior. This research harnesses this data to determine the increased effectiveness of a neural network predicting the future path of remote vehicles and lane change safety when a V2V network is available. The most studied maneuver is overtaking. To a lesser extent, this paper also provides a view into how a neural network predicts remote vehicle behaviors using a host vehicle equipped with only perceptive hardware and no given information from the remote vehicle.Master of Science in EngineeringElectrical Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/146791/1/49698122_breg_thesis_embedded (1).pd

Application of Artificial Intelligence and Machine Learning in Libraries: A Systematic Review

As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objectives of the study, a systematic literature review was conducted based on the original guidelines proposed by Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA and LISTA databases. Following the rigorous/ established selection process, a total of thirty-two articles were finally selected, reviewed and analyzed to summarize on the application of AI and ML domain and techniques which are most often used in libraries. Findings show that the current state of the AI and ML research that is relevant with the LIS domain mainly focuses on theoretical works. However, some researchers also emphasized on implementation projects or case studies. This study will provide a panoramic view of AI and ML in libraries for researchers, practitioners and educators for furthering the more technology-oriented approaches, and anticipating future innovation pathways