Next Generation Mobile Video Networking

HWANG Jenq-Neng received his Ph.D. degree from the University of Southern California, USA. In the summer of 1989, Dr. HWANG joined the De- partment of Electrical Engineering of the Universi- ty of Washington in Seattle, USA, where he has been promoted to Full Professor since 1999. He served as the Associate Chair for Research fi＇om 2003 to 2005, and from 2011-2015. He is current- ly the Associate Chair for Global Affairs and Inter- national Development in the EE Depamnent. Hehas written more than 330 journal papers, conference papers and book chapters in the areas of machine learning, muhimedia signal processing, and muhimedia system integration and networking, including an au- thored textbook on ＂Multimedia Networking： from Theory to Practice,＂ published by Cambridge University Press. Dr. HWANG has close work- ing relationship with the industry on muhimedia signal processing and nmltimedia networking.

Computational approaches for fast generation of digital 3D video holograms(Invited Paper)

Several approaches for fast generation of digital holograms of a three-dimensional （3D） object have been discussed. Among them, the novel look-up table （N-LUT） method is analyzed to dramatically reduce the number of pre-calculated fringe patterns required for computation of digital holograms of a 3D object by employing a new concept of principal fringe patterns, so that problems of computational complexity and huge memory size of the conventional ray-tracing and look-up table methods have been considerably alleviated. Meanwhile, as the 3D video images have a lot of temporally or spatially redundant data in their inter- and intra-frames, computation time of the 3D video holograms could be also reduced just by removing these redundant data. Thus, a couple of computational methods for generation of 3D video holograms by combined use of the N-LUT method and data compression algorithms are also presented and discussed. Some experimental results finally reveal that by using this approach a great reduction of computation time of 3D video holograms could be achieved.

Pedagogical Alignment of Large Language Models (LLM) for Personalized Learning: A Survey, Trends and Challenges

This survey paper investigates how personalized learning offered by Large Language Models (LLMs) could transform educational experiences. We explore Knowledge Editing Techniques (KME), which guarantee that LLMs maintain current knowledge and are essential for providing accurate and up-to-date information. The datasets analyzed in this article are intended to evaluate LLM performance on educational tasks, such as error correction and question answering. We acknowledge the limitations of LLMs while highlighting their fundamental educational capabilities in writing, math, programming, and reasoning. We also explore two promising system architectures: a Mixture-of-Experts (MoE) framework and a unified LLM approach, for LLM-based education. The MoE approach makes use of specialized LLMs under the direction of a central controller for various subjects. We also discuss the use of LLMs for individualized feedback and their possibility in content creation, including the creation of videos, quizzes, and plans. In our final section, we discuss the difficulties and potential solutions for incorporating LLMs into educational systems, highlighting the importance of factual accuracy, reducing bias, and fostering critical thinking abilities. The purpose of this survey is to show the promise of LLMs as well as the issues that still need to be resolved in order to facilitate their responsible and successful integration into the educational ecosystem.