Conférences  >  Informatique  >  Système embarqué & informatique ubiquitaire, RFIDs

Shree Venkateshwara Hi-Tech Engineering College

The rapid development of Information and Communication Technologies [ICT], which has the ability to completely change the way people communicate and interact with each other and the environment. In this scenario, recently ubiquitous computing is emerging as the new research paradigm that implements a pervasive usage of intelligent computing technologies in the emerging cyber-physical domains. The 4th International Conference on ubiquitous computing and intelligent information systems [ICUIS 2024 ] organized by Shree Venkateshwara Hi-Tech Engineering College, Tamil Nadu, India on 12-13, December 2024 aims to develop a new research forum for researchers, academicians and industrialist to share and exchange their potential insights on the design, implementation and analysis of ubiquitous computing models in intelligent information systems.

Tél.: [7418692998];     Email.: contact.icuis@gmail.com

Ubiquitous Computing and Communications, Intelligent Information Systems

WEKA FACHMEDIEN GMBH

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

A long-standing question in theoretical computer science is to offer analysis tools for improving performance on typical everyday, non-worst-case instances. A recent model addressing this goal is the algorithms-with-predictions model: each problem instance comes with a possibly error-prone prediction, and the worst-case running time is given as a function of the error in that prediction. This model reflects how recent advances in machine learning are able to make reasonably good predictions on practical ‒ even very complex ‒ datasets. The algorithms-with-predictions model has been used to give strong approximation-ratio guarantees for fundamental online algorithms like scheduling and caching. How to leverage predictions to speed up running time of offline algorithms and data structures has received less attention. This Dagstuhl Seminar aims to bring together researchers from the data structures, combinatorial optimization and learned predictions communities to address the challenges of adopting learned predictions for improving running time guarantees.