no code implementations • 11 Dec 2023 • Jingran Shen, Nikos Tziritas, Georgios Theodoropoulos
In this paper, a deep learning module inference latency prediction framework is proposed, which i) hosts a set of customizable input parameters to train multiple different RMs per DNN module (e. g., convolutional layer) with self-generated datasets, and ii) automatically selects a set of trained RMs leading to the highest possible overall prediction accuracy, while keeping the prediction time / space consumption as low as possible.
no code implementations • 7 Dec 2023 • Georgios Diamantopoulos, Nikos Tziritas, Rami Bahsoon, Georgios Theodoropoulos
Our Digital Twin leverages DDDAS feedback loop, which is responsible for getting the data from the system to the digital twin, conducting optimisation, and updating the physical system.
no code implementations • 11 Oct 2023 • Nan Zhang, Rami Bahsoon, Nikos Tziritas, Georgios Theodoropoulos
Engineering regulatory compliance in complex Cyber-Physical Systems (CPS), such as smart warehouse logistics, is challenging due to the open and dynamic nature of these systems, scales, and unpredictable modes of human-robot interactions that can be best learnt at runtime.
no code implementations • 19 Jul 2022 • Nan Zhang, Rami Bahsoon, Nikos Tziritas, Georgios Theodoropoulos
DT can leverage fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional symbiotic sensing feedback loops for its continuous updates.
1 code implementation • 11 Jul 2022 • Jingran Shen, Nikos Tziritas, Georgios Theodoropoulos
Ridesharing has received global popularity due to its convenience and cost efficiency for both drivers and passengers and its strong potential to contribute to the implementation of the UN Sustainable Development Goals.
no code implementations • 26 Apr 2022 • Georgios Diamantopoulos, Nikos Tziritas, Rami Bahsoon, Georgios Theodoropoulos
Given that Blockchains are complex, dynamic dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application.
1 code implementation • 15 Apr 2022 • Nan Zhang, Rami Bahsoon, Nikos Tziritas, Georgios Theodoropoulos
Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous.
no code implementations • 18 Jan 2021 • Masatoshi Hanai, Nikos Tziritas, Toyotaro Suzumura, Wentong Cai, Georgios Theodoropoulos
In the case of distributed graph processing, changing the number of the graph partitions while maintaining high partitioning quality imposes serious computational overheads as typically a time-consuming graph partitioning algorithm needs to execute each time repartitioning is required.
graph partitioning Distributed, Parallel, and Cluster Computing Databases Discrete Mathematics Data Structures and Algorithms Social and Information Networks