2,331 to 2,340 of 2,454 Results
Jupyter Notebook - 14.1 KB -
MD5: ae52057f8c26b99ebe6441530afee887
The main entry point to run new experiments. |
Jupyter Notebook - 41.7 KB -
MD5: 1e4cd09824803ddcc107348c88583166
The main entry point to plot generated data. Requires the data as CSV file. |
Markdown Text - 2.7 KB -
MD5: 14c26b62957d6d0d54a3bb8c129ef11a
Readme file with additional information on the data and code. |
Plain Text - 2.2 KB -
MD5: f74ff9705432d88bf78f186c1d8a26ed
Python requirements for reproduction. |
Python Source Code - 4.8 KB -
MD5: 894906f22dbd93ef4e3308dae176b06d
Functions to perform quantum teleportation. |
Python Source Code - 2.3 KB -
MD5: 036a4b11c59ddedcf7cd58d159b52f72
Utility functions for the experiments. |
Nov 27, 2023 - Architectures and Middleware @IAAS
Pesl, Robin D.; Stötzner, Miles; Georgievski, Ilche; Aiello, Marco, 2023, "Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities", https://doi.org/10.18419/DARUS-3767, DaRUS, V1, UNF:6:GV+GzTPs7xXW9ITeS6uC7Q== [fileUNF]
Experimental results for the ICSOC 2023 AI-PA position paper "Uncovering LLMs for Service-Composition: Challenges and Opportunities." Exemplars: List of scenarios found in the Google Scholar literature search. Experiment 1 Service Discovery: Chat history for experiment 1 asking ChatGPT for existing real services. Experiment 2 Service Composition: C... |
Nov 27, 2023 -
Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities
PNG Image - 6.5 MB -
MD5: 8b133aab3938b353cdcf0f0f77d8f319
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Nov 27, 2023 -
Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities
PNG Image - 5.9 MB -
MD5: f0f01ecd67f160810c5a0cb60d0368e2
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Nov 27, 2023 -
Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities
PNG Image - 3.4 MB -
MD5: 09d212cfd434a4d4f09bae18057a7aec
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